Can Crypto Really Beat Inflation?

Crypto has been vaunted variously as an inflation-proof asset and a digital answer to gold. But just how accurate are these descriptions? With inflation on the rise, the answer could well be around the corner.

The Problem of Inflation

Everyone with fiat savings fears the dreaded “i” word. But after years of low or almost no inflation in many parts of the world, it suddenly seems we cannot escape talk of a coming inflationary storm.

Much of the economic talk in 2021 centered on inflationary hotspots in Turkey, Argentina, Venezuela and the like.

Source: https://dolartoday.com/indicadores/; http://www.bcv.org.ve/estadisticas/tipo-de-cambio
Graphic: Nicolas Perrault III

But while these were once seen as outliers, nations that have been living without inflation for decades are now posting worrying figures. In countries like the UK and the United States, central banks are now finding themselves under increasing pressure to raise interest rates to fight back.

However, the problem cannot be so easily swept under the carpet. Wages in the West are on the rise, food prices are shooting up worldwide and energy price hikes are becoming commonplace.

If inflation is now a given, it is only logical to expect fiat currency holders to respond. Pressures like these naturally push investors toward “safe assets” – traditionally blue-chip stocks and gold. But more recently, the “safe asset” category has a new member: crypto.

Does Crypto Work as a Store of Value?

Many major economists say they think so, with some calling Bitcoin and the like “digital gold.”

One notable example is the Visa CEO Alfred Kelly, who last year said: “We see all [cryptoassets] as digital gold. They are predominantly held as assets that are not used as a form of payment in a significant way at this point.”

Just as gold or “safe-bet” stocks often experience price volatility, they are simply too valuable to bottom out. They are also a safe distance from currency markets, meaning that they might get dragged into periods of fiat-related volatility, but can never (or so the theory goes) experience the same kind of hyperinflationary pressures that can cause a currency to collapse, à la Germany in the 1920s.

While the Turkish Lira and the Argentine Peso are not quite at the same level, they too are edging ever closer to inescapable currency chaos.

In both nations, crypto adoption is flying up. Turks make a million crypto transactions a day, Reuters reported last month. In Argentina, even the President has called crypto a “hard currency, somewhat,” with the power to “nullify inflation.”

Bitcoin prices over the past five years

Is There Really Any Truth to All This?

This month, Rio de Janeiro’s Mayor said that he intends for the Brazilian city to keep 1% of its treasury reserves in crypto. Other cities have taken a similar tack, while there is now no shortage of mainstream financial advisors speaking to media outlets like CNBC and Time about the benefits of buying crypto. Most now advise investors to keep at least a small portion of crypto (10% or less, mostly) in their portfolios.

It looks like global politicians are starting to take this advice to heart.

Could Crypto Actually Replace Fiat?

Most critics think that crypto’s weak point is its use as a form of payment. Visa’s Kelly is just one of those who have pointed out that people seem happy to buy, trade and hold crypto, but seem unwilling to spend their coins on goods or services.

High gas fees, slow and transaction prices are often cited as prohibitive factors, while crypto pay incentives have thus far failed to blossom. But micro-payment-friendly solutions have been mooted, including the Bitcoin Lightning Network, which has been championed by the Bitcoin-keen government of El Salvador’s President Nayib Bukele.

Bukele’s government last year adopted BTC as legal tender, and has since snapped up hundreds of tokens using public funds. That means that crypto is now being put to the test in the Central American nation as not only as a treasury reserve asset (a store of value), but also as a means of payment.

As these are perhaps the two key properties an asset needs to possess if aspires to be called a currency, perhaps we will find out very soon if crypto really has what it takes to go toe-to-toe with fiat!

What Are Staking Tokens? How It Differ From Other Tokens?

Investors may think staking as less profitable option to mining. Although its the other way around.  A cryptocurrency wallet is used to keep money safe and secure for a blockchain network. Staking is just locking up cryptocurrency in order to reap the benefits.

Proof of Stake (PoS) is an important concept to learn before diving into the world of staking. It is possible to run a blockchain more efficiently while retaining a reasonable degree of decentralisation by using PoS, a consensus method. Let’s take a look at what Proof of Stake (PoS) is and how it works.

What is Staking?

Staking cryptocurrency implies committing crypto assets to a blockchain network to facilitate and validate transactions. Proof-of-stake (POS) allows cryptocurrency owners to verify block transactions based on staked currencies. As an alternative to Proof-of-work (POW), which is used to verify blockchains and add new blocks, POS was made. POS is considered less dangerous since it arranges payments in a manner that makes an attack less effective.

At the end of the day, staking is a way to earn rewards for holding cryptocurrencies.

How Staking Tokens Differ From Other Tokens?

Trust Wallet is an example of a crypto wallet that allows one to stake their coins straight from their account. On the other side, staking is available on several exchanges.  All one has to do is keep their coins in the exchange’s custody.

Play-to-win gaming platforms like Decentraland, Sandbox, and Axie Infinity are where most of the NFT staking opportunities are found, among others. All one needs to stake is a cryptocurrency wallet that has NFTs in it, and that’s all. Although, it should be noted that not all NFTs can be staked.

Staking in NFTs is a new approach to generate cryptocurrency passively. In order to get incentives, NFT holders may store their assets on DeFi platforms. They can all keep their NFT collections without having to sell them. Investors may profit from less overall supply by using NFT staking. For the most part, however, NFT stakes open the door to new applications for NFTs outside of digital art collection, but not all NFT’s can be staked unlike tokens.

The 4 Best Crypto Staking Tokens of 2022 are as follows:

Terra (LUNA)

Terra (LUNA) hit a new record of $20.05 billion in total value locked (TVL) across its 13 product lines, according to industry figures. Terra’s TVL was $11.9 billion on Dec. 1, up 68% in less than a month.

Luna is presently trading around $90, a gain of almost 12,000% from its price of $0.7 in January 2021. The coin is now valued at $34.8 billion on the market. LUNA has an annual staking payout of roughly 12.10 percent, making it one of the finest cryptos to stake.

PancakeSwap (CAKE)

PancakeSwap (CAKE) is a popular and enjoyable staking platform that allows users to stake any CAKE tokens they earn. When users stake CAKE coins, they have the option of earning extra CAKE or other currencies. Transaction costs on Binance Smart Chain are much cheaper than compared to Ethereum.

The owner may either collect their rewards or reinvest them into PancakeSwap after earning them. The CAKE coin’s yearly returns vary from 31 to 42 percent, making it one of the greatest crypto staking currencies available.

Shiba Inu (SHIB)

Shiba Inu (SHIB), also known as Shiba Token, has been more popular in recent years. With a 9.4 billion dollar market cap, it is now the 9th biggest cryptocurrency. Many investors regard SHIB as an asset to acquire and retain in their cryptocurrency portfolio. With the ShibaSwap exchange launch, SHIB holders may now stake and farm their tokens.

While Shiba Inu operates on Ethereum (now PoW), the initial quantity of SHIB was minted upon launch; therefore, it cannot be mined. SHIB holders may stake their Shiba coins on the ShibaSwap exchange for BONE tokens and 0.03 percent of the ETH swap transaction costs.

Solana (SOL)

SOL is a great staking currency due to its cheap transaction fees and fast transfers. On the Solana network, users may stake their coins with over 640 validators, but one cannot operate their own node.

It’s possible for the owner to share in the rewards that validators get on Solana if the owner gives them the stake. When the owner stakes the SOL coins, they may expect to obtain yearly returns ranging from 7–11 percent. SOL coins have soared in value in recent months, hitting an all-time high of $210.

Conclusion

Proof of Stake and staking opened the crypto market to more people who weren’t able to mine or trade cryptocurrency. Crypto staking is open to anyone wishing to contribute to blockchain consensus and governance. As the entry barriers to the blockchain ecosystem drop, staking becomes more comfortable, simpler, and more economical. With cryptocurrencies paying high interest rates, staking could be a brilliant method to earn passive income.

Support and Resistance – Pivot Analysis

What is Support and Resistance?

The purpose of support and resistance levels is to identify favorable entry and exit points.

There are multiple trading strategies that incorporate support and resistance levels. Additionally, there are multiple support and resistance strategies, the most common being the use of pivot levels and their associated major support and resistance levels that are based on a time period’s pivot level.

When trading, it is beneficial to use more common strategies as these will tend to be followed by a greater number of traders.

Support

Support levels refer to price levels below which an asset does not drop for an extended length of time.

At support levels, buyers enter into long positions thus delivering support and preventing further downside.

It is important to note, however, that there will be multiple support strategies. These include the use of the most recent lows as an example and Fibonacci’s. Pivots and major support levels are the most commonly used levels.

Once a support level has been breached, the support level becomes a resistance level.

Resistance

Similarly, resistance levels are price levels at which sellers will look to exit an asset or enter into a short position.

Here, resistance levels are calculated for time intervals by using the highs and lows of the previous time interval. In the case of using major resistance levels, traders base their resistance levels on the pivot level for a specified time interval, t.

Other resistance levels commonly used include daily, weekly, monthly, yearly, and all-time highs and Fibonacci’s.

Once a resistance level has been broken, the resistance level becomes a support level.

How to draw support and resistance

Analysts and traders calculate the pivot and the major support and resistance levels for multiple time periods. These can be as short as hourly and as long as monthly.

Once you have calculated the pivot and major support and resistance levels, traders and analysts will then plot these on charts to assist in their trading decisions as shown in the chart below.

Calculating Pivot Levels

A pivot level is derived by calculating the average of the high, the low, and the closing price of a time interval, t.

Looking at a 1-hour time interval for the chart below, we would take the average of the day high $55,329, the day low $53,711, and the closing price $54,791 to obtain the next day’s pivot level. Here the pivot level would be $54,610.

Chart 1 FX Empire Chart

Calculating Support Levels

Once you have calculated the pivot level, the major support levels, these being S1, S2, and S3 can be calculated. In the example below, using an hourly chart, a day’s pivot and major support levels can be calculated.

First Major Support Level: 2 x Pivot / the previous time interval high. In the example above, this would be (2 x $54,610) / 55,329 = $53,892.

Traders would be looking at the first major support level as an entry price.

Second Major Support level: S2 = Pivot – (Day high – Day low).

In the example above, this would be $54,610 – ($55,329 – $53,711) = $52,992.

Traders would be looking at the second major support level as an entry price in the event of an extended reversal.

Third Major Support level: S3 = S2 – (Day high – Day low).

In the example above, this would be $52,992 – ($55,329 – $53,711) = $51,374.

Traders would be looking at the third major support level as an entry price in the event of a market sell-off.

Support FX Empr

Calculating Resistance Levels

Once you have calculated the pivot level, the major resistance levels, these being R1, R2, and R3, can also be calculated.

First Major Resistance Level: R1: = 2 x Pivot / the previous time interval low. In the example above, this would be (2 x $54,610) / 53,711 = $55,510.

Traders would be looking at the first major resistance level as an exit price.

Second Major Resistance level: R2 = Pivot + (Day high – Day low).

In the example above, this would be $54,610 + ($55,329 – $53,711) = $56,228.

Traders would be looking at the second major resistance level as an entry price in the event of an extended rally.

Third Major Resistance level: R3 = R2 + (Day high – Day low).

In the example above, this would be $56,228 – ($55,329 – $53,711) = $57,846.

Traders would be looking at the third major resistance level as an exit price in the event of an event-driven breakout.

Resistance FX Emp

Support and Resistance trading strategies

As previously outlined, traders can use major support and resistance levels for a range of time periods. It is therefore important to decide the trading strategies to then select the appropriate time periods for calculating the pivot and major support and resistance levels.

For instance, day traders would use 1-minute charts and the previous day’s high, low, and closing price to calculate the support and resistance levels for the day ahead.

By contrast, swing traders would use 4-hourly and daily charts to calculate the respective pivot, major support and resistance levels.

Pivot and Support Levels

When considering major support levels, the pivot levels play a hand in whether support levels are likely to come into play. There are two ways in which to consider pivot levels:

  • A fall through a pivot level would be needed to bring support levels into play. This tends to be the scenario in a post-bullish or during a bullish session.
  • Failure to move through or back through the pivot level would also bring support levels into play. This tends to be the scenario in a post-bearish or during a bearish session.

Pivot and Resistance Levels

When considering major resistance levels, the pivot levels play a hand in whether resistance levels are likely to come into play. There are two ways in which to consider pivot levels:

  • A move through a pivot level would be needed to bring resistance levels into play. This tends to be the scenario in a post-bearish or during a bearish session.
  • Avoiding a fall through or back through the pivot level would also bring resistance levels into play. This tends to be the scenario in a post-bullish or during a bullish session.

Using Support Levels

In a correcting market, an asset may fall through its first support level, labelled as S1. Once breached, the second major support level will be the next key entry point for investors. In such an event, S1 would then become a resistance level.

The 3rd major support level is generally only breached and a major economic or financial event. These include earnings, central bank and government policy, and other global events.

The below chart shows flight to safety in response to the new Omicron COVID-19 strain. Demand for the Japanese Yen broke down support levels as the Greenback slid to sub-¥114 levels.

Support Example FX Empire Chart

Historically, global events would include:

  • The global financial crisis.
  • COVID-19 pandemic.
  • Dot.com

Here, 1st and 2nd major support levels would have provided little interest to investors looking to enter the market.

3rd major support levels, however, may have drawn investors in. Key in using major support levels is for an asset price not to fall below for an extended period of time

Using Resistance Levels

In a bull market, an asset may move through its first major resistance level, labelled as R1. Once broken, the second major resistance level will be the next key entry point for investors. In such an event, R1 would then become a support level.

The 3rd major resistance level is generally only broken through as a result a major economic or financial event. These include earnings, central bank and government policy, and other global events.

As with the above example, news of the new COVID-19 strain and government plans to contain the spread led to a reversal of EUR carry trades. The EUR broke down the 3 major resistance levels on its way to $1.13 levels against the Greenback.

Resistance Example FX Empire Chart

Historically, global events would include:

  • COVID-19 Pandemic recovery.
  • Post-Global Financial Crisis recovery.
  • Central bank action.
  • U.S Presidential Election
  • In the case of equities, corporate action and earnings.

Here, 1st and 2nd major resistance levels would have provided little interest to investors looking to exit the market.

3rd major resistance levels, however, may have resulted in investors locking in profits. Key in using major resistance levels is for an asset price not to move above a specified price for an extended period of time

Once a resistance level has been broken, however, the resistance level become a support level that forms part of the major support levels for the time period in question.

Other Major Support and Resistance Levels

There are multiple indicators/strategies that traders. Traders and analysts need to consider these when using pivot levels and the major support and resistance levels described above.

Of particular importance are all-time highs and lows, and daily, weekly, monthly, and yearly highs and lows.

For example, an asset class may face resistance at its current week high that may sit below the first major resistance levels.

Other strategies include the use of Fibonacci’s, moving averages, Bollinger’s, and MACDs.

Trading without the use of support and resistance levels would likely lead to losses. More significant losses are likely, however, without a trading strategy. Importantly, the two will need to be aligned.

Earnings Week Ahead: Advance Auto Parts, Home Depot, Nvidia and Ross Stores in Focus

Earnings Calendar For The Week Of November 15

Monday (November 15)

IN THE SPOTLIGHT: ADVANCE AUTO PARTS

The leading automotive aftermarket parts retailer Advance Auto Parts is expected to report its third-quarter earnings of $2.87 per share, which represents year-over-year growth of over 2% from $2.81 per share seen in the same period a year ago.

The Raleigh, North Carolina-based company would post revenue growth of nearly 2% to $2.6 billion up from $2.54 billion registered a year earlier. The company has beaten earnings per share (EPS) estimates three times in the last four quarters.

Advance Auto Parts (AAP) operates in a defensive (recession-resistant) category and has one of the largest long-term EBIT margin expansion opportunities in our coverage (we estimate 300-400 bps over time). COVID-19 slowed parts of AAP’s transformation but gross and EBIT margin upside from internal initiatives is still expected beginning in 2021,” noted Simeon Gutman, equity analyst at Morgan Stanley.

“Significant and improving FCF generation plus share repurchases likely to enhance EPS growth. We think the combination of a defensive category, AAP’s progress generating stable top-line growth, and significant margin upside all make for an upside case. Slowing topline momentum and associated risk to margin trajectory balance the risk/reward skew.”

TAKE A LOOK AT OUR EARNINGS CALENDAR FOR THE FULL RELEASES FOR THE NOVEMBER 15

Ticker Company EPS Forecast
AAP Advance Auto Parts $2.87
JJSF J&J Snack Foods $1.28
CMP Compass Minerals International $0.62

Tuesday (November 16)

IN THE SPOTLIGHT: HOME DEPOT

The largest home improvement retailer in the United States, Home Depot, is expected to report its third-quarter earnings of $3.39 per share, which represents year-over-year growth of about 7% from $3.18 per share seen in the same period a year ago.

The home improvement retailer would post revenue growth of over 4% to $34.942 billion from $33.54 billion a year earlier. In the last two years, the company has beaten earnings per share (EPS) estimates in most of the quarters with a surprise of over 5%.

Home Depot shares have gained nearly 40% so far this year. The stock closed 1.36% higher at $372.63 on Friday. Home Depot’s better-than-expected results, which will be announced on Nov 16, could help the stock hit new all-time highs.

“Shares of Home Depot have risen and outpaced the industry year to date. The company boasts a robust surprise trend with the fifth straight quarter of earnings and sales beat in second-quarter fiscal 2021. Results gained from continued demand for home improvement projects, the robust housing market and ongoing investments. The company is effectively adapting to the demand for renovations and construction activities, driven by prudent investments,” noted analysts at ZACKS Research.

“It is gaining from growth in Pro and DIY customer categories as well as digital momentum. However, in the second quarter, the company witnessed year-over-year moderation in its comparable-store sales growth. This was due to the lapping of the high demand environment for home-improvement projects seen last year. Soft gross margin, stemming from increased penetration of lumber, has also been a drag.”

TAKE A LOOK AT OUR EARNINGS CALENDAR FOR THE FULL RELEASES FOR THE NOVEMBER 16

Ticker Company EPS Forecast
ICP Intermediate Capital £32.70
HSV Homeserve £6.60
ARMK Aramark $0.19
HD Home Depot $3.35
DLB Dolby Laboratories $0.35
LAND Land Securities £18.78
IMB Imperial Brands PLC £138.10

Wednesday (November 17)

IN THE SPOTLIGHT: NVIDIA

The Santa Clara, California- based multinational technology company, Nvidia, is expected to report its third-quarter earnings of $1.11 per share, which represents a year-over-year decline of over 60% from $2.91 per share seen in the same period a year ago.

The company, which designs graphics processing units for the gaming and professional markets, as well as system on a chip unit for the mobile computing and automotive market would post year-over-year revenue growth of over 40% to $6.8 billion.

According to Oppenheimer analyst Rick Schafer, Nvidia will report above-consensus October quarter results, lifting its price target to $350 from $235 and rating the company “outperform”.

“Supply constraints continue to weigh on the group, though we see Nvidia (NVDA), a top semi-supplier, as better positioned to secure capacity. The company’s leading soup-to-nuts software/hardware platform solidifies its AI accelerator dominance,” Oppenheimer analyst Rick Schafer wrote in his report, reported by Reuters.

TAKE A LOOK AT OUR EARNINGS CALENDAR FOR THE FULL RELEASES FOR THE NOVEMBER 17

Ticker Company EPS Forecast
BLND British Land Company £8.75
SGE The Sage Group £11.11
LOW Lowe’s Companies $2.33
CPRT Copart $0.99
NVDA Nvidia $1.11
CPA Copa -$0.19
KLIC Kulicke And Soffa Industries $2.07
TTEK Tetra Tech $1.00
HI Hillenbrand $0.91
SSE SSE £11.80

Thursday (November 18)

IN THE SPOTLIGHT: ROSS STORES

The second-largest off-price retailer in the U.S., Ross Stores, is expected to report its third-quarter earnings of $0.79 per share, which represents a year-over-year decline of over 24% from $1.02 per share seen in the same period a year ago.

The U.S. home fashion chain would post year-over-year revenue growth of nearly 16% to $4.4 billion. The company has beaten earnings per share (EPS) estimates three times in the last four quarters.

“Market share capture from competitor bankruptcies & store closures, favourable customer fundamentals, and high exposure to Hispanics, the fastest-growing US population segment, support 6-8% long-term revenue growth and 10%+ annual EPS. Upward EPS revisions appear an ongoing positive share price catalyst. Profit flow-through is magnified when comps exceed the 1-2% plan in a typical year,” noted Kimberly Greenberger, equity analyst at Morgan Stanley.

“The ‘everyday value’ proposition fosters comp outperformance, while recessions accelerate customer acquisition. Low average selling prices ($8-10/unit) and narrow gross margin render selling online unprofitable at this price point.”

TAKE A LOOK AT OUR EARNINGS CALENDAR FOR THE FULL RELEASES FOR THE NOVEMBER 18

Ticker Company EPS Forecast
NG National Grid £15.70
HLMA Halma £21.19
RMG Royal Mail -£6.30
NJR New Jersey Resources $0.08
KSS Kohl’s $0.69
HP Helmerich & Payne -$0.50
MMS Maximus $0.87
BJ BJs Wholesale Club Holdings Inc $0.79
M Macy’s $0.29
BERY Berry Plastics $1.53
NUAN Nuance Communications $0.20
BRC Brady $0.76
ROST Ross Stores $0.79
INTU Intuit $0.97
FTCH Farfetch -$0.24
ESE ESCO Technologies $0.78

Friday (November 19)

Ticker Company EPS Forecast
BKE Buckle $0.80
FL Foot Locker $1.34

 

What are Real Yields?

The concept of real interest rates or real yields hit the headlines last month, raising growth concerns for global investors. To everyday consumers, the phrase meant little, but to professional investors, it meant returns investors expect to earn after inflation.

Everyday consumers are conscious of interest rates. They know they are earning very little by putting their money in the bank. They are also aware of rising inflation, which has dominated the headlines for several months.

What they haven’t done is put together the concept of how little they really earn on their money in the bank when one strips out the inflation rate. That number is the real rate they are earning.

Investors didn’t pay much attention to the real yield when inflation was low in the sub-2% area. This is because the benchmark 10-year U.S. Treasury yield was trading in the essentially the same area. But since the start of the global economic recovery, yields have stayed near historical levels, while inflation has soared to multi-year highs. This has pushed real rates into negative territory.

Many consumers know the interest rate on their savings account, or the money they earn on their balance. However, they probably don’t know what their real interest rate is. This article will explain real interest rates.

What is the Real Interest Rate?

Simply stated, a real interest rate is an interest rate that has been adjusted to remove the effects of inflation to reflect the real cost of funds to the borrower and the real yield to the lender or to an investor.

In equation form, the real interest rate is simply equal to the nominal interest rate minus the actual or expected inflation rate.

To understand real interest rates, you have to first understand inflation. Inflation is a general, sustained upward movement in the prices of goods and services in an economy.

Inflation matters when making decisions related to interest rates on savings accounts and other financial assets. For example, when you have a savings account, interest is at work increasing the amount deposited, while inflation is at work reducing its value.

What is Your Real Rate of Return?

Knowing your real interest rate gives you an idea of what your investment is paying you after factoring in inflation. It also gives you a better idea of the rate at which their purchasing power increases or decreases.

Treasury bonds are fixed-rate U.S. government debt securities with a maturity range between 10 and 30 years.

Treasury Inflation-Protected Security (TIPS) is a Treasury bond that is indexed to an inflationary gauge to protect investors from the decline in the purchasing power of their money.

To estimate their real rate of return, an investor compares the difference between the current Treasury bond yield and the current Treasury Inflation-Protected Securities (TIPS) yield of the same maturity, which estimates inflation expectations in the economy.

What are Real Yields Telling Us about the State of the Economy and Investments?

Currently, the U.S. economy is in a low-yield, high-inflation environment and is likely to remain there until inflation starts to decline and the Federal Reserve begins to raise interest rates. Because of this, the yield on 10-year Treasury Inflation-Protected Securities (TIPS) is hovering near record lows.

In July 2021, yields on U.S. Treasuries eased after the auction of $16 billion in 10-year TIPS was bid at a record low of -1.016%.

Some analysts believe this means investors are pricing in higher inflation going forward. Other analysts believe this reflects concerns about slowing growth after a strong first half of the year. Still others are saying it’s just a function of mathematics and may not mean anything.

In other words, negative real yields are a function of the expected path of short-term interest rates set by the Fed compared with current and forecasted inflation. So there is no way that term real yields could be anything but negative in July 2021.

With yields this low, investors are throwing money at the stock market. This is driving up prices to unwarranted levels. Although stocks, for example, are overpriced using traditional indicators, investors don’t have a lot of choices if they want to beat inflation.

Negative real yields pose a big problem for pension funds and other long-term asset allocators that are also grappling with equity markets trading at high valuations.

One effect of deeply negative real yields is to buoy a range of other asset classes, as they make the returns they offer more attractive in comparison to bonds.

Real Yields Reflect the Future Investment Environment

Monitoring the direction of real yields offers investors a chance to gauge the state of the economy.

If real yields remain near record lows then this likely means investors believe elevated inflation levels are going to linger and the Fed is going to stand pat on policy. It could also indicate investor expectations of a weakening economy.

If real yields start to move higher then this will tell investors that the Fed may be getting ready to tighten policy by raising rates in the near future. It could also be an indicator of economic growth and lower inflation on the horizon.

Ethereum 2.0: What Is It and Why Is It So Important?

Ethereum began as a spark in the eye of co-founder Vitalik Buterin’s back in 2013. That’s when he released the whitepaper for the project, though the Ethereum network wouldn’t see the light of day until 2015.

In between, there was the Ether sale in 2014, which is when early buyers could scoop up some of the now second-biggest cryptocurrency using bitcoin. Back then, Ether fueled the Ethereum network as a payment method for transactions on the network, and years later it stills serves that purpose.

PoW to PoS

Ethereum was launched using the proof-of-work (PoW) consensus protocol, similar to Bitcoin. The PoW data essentially does two things:

  • Allows computer nodes, which secure and guard the platform, to agree on the validity of the information published on the Ethereum network
  • Thwarts any economic attack on the network

The PoW algorithm, however, is not perfect, and the flaws — including slow transaction times and hefty gas fees — became too big to ignore. The emergence of the Ethereum-based CryptoKitties game is a good example. The game, which introduced an early version of non-fungible tokens (NFTs), became so popular that it clogged the Ethereum network, delaying transactions and causing fees to skyrocket.

The rise of decentralized finance, or DeFi, is yet another use case that has underscored the importance of an efficient network. While the DeFi market has seen its total-value-locked (TVL) balloon since catching on like wildfire in 2020, its growth has been stifled in some ways. Some developers have opted for other blockchains, while institutions have largely remained on the sidelines until the kinks are worked out. With greater scalability and more stable fees, Ethereum would likely disrupt traditional finance even more.

This is where Ethereum 2.0 comes in. In order for developers to avoid shooting themselves in the foot with their own innovation, they are building Ethereum 2.0. This is a massive upgrade of the existing network to one that is more scalable and could hasten the adoption of the blockchain among the mainstream.

Chief among the changes is a switch in the consensus protocol from PoW to proof-of-stake (PoS). Staking will lead to greater participation in securing the Ethereum network, which in turn will create a more decentralized blockchain.

3d illustration of bitcoin and Ethereum coins

What Are the Problems With the Original Ethereum Protocol?

The version of Ethereum that was introduced in 2015 was groundbreaking, but unprecedented demand for the network exposed some issues. These problems can be boiled down into three key areas:

  • A clogged network: The blockchain became too crowded, which is not ideal when trying to attain global adoption. To maintain security, each and every computer node must verify transactions on the blockchain, which slows transaction times down.
  • Insufficient disk space: As the Ethereum network grew more popular, it became increasingly difficult to run software known as nodes. The trick is to come up with a way to increase Ethereum’s size and power without compromising decentralization.
  • High energy consumption: Ethereum’s power use to maintain the PoW consensus algorithm for network security is not sustainable for the long term.

The Ethereum team sought out to solve these issues while keeping the most important feature of the network intact: decentralization. Eth2 is the solution to achieving greater scalability and security without becoming a centralized network, though it is far from an easy task.

What Is Ethereum 2.0?

Now that we’ve established how far Ethereum has come, let’s take a look at where it is headed. Ethereum 2.0, which is synonymous with Eth2 and Serenity, is a major upgrade of the blockchain network. While it was not the first upgrade, it is the one that is designed to catapult Ethereum to total-value-locked (TVL) balloon

It is a massive undertaking among the developers that will not happen overnight. Instead, Ethereum 2.0 is unfolding in a series of steps, the first of which occurred in the year 2020 with Phase 0, otherwise known as the Beacon Chain.

The Beacon Chain is an implementation of PoS that runs alongside the PoW network and is being battle-tested first. In August 2021, Ethereum completed a hard fork dubbed London, which introduced greater stability to gas fees on the Ethereum network and presented a deflationary model to the protocol’s monetary policy. With every phase comes new functionality and enhanced performance that will ultimately lead to the destination of Ethereum 2.0, a PoS network.

What Are the Benefits of Ethereum 2.0?

Ethereum 2.0 will deliver a host of key benefits that are likely to attract even more developers to the network. The three key improvements include:

  • Greater scalability: Ethereum must be able to support thousands of transactions per second (TPS) for applications built on the network with greater speed and cheaper fees. The one-two punch of sharding and a PoS algorithm is expected to create greater scale thanks to the addition of more nodes, resulting in higher TPS without using more electricity.
  • Greater security: Ethereum must be as secure as possible to thwart attacks so that users including institutions will feel comfortable using it. The aforementioned Beacon Chain is designed to help with network security.
  • Greater sustainability: A lesser carbon footprint has become a major theme in the cryptocurrency industry. The PoW consensus algorithm consumes a great deal of energy. Ethereum 2.0 will be better for the environment as there will be no more mining involved. According to Ethereum Co-Founder Vitalik Buterin, whose vision for Ethereum is outlined in the below graphic, Ethereum’s energy consumption will be diminished “by a factor of more than 1,000” with PoS.

What’s Taking So Long?

Ethereum 2.0 comprises three separate upgrades, each of which is a monumental task in its own right.

  • Beacon Chain: Launched in 2020, this technology introduced staking to the network and paved the way for future upgrades. While the Beacon Chain is in testing mode, it is live and will eventually be the cornerstone of Eth2.
  • Merge: The Ethereum merge is expected for either late 2021 or sometime in 2022. This is where the Beacon Chain will be combined with Ethereum’s mainnet and it will make staking on the Ethereum blockchain a reality while marking an end to mining.
  • Shard Chains: This represents the splitting of the Ethereum network, which will occur in phases will result in a greater capacity for processing transactions and storing data. Sharding chains are planned for 2022.

Ethereum is one of the biggest cryptocurrencies, second only to bitcoin. The transition to Eth2 is a major series of events that solve the issues plaguing the network and could potentially lead to wide-scale adoption of the blockchain while potentially strengthening the Ether price in the interim.

Bitcoin Fork Explained

Since the Beginning

In response to the global financial crisis of 2008, Satoshi Nakamoto ventured into the unknown and delivered the global financial markets with Bitcoin and blockchain tech.

Bitcoin’s creator set on a path to bring to end the control that central banks held over the global financial markets.

The concept and ideology of blockchain and ultimately Bitcoin was to allow the community to advance the technology on a united front in a bid to bring down central banks and the world’s largest financial institutions.

Things have not turned out, perhaps, how Satoshi had intended.

Miners vs Developers

In order to police and keep Bitcoin and the blockchain world moving forward, Bitcoin and the crypto community, not only needed developers, but also miners to verify transactions on the Bitcoin network and other crypto networks.

In contrast to Satoshi’s ambition to decentralize, miners and developers, have on occasion, fallen into disagreement over blockchain enhancements and/or developments.

For Bitcoin, minors had cornered the market with mining farms, leaving want-to-be minors out in the cold. This also meant that the income stream was just too large to give up control. Decentralized became centralized in a matter of years.

As a result, the Bitcoin community and the crypto community became divided between those in search of crypto income and the ideologists looking to continue to prize control from governments, central banks, and the world’s largest financial institutions.

This divergence in view and intent ultimately led to the splitting of crypto communities. The crypto technical term for this being a “Fork.”

The Fork

In the crypto sphere, there are two types of forks that investors need to be concerned with. The first and generally of little impact to value and the broader market are soft forks.

In the event of a soft fork, only one blockchain remains valid, with users adopting the changes made to the blockchain.

By contrast, hard forks can have a material impact on price in the lead up and immediate aftermath of a fork.

In a hard fork event, both blockchains coexist. The coexistence occurs from nodes continuing to support the original blockchain.

In some instances, therefore, both blockchains can coexist and remain prominent in the crypto market place. This is when there is sufficient support for both the old and the new versions.

In some cases, however, nodes may eventually shift to the new version, leaving the old blockchain obsolete.

From an investor perspective, an important feature of a hard fork is that holders of the original crypto are awarded the new coins upon completion of the hard fork.

In the case of a successful hard fork, where both chains coexist, the value of the coins can increase substantially.

For this reason, anticipation and an eventual hard fork can have a material impact on price and crypto market volatility.

Since Bitcoin’s creation, the total number of cryptos in the market place have surged to a whopping 11,064 based on numbers from CoinMarketCap.

Notably, in spite of numerous soft and hard forks, Bitcoin (“BTC”) continues to be the dominant crypto.

The 2017 Convergence

Back in late 2017, we did see Bitcoin’s dominance converge with the likes of Ethereum. This coincided with Bitcoin’s first major hard fork, which resulted in the creation of Bitcoin Cash (“BCH”).

While Bitcoin Cash (“BCH”) enjoyed a lengthy period in the top 10 by market cap, a Bitcoin Cash hard fork in late 2018 led to the creation of Bitcoin Cash ABC and Bitcoin Cash SV.

The Bitcoin community have not been alone in dealing with hard forks.

Ethereum hard forked, leading to the creation of Ethereum Classic. In this case, Ethereum Classic maintained the old blockchain history. We also saw Litecoin hard fork, leading to the creation of Litecoin Cash.

In spite of disagreements between respective developers and the communities, however, Bitcoin, Ethereum, and Litecoin have all remained the dominant chain.

Lessons Learned

Major disagreements between developers and communities can lead to significant disruption. More importantly, market stability also comes into question.

Since the headline grabbing hard forks of Bitcoin, Ethereum, and Litecoin, the number of notable hard forks have fallen.

Developers and nodes working together to achieve Satoshi’s ambition of toppling central banks is now a more plausible outcome. Infighting had led to significant disruption and ultimately a marked decline in value.

Stability across the major crypto blockchains have supported the increased adoption. The increased adoption contributed to Bitcoin’s surge to an all-time high $64,829.0, struck in April 2021.

While volatility across the market place will unlikely abate anytime soon. The absence of hard forks and infighting, however, would serve the crypto community and investors well in the short to medium term.

The Role of Blockchain in Finance

Blockchain offers tremendous benefits for businesses. The question is whether they will use them to their advantage.

The rapidly progressing adoption of blockchain technology and cryptocurrencies are disrupting the financial industry.

According to CoinMarketCap, the crypto market – which now includes over 9,800 digital assets – has a combined capitalization of $1.25 trillion, outpacing Apple on the road to challenge gold’s leading position ($11.65 trillion).

At the same time, a recent report estimates the blockchain market to expand from 2019’s $2.01 billion to $69.04 billion by 2027 at a compound average growth rate (CAGR) of 56.1%.

By now, it has become clear that distributed ledger technology (DLT) is in high demand.

But how can blockchain and crypto help financial organizations in improving business efficiency?

Blockchain Is More Than Crypto

When most people hear the phrase “blockchain”, the first thing that comes to their minds is cryptocurrency.

Indeed so, blockchain is the underlying technology of crypto, which powers nearly all digital assets on the market while promoting transparency, high security, peer-to-peer (P2P) transactions, and decentralization.

That said, blockchain is not solely about cryptocurrency transactions. Instead, DLT can be used in almost any field related to data delivery and information processing.

For that reason, many companies are either considering or already adopted blockchain technology to enhance their business processes.

Despite that DLT is still in its very early form, there are many examples of large corporations utilizing the blockchain for real-world use-cases.

One is Walmart that has partnered with IBM and Unilever to leverage the Hyperledger Fabric blockchain for tracking product supply chains.

IBM also has its own blockchain, with the multinational tech firm becoming a leading B2B distributed ledger technology provider in recent years.

Real-world blockchain applications continue to proliferate, with an increasing number of companies integrating DLT-based solutions into their business processes to achieve higher efficiency.

Through transparency in a decentralized environment, businesses can promote trust as well as attract new customers and increase their existing clients’ loyalty, who can now track their products to assess their quality via the blockchain.

In China, the clothing-retail giant H&M partnered with the VeChain blockchain platform to implement a similar solution.

By leveraging DLT, the company’s customers can access detailed information about the production of branded clothing by simply scanning a QR code via their smartphones. Furthermore, shoppers can even watch videos of how the products in the stores were made in the factories.

The Power of Blockchain

As you can see, blockchain is a powerful tool for businesses.

And for an excellent reason, DLT offers both service providers and end-users tremendous benefits compared to traditional systems.

Due to its transparent nature, blockchain technology allows data to be tracked from start to finish, eliminating the need for blind trust from customers. At the same time, it offers an opportunity for businesses to attract more users.

Furthermore, blockchain transactions are peer-to-peer, which means there’s no need for intermediaries or other third parties. As a result, companies can significantly reduce their operational costs while improving business efficiency by accelerating and automatizing processes via smart contracts.

Despite the traceability and visibility of blockchain transactions, users do not know the real persons behind the transfers, which makes them more private than traditional solutions.

How Businesses Adopt Crypto

Blockchain and cryptocurrency often walk hand in hand.

For that reason, many businesses are increasingly exploring crypto as an asset class for investments.

Since 2020, we have seen that this has become a growing trend among not just private and digital asset businesses but also publicly traded companies.

For example, MicroStrategy, Tesla, and Square have invested $2.24 billion, $1.5 billion, and $220 million in BTC to date, respectively.

But what would happen if businesses decided to adopt cryptocurrencies for payments as well?

The thing is, many of them already did.

In addition to the travel industry where digital assets have demonstrated increased adoption for payments (e.g., Expedia, airBaltic, LOT Polish Airlines), large enterprises like Microsoft, Starbucks, AXA Insurance, etc. have integrated crypto as a payment method for their solutions.

Furthermore, while PayPal has already added support for crypto transactions, Visa and MasterCard are racing against each other to integrate digital asset settlement into their massive payment networks.

Cryptocurrency Promotes Financial Sovereignty

Compared to fiat currency, crypto has three major advantages: autonomy, convertibility, and decentralization.

Blockchain networks are highly resilient against network issues and do not require third-party intervention to operate.

For that reason, cryptocurrencies are virtually independent of government action, with the latter potentially causing severe failures in the monetary system that can often lead to economic collapses.

Furthermore, with the industry maturing, it has become much easier to exchange fiat currency to crypto with only a small commission.

Thereby, crypto can be effectively used for cross-border transactions, which usually feature much faster settlements and cost-efficient fees compared to traditional international transfer (especially for payment-optimized assets like XRP or XLM).

Businesses Must Adopt Blockchain to Become More Efficient

Blockchain is a technology that is still being studied.

Yet, despite its early development stage, DLT already has a lot to offer for the companies willing to adopt it.

Besides, as more of blockchain’s potential gets harnessed, we will undoubtedly see drastic changes in the financial industry and many other sectors as key players seek to achieve greater operational efficiency.

Petr Kozyakov, co-founder and CEO at the global payment network Mercuryo

Cost-Push and Demand-Pull Inflation: Definitions and Examples

Economists tell us that controlled inflation is a sign of economic growth. Central banks, such as the U.S. Federal Reserve, actually set monetary policy to maintain a consistent inflation rate of around two percent per year.

The gradually rising prices associated with inflation can be caused in two main ways:  cost-push inflation and demand-pull inflation.

Both are associated with the principles of supply and demand.

What is Cost-Push Inflation?

Economists describe cost-push inflation as a condition when the supply of goods or services is limited in some way but demand remains the same, pushing up prices. The increased price of labor or raw materials, for example, leads to decreased supply of these goods. While demand remains constant, the prices of commodities increase causing a rise in the overall price level.

The overall price level increases due to higher costs of production which reflects in terms of increased prices of goods and commodities which primarily use these inputs. This is essentially inflation triggered by less supply.

Cost-Push Inflation is usually associated with an unexpected external event like a natural disaster or the depletion of natural resources, monopoly, government regulation, government taxation, and changes in exchange rates. Basically, any event that hinders a company’s ability to produce enough of certain goods to keep up with consumer demand. This forces them to raise or inflate prices.

Examples of Cost-Push Inflation

The most common example of cost-push inflation occurs in the energy sector – oil and natural gas prices.

You and pretty much everyone else need a certain amount of gasoline to fuel your car or natural gas to heat your home. Refineries need a certain amount of crude oil to create gasoline and other fuels. Electric power suppliers need high levels of natural gas to create electricity.

When global policies, war, or natural disasters drastically reduce the oil supply, gasoline prices rise because demand remains relatively stable even as supply shrinks. Additionally, the recent shutdown of a natural gas pipeline due to cyber-theft trimmed the supply of natural gas, driving up prices despite steady weather-driven demand.

Hurricanes or floods are often the causes of cost-push inflation when they lead to the shutdown of certain refineries. Demand usually remains the same, but the refineries available to produce gasoline usually have to jack up prices because they don’t have enough crude oil supply to turn into fuel.

What is Demand-Pull Inflation?

Demand-pull inflation is the tendency for prices to increase due to increasing aggregate demand, or the amount of goods and services the entire population buys. This type of inflation is usually associated with a strong economy.

As an economy strengthens, employment tends to rise. As more people go back to work, they make more money and they spend more money. However, if goods are limited at a time when people are willing to spend more money, competition among consumers drives prices up. Economists often refer to this type of inflation as “too many dollars chasing too few goods.”

Demand-pull inflation is not limited to the consumer sector of the economy. We get a similar outcome if the government puts more money into circulation, or if a low interest rate environment encourages too much borrowing.

Examples of Demand-Pull Inflation

In March of 2020, the global economy shut down due to the coronavirus pandemic. With the advent of a number of vaccines in late 2020, the global economy began to slowly open up. As the availability of vaccines increased, the pace of vaccinations rose sharply and the global economic recovery moved forward at a rapid speed.

The global economic recovery is driving up demand for goods and services that weren’t readily available for close to a year. Inventories have been depleted as consumers demand more food, household items, and fuel. This increased demand is “pulling” up prices.

Employment is rising also which means consumers have more disposable income. Gasoline demand and prices are rising as more employees drive to work. Airline tickets and hotel rooms are also rising as pent-up consumers increase travel.

The current low-interest-rate environment is keeping a lid on mortgage rates, which is encouraging consumers to buy more houses, but with the supply of homes limited, prices are skyrocketing. Some are buying new homes which have driven up the prices of lumber and copper to near-record levels.

Essentially, as the global economy opens up, individuals want to spend money, but factories haven’t been able to meet demand as quickly. Consumers are willing to pay higher prices, thereby, creating demand pull-inflation.

Advance Auto Parts Earnings to More Than Double in Q1; Target Price $221

The leading automotive aftermarket parts retailer Advance Auto Parts is expected to report its first-quarter earnings of $3.05 per share, which represents year-over-year growth of over 235% from $0.91 per share seen in the same period a year ago.

The company would post quarterly revenue of $3.31 billion, up from $2.70 billion seen in the same period a year ago. The company forecasts full-year 2021 net sales in the range of $10.1-$10.3 billion.

The Raleigh, North Carolina-based company is scheduled to issue its quarterly earnings results before the market opens on Wednesday, June 2. Advance Auto Parts shares traded 2.4% higher at $194.26 on Tuesday. The stock rose over 20% so far this year.

Analyst Comments

Advance Auto Parts (AAP) operates in a defensive (recession-resistant) category and has one of the largest long-term EBIT margin expansion opportunities in our coverage (we estimate 300-400 bps over time). COVID-19 slowed parts of AAP’s transformation but gross and EBIT margin upside from internal initiatives is still expected beginning in 2021,” noted Simeon Gutman, equity analyst at Morgan Stanley.

“Significant and improving FCF generation plus share repurchases likely to enhance EPS growth. We think the combination of a defensive category, AAP’s progress generating stable top-line growth, and significant margin upside all make for a positive risk/reward skew.”

Advance Auto Parts Stock Price Forecast

Fifteen analysts who offered stock ratings for Advance Auto Parts in the last three months forecast the average price in 12 months of $221.38 with a high forecast of $235.00 and a low forecast of $200.00.

The average price target represents a 14.04% increase from the last price of $194.12. Of those 15 analysts, 12 rated “Buy”, three rated “Hold” while one rated “Sell”, according to Tipranks.

Morgan Stanley gave the stock price forecast to $215 with a high of $275 under a bull scenario and $145 under the worst-case scenario. The firm gave an “Overweight” rating on the health care company’s stock.

Several other analysts have also updated their stock outlook. BTIG Research raised shares from a neutral rating to a buy rating and set a $140 price objective. SVB Leerink raised their price target to $128 from $115 and gave the stock a market perform rating. Raymond James raised their price target to $130 from $126 and gave the stock an outperform rating.

Check out FX Empire’s earnings calendar

Earnings to Watch Next Week: Zoom, Advance Auto Parts, Lululemon and Cooper Companies in Focus

Earnings Calendar For The Week Of May 31

Monday (May 31)

There are no major earnings scheduled

Tuesday (June 1)

IN THE SPOTLIGHT: ZOOM

The San Jose, California-based communications technology company Zoom is expected to report its first-quarter earnings of $0.99 per share, which represents year-over-year growth of about 395% from $0.20 per share seen in the same period a year ago.

The company, which provides videotelephony and online chat services through a cloud-based peer-to-peer software platform, would post revenue growth of 175.8% to $905.24 million.

For first-quarter fiscal 2022, Zoom forecasts revenues in the range of $900 million and $905 million. Non-GAAP income from operations is expected in the range of $295 million and $300 million. Moreover, non-GAAP earnings are expected in the 95-97 cents-per-share range.

The cloud video communications provider forecasts revenues in the range of $3.760 billion and $3.780 billion for the full fiscal year.

“Sentiment improving, but still leans negative heading into FQ1. Commentary around 2H churn / Phone still likely more incremental to move vs. 1Q print / 2Q guide. Profitability potential meaningful LT, but balanced in NT by churn concerns, keeping us EW into print,” noted Meta A Marshall, an equity analyst at Morgan Stanley.

Zoom has established its position as the newly emerged leader in video conferencing, now a growth market, largely credible to the company itself given an introduction of a solution that employees actually use. The company has a meaningful competitive moat built on more than just architecture, but a rapid uptick in video usage has attracted significant investment efforts from competitors. Position within customers makes an attractive opportunity to expand into the broader UC market. Early wins encouraging. Environment post-COVID and large-scale WFH, and timing to reach, less certain.”

TAKE A LOOK AT OUR EARNINGS CALENDAR FOR THE FULL RELEASES FOR THE JUNE 1

Ticker Company EPS Forecast
BNS Scotiabank $1.45
HPE Hewlett Packard $0.42
AMBA Ambarella $0.17
MDLA Medallia, Inc. -$0.07
ZM Zoom Video Communications $0.99

Wednesday (June 2)

IN THE SPOTLIGHT: ADVANCE AUTO PARTS

The leading automotive aftermarket parts retailer is expected to report its first-quarter earnings of $3.05 per share, which represents year-over-year growth of over 235% from $0.91 per share seen in the same period a year ago. The company would post revenues of $3.31 billion.

AAP operates in a defensive (recession-resistant) category and has one of the largest long-term EBIT margin expansion opportunities in our coverage (we estimate 300-400 bps over time). COVID-19 slowed parts of AAP’s transformation but gross and EBIT margin upside from internal initiatives is still expected beginning in 2021,” noted Simeon Gutman, equity analyst at Morgan Stanley.

“Significant and improving FCF generation plus share repurchases likely to enhance EPS growth. We think the combination of a defensive category, AAP’s progress generating stable top-line growth, and significant margin upside all make for a positive risk/reward skew.”

TAKE A LOOK AT OUR EARNINGS CALENDAR FOR THE FULL RELEASES FOR THE JUNE 2

Ticker Company EPS Forecast
DCI Donaldson $0.58
AAP Advance Auto Parts $3.05
NTAP NetApp $1.12
PVH PVH $0.83
CLDR Cloudera Inc. $0.08
SPLK Splunk -$0.70
GWRE Guidewire Software -$0.24
AI Arlington Asset Investment -$0.25
SMAR Smartsheet Inc. -$0.14
SMTC Semtech $0.52
OMVJF OMV $0.97

Thursday (June 3)

IN THE SPOTLIGHT: LULULEMON ATHLETICA, COOPER COMPANIES

LULULEMON ATHLETICA: The Vancouver-based retailer healthy lifestyle-inspired athletic retailer is expected to report its fiscal first-quarter earnings of $0.90 per share, which represents year-over-year growth of over 309% from $0.22 per share seen in the same period a year ago.

The apparel retailer would post year-over-year revenue growth of over 70% to $1.12 billion.

“Revenue & GM upside could yield a 16c 1Q21 EPS beat vs. the Street. While 1Q21 beats & raises haven’t been enough to send most Softline retailers’ shares higher, LULU may be an exception as investors move up the quality curve. Trim PT to $377 on an updated WACC; raise 1Q21 EPS on better sales,” noted Kimberly Greenberger, equity analyst at Morgan Stanley.

COOPER COMPANIES: The global medical device company is expected to report its fiscal first-quarter earnings of $3.09 per share, which represents year-over-year growth of over 104% from $1.51 per share seen in the same period a year ago.

The San Ramon, California-based company would post revenue growth of 31% to $690.73 million.

“Shares of Cooper Companies outperformed the industry in the past six months. The company exited the fiscal first quarter on a strong note, wherein both earnings and revenues beat their respective consensus mark,” noted analysts at ZACKS Research.

“The company witnessed solid performance across its core CVI and CSI units during the quarter under review. Expansion in both gross and operating margins is a positive. Management at Cooper Companies remains optimistic about the Clarity, MyDay and Biofinity suite of products and the portfolio of daily silicone hydrogel lenses, which makes it one of the leaders in the soft contact lens market.”

TAKE A LOOK AT OUR EARNINGS CALENDAR FOR THE FULL RELEASES FOR THE JUNE 3

Ticker Company EPS Forecast
SJM J.M. Smucker $1.66
CIEN Ciena $0.48
TTC Toro $1.18
LULU Lululemon Athletica $0.90
WORK Slack Technologies -$0.01
MDB MongoDB Inc -$0.35
SAIC Science Applications International $1.53
DOCU DocuSign Inc. $0.28
AVGO Avago Technologies $6.43
FIVE Five Below $0.65
PD PagerDuty Inc. -$0.09
COO Cooper Companies $3.09
CRWD CrowdStrike Holdings Inc. Cl A $0.06
PLUG Plug Power -$0.08
JOBS 51job $0.43
TOELY Tokyo Electron Ltd PK $1.25
ASEKY Aisin Seiki Co $0.88
AUOTY AU Optronics $0.45

Friday (June 4)

There are no major earnings scheduled

The Complete Guide to Trend-Following Indicators

Trend-following indicators are technical tools that measure the direction and strength of trends in the chosen time frame. Some trend-following indicators are placed directly on the price panel, issuing a bearish signal when positioned above price and a bullish signal when situated below price. Others are drawn below the panel, generating upticks and downticks from 0 to 100 or across a central ‘zero’ line, generating bullish or bearish divergences when opposing price.

Most trend-following indicators are ‘lagging’, meaning they generate a buy or sell signal after a trend or reversal is underway. The moving average is the most popular lagging trend-following indicator. These indicators can also be ‘leading’, meaning they predict price action before it starts by using multiple calculations and comparing momentum in different time frames. Parabolic Stop and Reverse (Parabolic SAR) is a popular leading trend-following indicator.

These indicators have three primary functions. First, they attempt to alert the technician to a developing trend or an impending reversal. Second, they attempt to predict short- and long-term price direction. Third, they confirm observations and signals in the price pattern and other technical indicators. Signal reliability is limited to the settings used to draw the trend-following indicator. For example, a 50-day moving average and a 200-day moving average generate unique buy and sell signals that may work in one time frame but not the other.

Simple Moving Average (SMA)

The Simple Moving Average (SMA) measures the average price across a range of price bars chosen in the settings by the technician. Closing prices are commonly used in the calculation but the open, high, low, or median price is often applied as a substitute. The indicator is a highly-effective technical tool used to evaluate the strength of the current trend and to determine if an established trend will continue or reverse. The SMA is less effective for prediction in sideways and rangebound markets.

The calculation simply sums up prices over the chosen period and divides by that period. Each data point adds to a line placed in the same panel as price. Interactions between price and the moving average generate bullish and bearish divergences that evaluate trend strength and direction. For example, price falling below a 20-day SMA in an established uptrend denotes unusual weakness while price lifting above the 20-day SMA in a downtrend denotes unusual strength. The direction of the SMA also generates a divergence when opposing price action.

Exponential Moving Average (EMA)

The Exponential Moving Average (EMA) measures the average price across a range of price bars chosen by the technician, placing greater weight on more recent data points. The EMA is a ‘weighted moving average’, meaning that price bars are not treated equally in the calculation.  This moving average responds more quickly to recent price action than the simple moving average, theoretically generating earlier buy and sell signals.

The calculation assumes that recent price action will have a greater impact on trend direction than an equally weighted data series. However, this weighting also tends to generate more false signals than the SMA. Examining several EMAs at different time intervals can overcome the shortcomings of this moving average but the added complexity requires stronger interpretation skills. The 20-day, 50-day, and 200-day EMA combination has grown popular among traders in recent decades. The direction of the EMA and its relative positioning with price generate convergence-divergence relationships that are useful in trade management.

Average Directional Index (ADX/DMS)

The Average Directional Index (ADX/DMS) measures the strength or weakness of an active trend. The quality of trend strength should correlate strongly with the persistence of the trend and capacity to generate profits for the trend-following trader or investor. ADX uses moving averages in several time frames to generate three lines that cross higher or lower through a panel with values between 0 and 100. These lines are designated ‘ADX’, ‘+DMI+’, and ‘-DMI‘.

ADX measures the strength or weakness of an uptrend when +DMI is above -DMI, indicating that prices are rising. Conversely, ADX measures the strength or weakness of a downtrend when +DMI is below –DMI, indicating that prices are falling. ADX with values at or below 25 denotes a weak trend or rangebound market, lowering the reliability of this trend-following strategy. ADX direction also generates momentum signals, with a trend gathering strength when rising and losing strength when falling.

Moving Average Convergence-Divergence (MACD)

Moving Average Convergence-Divergence (MACD) is a highly popular technical tool developed by Gerald Appel in the 1960s. MACD analyzes the relationship between moving averages set at different intervals, generating a set of directional lines or a histogram that gauges current momentum and price direction. The indicator is commonly calculated by subtracting at 26-period EMA from a 12-period EMA. A 9-period EMA of the MACD, called the ‘signal line’, is then added to the plot.

MACD emits an assortment of visual data that generates crossovers, divergences, and sharp directional swings. The MACD histogram draws the distance between MACD and signal line and has become the most popular method to apply this indicator.  A bullish divergence occurs when the histogram turns higher at an extreme below a zero line while price is falling and a bearish divergence when the histogram turns lower at an extreme above a zero line while price is rising. Crossovers above and below the zero line can also generate potent buy and sell signals.

Parabolic Stop and Reverse (Parabolic SAR)

Developed by RSI creator Welles Wilder Jr, the Parabolic Stop and Reverse (Parabolic SAR) is used by technicians to confirm trend direction and to generate reversal signals. However, signals are only applicable within the time setting applied to the indicator. Indicator data points generate dots above or below price on the main chart panel. The calculation applies an ‘invisible’ trailing stop, forcing the indicator to change direction when hit, marking a potential reversal in trend. The indicator often generates reliable signals in strong trends and whipsaws in rangebound markets.

Parabolic SAR is most useful when analyzed in combination with the overall price pattern and other trend-following indicators. The trader or investor can also use the indicator as a tool to manage long and short positions, raising or lowering the stop loss after each data point to match the price of the last dot. Keep in mind this indicator issues continuous buy or sell signals, forcing the technician to look at other data to avoid over-trading.

Additional Trend-Following Indicators

Accumulative Swing Index – evaluates the long-term trend through changes in opening, closing, high, and low prices.                                                                                                                                    

ADX/DMS – measures the strength or weakness of an active trend and how long it may persist before a reversal.                                                                                                                             

Alligator – uses three Fibonacci-tuned moving averages to identify trends and reversals.

Aroon – evaluates whether a security is trending or rangebound and, if trending, the strength or weakness of the advance or decline.                                                                                                        

Aroon Oscillator – applies data from the Aroon indicator to determine if a trend is likely to persist. The oscillator generates a zero line that, when crossed, signals potential changes in trend.

Elder Ray Index – evaluates buying and selling pressure by separating price action into bull and bear power. The output is used to determine trend direction and high odds entry/exit prices.

Gator Oscillator – is used as an alternative to the Alligator indicator, drawing green and red histograms to determine if trends are getting stronger or weaker.

Linear Regression Forecast – uses regression analysis to compare price action to the expected mean, looking for high odds reversal signals.

Linear Regression Intercept – uses regression analysis to compare price action to the predicted value, looking for high odds reversal signals.

Linear Regression R2 – analyses the reliability of price vs regression prediction.                                                                                                                     

Linear Regression Slope – determines the average rate of change when using regression analysis and compares price action to the expected mean.

QStick – identifies trends by examining a moving average of the difference between the opening and closing price of a hourly, daily, weekly, or monthly price bar.

Rainbow Moving Average – plots multiple weighted moving averages to determine price extremes over the examined period.                                                                                                          

Rainbow Oscillator – uses rainbow moving averages to evaluate trends by plotting bandwidth lines at the edges of a histogram.                                                                                                    

Random Walk Index – compares an asset’s movement to random movement to determine if its noise or signal. The indicator issues buy and sell signals, depending on trend strength or weakness.

RAVI – also known as the Range Action Verification Index, the indicator evaluates current trend and projects future trend intensity through a histogram plotted with two moving averages.

Schaff Trend Cycle – identifies trends and issues buy and sell signals by examining acceleration and deceleration of price change over time.

Shinohara Intensity Ratio – evaluates trend intensity by plotting Strong Ratio (Strength) and Weak Ratio (Popularity) lines.                                                                                                                                               

Supertrend – draws an overlay across price action that seeks to identify current trend direction.                                                                                                      

Swing Index – predicts price action in short-term trading strategies through crossovers above and below a zero line.                                                                                                                                       

Time Series Forecast – uses linear regression to identify divergences between current price and the expected mean. It is constructed to be more flexible than basic linear regression analysis.

Trend Intensity Index – tracks correlation between price movement and volume levels to evaluate the strength or weakness of a trend.

TRIX – displays percentage change of a triple smoothed exponential moving average in an effort to filter out inconsequential price movement.

Typical Price – draws a straight line plot of the average price for each bar to generate a more realistic view of developing trends than using closing prices.

Vertical Horizontal Filter – measures the intensity of trends by looking at the highest and lowest prices over the specified time period.                                              

Weighted Close – calculates the average price between the high, low, and close of each bar, placing greater weight on the close.

ZigZag – connects plot points on a price chart that reverse whenever the asset reverses by more than a specified percentage.

The Complete Guide to Momentum Oscillators

The momentum oscillator is a technical tool that issues a signal when a price move or trend is about to start. It can fluctuate between an upper and lower band or across a zero line, highlighting relative strength or weakness within a specific time frame. Many oscillators generate values between zero and 100 while band placement near those extremes denotes ‘overbought’ or ‘oversold’ conditions that raise odds for a reversal. They can also feature multiple lines that generate signals when ‘crossing over’. Strongly-trending securities can get overbought or oversold and stay that way for long periods.

These are forward-looking indicators rather than trend-following indicators, with crossovers and reversals at band extremes often defining pauses in the broader trend, rather than trend reversals. These types of indicators generate the most potent buy and sell signals when looking for convergences or divergences within a set of time frames, like monthly, weekly, and daily charts. They can also issue potent trend breakout and reversal signals when used in conjunction with moving averages and other lagging indicators that apply moving averages to create values.

An oscillator can generate useful guidance when the underlying trend isn’t clear and the trader focuses on buy or sell ‘cycles’, as opposed to ‘signals’. The reasoning is easy to understand. A buy cycle doesn’t necessarily translate into higher prices while a sell cycle doesn’t necessarily translate into lower prices. However, cycle alternation often foretells the transition from a trend into a trading range, and vice-versa. It can also predict when the established trend’s trajectory is going to increase or decrease.

Stochastic Oscillator

Stochastic

The Stochastic oscillator was developed by George Lane in the 1950s. It’s become hugely popular since that time due to a high degree of accuracy in determining when it’s a good time to buy or sell a security. The indicator looks at an instrument’s closing price and compares that value to the price range over a specified time period. The ability to close higher within those values lifts the Stochastic to a higher number between zero and 100. A security typically enters the overbought zone when above 80 and the oversold zone when below 20. The 5-smoothed or 5-3-3 Stochastic setting is highly effective for position and swing trading.

The Stochastic generates two lines, a lead line and a signal line that ‘crosses over’ when certain conditions are met. Use this indicator to determine if a security is engaged in a buy or sell cycle within the time period under examination. The indicator’s power of prediction grows geometrically when comparing buy and sell cycles in multiple time frames. For example, a security engaged in a monthly Stochastic buy cycle may also be engaged in a weekly Stochastic sell cycle. Correct interpretation when these types of cycles are in conflict can generate excellent trade entry and exit timing, as well as windfall profits.

Relative Strength Index

Relative Strength Index

Welles Wilder Jr. introduced the Relative Strength Index (RSI) in 1978. RSI examines the characteristics of recent price change to evaluate momentum and to identify overbought or oversold readings that predict cycle reversals. Like other oscillators, RSI fluctuates between zero and 100, with a reading above 70 typically denoting an overbought security while a reading below 30 typically denotes an oversold security.  Unlike Stochastic, RSI generates just a single value that changes in reaction to the latest price bar.

RSI is constructed by looking at a computation in which average gains are divided by average losses over a specified period. 14 (days, weeks, or minutes) is the indicator’s most popular setting. The plot is placed below the price chart, generating a convergence when top and bottom panels move in the same direction and a divergence when moving in opposite directions. A rising RSI when price is falling marks a bullish divergence that often works well with a pullback strategy while a falling RSI when price is rising marks a bearish divergence that can support all sorts of profitable short sales.

Money Flow Index

Money Flow Index

Money Flow Index (MFI) looks at price and volume to identify overbought and oversold conditions. The indicator oscillates between zero and 100, rather than carving a traditional pattern.  As with RSI, MFI generates convergence-divergence signals when comparing the trajectories of price and indicator, with divergences often producing the most profitable buy or sell signals. In addition, the calculation looks at volume’s contribution to price action, encouraging technicians to call it the ‘volume-weighted RSI’. MFI hits overbought above 80 and oversold below 20 and, like RSI, 14 (days, weeks, or minutes) is the most popular setting.

MFI separates up days from down days, measuring volume generated by those sessions. Indicator plots are characterized as “Positive Money Flow’ when rising and ‘Negative Money Flow’ when falling, often matching up well with accumulation-distribution indicators.  For example, falling volume while price is rising above the overbought level can turn the MFI lower, generating a bearish divergence that warns about an imminent reversal. The indicator works best when used in conjunction with pattern analysis, looking for instances when a higher high or lower low in one plot isn’t matched by the other plot.

Price Rate of Change

Price Rate of Change

Price Rate of Change (ROC) measures momentum by looking at the percentage change in price between the current bar and a specified prior period. Unlike other oscillators, the ROC indicator plot starts at a central zero line and moves into positive or negative territory, depending on price movement over the examined period. In addition to generating convergence and divergence data, crossovers through the zero line also elicit buy and sell signals. ROC values are unbounded, meaning they can go well above 100 or well below -100. As an example, Gamestop ROC reached 1,800 during the historic 2021 short squeeze.

A rising ROC above the zero line confirms an uptrend within the applied setting while a falling ROC below the zero line confirms a downtrend. Rangebound price action generates ROC oscillation around the zero line, limiting its usefulness. In addition, signals only apply to the time period under examination and different settings always produce different signals. Signals also change when looking at different chart intervals, like daily, 60-minute, or 15-minute views. As a result, comparisons between intervals also generate convergence and divergence that requires interpretation. Many platforms default to a 14 ROC setting but 9 and 25 have also grown popular in recent years.

Commodity Channel Index

Commodity Channel Index

Commodity Channel Index (CCI) evaluates trend direction and strength, generating a plot that market technicians use to identify the best times to enter or exit a position. CCI also works well as a trade filter, identifying dull markets when it’s best to stand aside. The indicator examines the difference between the current price and an historical average price set by the technician. 20 periods is a popular setting. A CCI above zero indicates the current price is above the historic price while a negative CCI indicates the current price is below the historic price.

Unlike RSI and Stochastic, CCI values are unbounded and can go above 100 or below -100, making arbitrary overbought and oversold bands less useful for signal generation. As a result, the technician needs to compare current CCI extremes to prior turning points, which will change from asset to asset. As with other momentum oscillators, CCI can diverge from price, signaling potential weakness in an uptrend and potential strength in a downtrend. Pullback strategies often work well in timing long and short entries into these divergent conditions.

Additional Momentum Oscillators

Awesome Oscillator – evaluates momentum to determine if bulls or bears are controlling price action of a security.

Center Of Gravity – is a forward-looking indicator that generates crossovers to identify high odds turning points in rangebound markets.

Chande Forecast Oscillator – measures the percentage difference between a closing price and a linear regression line over a specified time period. An oscillator reading above zero predicts higher prices while an oscillator reading below zero predicts lower prices.

Chande Momentum Oscillator – subtracts the sum of losses over the specified time period from the sum of gains over the specified time period, and divides the total by the sum of all price movement over the specified time period.

Coppock Curve – calculates a 10-month weighted moving average of the sum of the 14-month and 11-month rates of change of an index to determine long-term momentum.

Disparity Index – evaluates the current price of a security in relation to a moving average.

Ease of Movement – is a volume-weighted indicator that gauges how easily price moves up or down through a formula that subtracts the prior average price from the current average price and divides the difference by volume.

Ehler Fisher Transform – isolates price movement to determine when a security hits an extreme, raising odds for a reversal.

Elder Force Index – quantifies the relative power needed to move price by comparing current price to prior price and multiplying by trading volume during the period.

Elder Impulse System – combines trend-following and momentum data to identify inflection points where a trend is likely to accelerate or slow down.

Fractal Chaos Oscillator – seeks to determine the choppiness or trendiness of a security, returning to zero in choppy conditions and hitting +N and -N extremes in trending conditions.

Intraday Momentum Index – combines candlestick and relative strength data to determine when a market is overbought or oversold.

Market Facilitation Index – measures the strength or weakness of price movement, seeking to determine if an uptrend or downtrend will persist or reverse.

Momentum Indicator – evaluates the strength or weakness of price movement over time, seeking to identify high odds reversal signals.

Pretty Good Oscillator – measures the distance of the close from the specified simple moving average, modified by the average true range over the same period.

Price Momentum Oscillator – applies smoothing calculations to Price Rate of Change to determine relative strength and weakness.

Price Oscillator – calculates the difference between pre-chosen moving averages, looking for overbought-oversold and convergence-divergence signals.

Price Volume Trend – looks at directional movement and trend intensity through a cumulative plot that multiplies volume by price percentage change over a given period.

Prime Number Oscillator – identifies high odds turning points by taking the prime number closest to the current price and calculating the difference between the nearest prime numbers across a specified time period.

Pring’s Know Sure Thing – interprets price rate-of-change data through a plot that identifies overbought or oversold extremes.

Pring’s Special K – evaluates trend intensity in multiple time frames to build a comprehensive view of asset cyclicity. It is primarily used to identify reversals before they unfold and to locate high odds entry/exit levels.

Psychological Line – computes the ratio of rising price bars to the total number of price bars over the specified time period. A reading above 50% indicates bulls are in control while a reading below 50% indicates bears are in control.

Relative Vigor Index – measures trend strength by contrasting the closing price to the trading range over a specified time period.

Stochastic Momentum Index – refines the Stochastics oscillator, applying a broader range of price settings and placing more weight on closing prices.

Ultimate Oscillator – applies weighted moving averages in multiple time frames to measure momentum.

Valuation Lines – calculates and displays the average of visible prices by applying multiple standard deviations.

Williams %R – measures trend momentum over the specified time period by drawing an oscillator bounded by 0 and 100.

The Complete Guide to Comparison Indicators

The comparison indicator is a technical tool that analyzes relationships between two or more securities, indices, or markets. It compares prices, volume, and/or volatility, determining which instrument is relatively stronger or weaker over the chosen time frame. This type of analysis elicits a series of convergence and divergence signals, with the leading instrument generating a bullish divergence when it gets even stronger compared to the lagging instrument, and a bearish convergence when it gets even weaker compared to the lagging instrument.

This type of analysis may also measure correlation, which is the tendency of one security or indicator to mimic the behavior of another security or indicator. Correlation analysis first measures the relationship between two securities, or a security and a technical indicator. The resulting values are then strung together in a new indicator that visualizes how changes in one variable over time influence changes in the other variable.

This plot allows the technician to predict how price behavior in a correlated market will influence investment or trading returns. When computing correlation, one security or indicator is considered to be the dependent variable while the other security or indicator is considered to be the independent variable.  The calculation determines if a change in the independent variable is expected to result in a similar change in the dependent variable.

Plot

Plot

The Plot function simply adds a second security to an Advanced Chart price panel and examines relative strength and weakness between instruments. This built-in charting function normalizes divergent security behavior by replacing Y-axis prices with percentage change. The first security becomes the independent variable when performing this analysis while the second security acts as the dependent variable and can be substituted with other securities, as needed.

Comparison and correlation analysis can be accomplished in several ways with the Plot function. The easiest method just scans price action over days, weeks, or months, looking for relative highs and lows to occur at the same time. Crossovers between securities are especially useful in this analysis because it identifies turning points and divergences, in which one security shifts from a leading into a lagging relationship with the other security. The Plot function is used in conjunction with the Correlation Coefficient and Performance Index indicators.

Correlation Coefficient

Correlation Coefficient

Correlation Coefficient indicator evaluates the relationship between a security price and a related technical indicator, or two securities. As noted above, correlation is measured by choosing an independent variable and comparing performance against a dependent variable, which is usually a related security. Indicator output ranges from plus 1 to minus 1 (+1 to -1), with perfect positive correlation at the upper limit and perfect negative correlation at the lower limit.

A perfect positive reading specifies that any change in the independent variable will generate an identical change in the dependent variable. Conversely, a perfect negative reading specifies that any change in the independent variable will generate an identical but opposite change in the dependent variable.  Not surprisingly, a correlation coefficient of 0 specifies there is no relationship or correlation between the two variables.

The direction of the dependent variable’s plot over time depends on whether the coefficient is positive or negative. When the coefficient is positive, the dependent variable will move in the same direction as the independent variable. Conversely, when the coefficient is negative, the dependent variable will move in the opposite direction of the independent variable. Choosing an appropriate holding period is critical when applying this analysis to trade management because correlation tends to oscillate over time, generating frequent whipsaws.

Performance Index (PI)

Performance Index

Performance Index is used in conjunction with the Plot function. The indicator compares a security’s price to a benchmark index or other security, generating a line that turns green above a reading of 1.0 and red below a reading of 1.0. A reading of exactly 1.0 indicates perfect correlation with the underlying security or index. A security that outperforms a benchmark index should generate higher returns in a trend-following strategy than a security that underperforms a benchmark index.

A rising PI indicates the security is making a stronger move to the upside than the underlying index, or the security is rising while the underlying index is falling. A declining PI indicates the security is declining at a faster rate than the underlying index, or the security is falling while the underlying index is rising.  Flat PI readings indicate that both markets are gaining or losing value at an equal pace.

Price Relative

Price Relative

Price Relative indicator compares the performance of a security against an underlying index, sector, or another security through a ratio chart.  The indicator is also called the Relative Strength indicator, not to be confused with Wilder’s Relative Strength Index (RSI). This analysis generates convergence and divergence signals that may predict relative returns on active positions over time.

It is a simple plot, calculated by taking the closing price of a dependent variable (base security) and dividing it by the closing price of an independent variable (comparative security). A rising ratio indicates the base security is rising at a faster pace than the comparative security, or falling at a slower rate than the comparative security. Conversely, a falling ratio indicates the base security is falling at a faster pace than the comparative security, or rising at a slower pace than the comparative security.

The Complete Guide to Range Indicators

The range indicator is a technical tool that measures the limits of price movement over a specified time frame. It is estimated that market prices are engaged in uptrends and downtrends just 15% to 20% of the time, with the balance spent within the boundaries of trading ranges that can be relatively narrow or wide. This indicator attempts to determine the characteristics of prices caught within these ranges, seeking to predict future movement and direction.

Many range indicators look for boundaries between ranges and trends, with the technician seeking to profit when a) a trend eases into a trading range or b) a trading range yields a new trend, higher or lower. One popular method to perform this analysis identifies range boundaries and then measures the quality of price action between highs and lows. This analysis often includes volatility calculations, looking for transitions from low to high volatility, and vice-versa, to generate preliminary buy and sell signals.

Other types of technical indicators can perform sophisticated range analysis as well. For example, Bollinger Bands will expand during trends and contract during range development. Bands tend to narrow to an extreme at the starting point of a new trend and widen to an extreme at the starting point of a new trading range. Those turning points can generate actionable entry or exit signals, especially when confirmed through other forms of technical analysis.

Average True Range

ATR

Average True Range (ATR) measures volatility by examining a security’s price action over a specified time period. The initial calculation subtracts the high from the low of a single price bar and compares that value to price ranges in prior bars. The final calculation is derived from a smoothed moving average of these values (true ranges) over N periods, with ‘N’ the time setting chosen by the technician. 14 days (or periods) is the most common ATR setting.

Securities with high ATR readings are more volatile than securities with low ATR readings but the calculation does not predict price direction. Rather, it is a supplementary technical tool best used in conjunction with trend-following and momentum indicators. Many traders develop exit strategies using ATR multiples that seek to identify when volatility has reached an unsustainable level. In addition, the indicator has powerful applications in determining position size and risk.

Darvas Box

Darvas Box

The Darvas Box indicator generates rectangular-shaped boxes that rise or fall over time. Although frequently listed as a momentum indicator, the formula identifies rangebound market conditions that lower odds for profitable trend-following strategies. The rise and fall of boxes add to this analysis, signaling when the quality of price action has changed enough to permit freer directional movement, higher or lower.

Traditional Darvas Box strategies require that market participants take exposure solely in the direction of the boxes, updating stops whenever price action crosses the top threshold. The original work includes fundamental filters that prefer growth plays with strong earnings, similar to the work of William O Neil and Investor’s Business Daily. However, the indicator’s usage has expanded naturally over the years into a purely technical form of market analysis.

High Low Bands

High Low Bands

High Low Bands (HLB) are generated from a series of moving averages calculated by evaluating price action over specified time periods, which are then shifted higher or lower by a fixed percentage of the median price. Indicator calculation requires setting the specific period and appropriate shift percentage. The ‘right’ settings are market specific and need to match volatility characteristics for the chosen security or trading venue.

The indicator applies triangular moving averages instead of simple or exponential moving averages. This is a double-smoothed average, or an average of an average, that irons out suspected outliers from the final calculation.  As a result, bands are smoother than similar indicators that track fast moving market activity and are less useful for many short-term trading strategies. However, HLB can generate extremely reliable high and low predictions in rangebound markets.

Mass Index

Mass Index

Developed by Donald Dorsey in the 1990s, Mass Index evaluates the range between the high and low of a security over a specified time period. Reversal signals with this indicator are generated when a range expands to a subjective extreme and then reverses into contraction. However, the technician may also need to examine momentum, volatility, and trend-following indicators to determine the overall trend direction that will be impacted by the reversal signal.

The classic setting uses a 9-day (or period) exponential moving average (EMA) of the range between the high and low price over the last 25 days. The initial output is then divided by a 9-day EMA of the 9-day EMA used for the initial calculation. In the original usage, an indicator value that surges above 27 and drops to 26.5 issues a reversal signal. However, in modern usage, the technician needs to identify signal levels that are appropriate for the currently traded markets and securities, which can differ greatly from Dorsey’s original observations.

Pivot Points

Pivot Points

Pivot Points determine range and trend intensity in different time frames. The first level of the indicator is calculated by adding the high and low of the current bar to the closing price of the prior bar, and dividing by three. Price action in the next bar is considered bullish when above the pivot point and bearish when below the pivot point. This observation has limited value so the calculation adds support and resistance levels, notated as S1, S2, R1, and R2, based upon projections from the pivot point value.

Price movement above support or below resistance signifies a strengthening uptrend or downtrend while reversals within support (S1 or S2) and resistance (R 1 or R2) boundaries define the quality of rangebound markets, at least within the time frame used in the price chart.  These five levels can also be used to identify appropriate trade entry levels, place stop losses and trailing stops, and to locate high odds trade exit levels.

Additional Range Indicators

Anchored VWAP – attempts to identify the average price of a security over a time period chosen by the technician.

ATR Bands – are drawn around the average true range indicator to identify potential turning points and whether price is engaged in an uptrend, downtrend, or a trading range.

ATR Trailing Stops – identifies optimized stop levels using multiples of average true range indicator output.

Detrended Price Oscillator – seeks to measure the length of price cycles from peak to peak or trough to trough.

Gopalakrishnan Range Index – quantifies price movement and asset volatility by studying the asset’s trading range over a specified time period.

High Minus Low – subtracts the daily (or bar) high from the daily (or bar) low to determine average price movement over a specified time period

Highest High Value – measures the highest high over a specified time period.

Lowest Low Value – measures the lowest low over a specified time period.

Median Price – measures the most common price over a specified time period.

True Range – displays a derivative of the trading range by removing the impact of gaps and volatility between price bars.

Vortex Indicator – separates uptrends and downtrends into two continuous lines that reveal relative bull and bear power over time.

VWAP – attempts to identify the average price for a security over the entire session.

The Complete Guide to Volume Indicators

Volume indicators are technical tools to evaluate a security’s bull and bear power. Most look specifically at buying vs. selling pressure to determine which side is in control of price action. Others attempt to identify emotions that are moving the security at a particular time. For example, exceptionally high volume compared to a moving average of volume can reveal euphoria or fear while much lower than average volume can reflect apathy or disinterest.

These indicators measure shares in the equity markets, contracts in the futures markets, and tick movements in the forex markets. All versions attempt to accomplish the same types of technical analysis. When a market rises on increased volume, it is considered to be under accumulation. Conversely, when a market falls on increased volume, it is considered to be under distribution.    In addition, a market rising on decreased volume generates a bearish divergence while a market falling on decreased volume generates a bullish divergence.

Forex market volume evaluates the degree of price movement within a certain period, rather than looking at individual buy and sell transactions. Forex traders often supplement their accumulation-distribution analysis by looking at open interest in the currency futures markets. Whether equity, contract, or pair, volume is used in conjunction with price action to confirm trend strength, reveal trend weakness, and confirm breakouts and breakdowns.

Accumulation-Distribution (A/D)

A/D

Accumulation-Distribution (A/D) is a cumulative volume indicator, meaning that each data point is added to the prior data point before it’s plotted on an indicator panel. As the name states, the indicator attempts to determine if a security is being accumulated (bought over time) or distributed (sold over time). The calculation measures the closing price in relation to the price bar’s range and multiplies the result by volume for that bar.

A/D indicator direction generates convergence and divergence relationships with price that assist in trade decision-making and risk management when used in conjunction with pattern analysis and other technical indicators. Rising price when A/D is falling generates a bearish divergence while falling price when A/D is falling generates a bullish divergence. The indicator also carves orderly patterns over time that look similar to price action, with channels, trendlines, and triangles assisting prediction.

On Balance Volume (OBV)

OBV

On Balance Volume (OBV) was created by Joseph Granville in 1963 and is now the most popular accumulation-distribution indicator. OBV generates a bullish divergence when price is falling and OBV is rising and a bearish divergence when price is rising and OBV is falling. The value of OBV at a particular time isn’t important but the relationship between current and prior OBV levels determines whether accumulation or distribution is keeping up with price action.

OBV plots a running total of a security’s buy and sell volume, seeking to determine if it is under accumulation (bought over time) or distribution (sold over time). The calculation has three primary components. First, if the current price bar is higher than the previous price bar, current OBV = previous bar’s OBV + current volume. Second, if the current price bar is lower than the previous price bar, current OBV = previous bar’s OBV – current volume. Third, if the current price bar = the previous price bar, current OBV = previous OBV.

Chaikin Money Flow (CMF)

CMF

Chaikin Money Flow (CMF) was created by Marc Chaikin in the early 1980s. The indicator measures accumulation and distribution of a security over time. This is an oscillator, with values ranging from +100 to -100 and a zero line that signifies neither accumulation nor distribution. As with other oscillators, CMF generates buy, sell, and confirmation signals through bullish and bearish convergences and divergences as well as crossovers through the zero line.

Chaikin applied a 21-period (one month) setting to the indicator but that element is now customizable in charting programs and has different implications, depending on the chosen period. According to the creator, money flow persistence over 6 to 9 months evaluates accumulation or distribution by major funds and institutions. Most traders don’t need that information but CMF also reveals short-term money flow convergence-divergence when viewed with shorter time frames and settings.

Volume Oscillator (VO)

VO

Volume Oscillator (VO) identifies accumulation and distribution by examining the relationship between two volume moving averages. A fast cycle moving average of 14 days or weeks is often used in conjunction with a slow cycle moving average of 28 days or weeks but settings are customizable. The calculation simply subtracts the slow MA from the fast MA and plots the result as a line or histogram. As with other oscillators, VO fluctuates across a zero line but has no fixed upper or lower values.

VO is non-directional and expected to turn higher in both uptrends and downtrends. It generates a bearish divergence when price is rising and VO is falling and a bullish divergence when price is falling and VO is falling. The indicator also has the power to identify overbought and oversold markets and to confirm breakouts and breakdowns. In addition, crossovers through the zero line may reveal important turning points or be used to confirm other technical indicators.

Balance of Power (BOP)

BoP

Balance of Power (BOP) measures the strength of buying and selling pressure. This oscillator is plotted in a panel with a central zero line and extremes at +1 and -1. Buyers are in control when the indicator is located above the zero line while sellers are in control when the indicator is located below the zero line. Readings near the zero line can indicate a reversal in trend or a rangebound market. Values near +1 signal an overbought market while values near -1 signal an oversold market.

BOP divides the distance between the open and close of the price bar by the distance between the high and low of the price bar. The initial result looks choppy and confusing so the calculation is then smoothed by a 14-period or other moving average. The distance above or below the zero line indicates the extremity of the positive or negative price change. It emits buy and sell signals through bullish and bearish divergences with price, as well as crossovers through the zero line.

Additional Volume Indicators

Klinger Volume Oscillator – looks at long and short-term money flow to confirm uptrends and downtrends.

Negative Volume Index – evaluates how rising and falling volume impact price movement over time.

On Balance Volume – calculates accumulation or distribution in a security over time. It generates a bullish divergence when price is falling and OBV is rising and a bearish divergence when price is rising and OBV is falling.

Positive Volume Index – evaluates how rising and falling volume impact price movement over time.

Projected Aggregate Volume – calculates the daily volume up to an intraday setting and projects total volume for the remainder of the session.

Projected Volume at Time – looks back at past sessions to project future volume over specified time periods.

Trade Volume Index – tracks correlation between price movement and volume levels to evaluate accumulation and distribution.

Twiggs Money Flow – applies a variation of Chaikin Money Flow to measure accumulation and distribution of a security over time.

Volume Chart – plots a volume histogram below each price bar.

Volume Oscillator – looks at accumulation and distribution by examining the relationship between two volume moving averages.

Volume Profile – displays the quantity of trading activity of a security at different price levels.

Volume Rate of Change – plots the percentage change of volume over a specified time period to determine if participation is rising or falling.

Volume Underlay – displays volume histograms in the same pane as price, rather than in a separate indicator pane.

The Complete Guide to Volatility Indicators

The volatility indicator is a technical tool that measures how far security stretches away from its mean price, higher and lower. It computes the dispersion of returns over time in a visual format that technicians use to gauge whether this mathematical input is increasing or decreasing. Low volatility generally refers to quiet price movement, with predictable short-term swings, while high volatility refers to noisy or dramatic price movement, with often wildly unpredictable short-term swings.

Volatility measures the degree to which price moves over time, generating non-directional information unless the data is plotted in specific visual formats. This technical element has a great impact on options pricing and market sentiment, with high volatility generating greater extremes in greed and fear. Constructed as an indicator, volatility plots a history of price movement that supplements trend, momentum, and range analysis.

Volatile instruments are considered to be more risky than non-volatile instruments. Volatility oscillates regularly between high and low states, offering a potential timing tool for traders and market timers. Specially, the lowest volatility over X periods is often a precursor for an imminent shift to high volatility that translates into trend movement and trading signals. Market lore outlines these classic dynamics, telling market players to ‘buy in mild times and sell in wild times’.

Bollinger Bands

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Bollinger Bands is the financial market’s best-known volatility indicator. Created by John Bollinger in the early 1980s, the indicator constructs three lines around price: a simple moving average acts as the middle band while equally-distanced upper and lower bands expand and contract in reaction to changing volatility. The 20-day or period SMA is the most common setting for the middle band but this value is customizable in the advanced charting program.

The calculation takes the standard deviation of the SMA, which is one way to calculate distance from the SMA over time, and applies the result to the upper and lower bands. Bands expand and contract over time in reaction to changing volatility levels. Constricted bands ‘squeeze’ price action between narrow boundaries, indicating low volatility while predicting a cycle shift to high volatility. The transition can elicit high odds entry and exit signals for many trading strategies.

Donchian Channels

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Donchian Channels construct upper, lower, and mid-range bands through examination of price extremes over the chosen time period. The highest price over the chosen period marks the high band while the lowest price over the chosen period marks the low band. The median band is constructed by subtracting the low band value from the high band value and dividing by two. The indicator is then used to investigate relationships between the current price and trading ranges over the chosen period.

As with Bollinger Bands, 20-days or periods is the most common Donchian Channel setting.  A top band that moves higher when price approaches (or a bottom band that moves lower) signals ease of movement that facilitates trend development. Conversely, a band that remains horizontal when price approaches identifies support or resistance that raises odds for a reversal and return to the median band. Bollinger Bands differ from Donchian Channels, applying moving averages that lower the impact of high and low outliers during lookback periods.

Keltner Channel

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Keltner Channels place bands around developing price in order to gauge volatility and assist directional prediction. Upper and lower bands are calculated as a multiple of average true range (ATR) and are plotted above and below an exponential moving average (EMA). Both the EMA and ATR multiplier can be customized but 50 and 5 are common settings. Price lifting into the upper band denotes strength while price dropping into the lower band denotes weakness.

These bands represent support and resistance regardless of inclination, with piercing through bands generating overbought and oversold trading signals in addition to marking an acceleration of the trend. Horizontal bands exert greater support or resistance than bands ticking higher or lower.  Price falling into a rising band generates a bullish divergence while price rising into a falling band generates a bearish divergence.

Ichimoku Clouds

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Ichimoku Clouds, developed by Goichi Hosada in the late 1960s, plots multiple moving averages above and below price in the form of shaded areas that are called bullish or bearish ‘clouds’. Five calculations are applied to construct the indicator, generating a cloud that represents the difference between two of the lines. Price above a cloud signals an uptrend while price below a cloud signals a downtrend. A bullish price swing into a cloud denotes resistance while a bearish price swing into a cloud denotes support.

Clouds also tick higher or lower over time, adding to the indicator’s versatility. Trend signals are expected to be stronger and more reliable when price is moving higher above a cloud or lower below a cloud. The two cloud lines are called ‘Span A’ and ‘Span B’. The cloud is colored green when Span A is above Span B and colored red when Span A is below Span B.  Price above a red cloud signals a bullish divergence while price below a green cloud signals a bearish divergence.

Historical Volatility

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Historical Volatility is plotted in a separate pane, unlike most volatility indicators. It measures the distance that price travels away from a central mean over the chosen time period. Standard deviation is often used to calculate the indicator but variations utilize other measurements. Risk increases when the indicator rises and decreases when it falls. It is non-directional, meaning that rising or falling volatility doesn’t specifically favor buying or selling strategies.

The original indicator applied a 10-period and 252-day setting to measure volatility over a year (252 = average number of trading days in a year). The technician now customizes these inputs as well as standard deviation (SD). It’s best to ‘form fit’ the calculation to a security because average volatility is expected to differ between different types of instruments and markets. Interpretation of historical volatility compares current levels with prior levels, looking for high and low extremes that may impact profits and losses. It can also be useful to compare values across highly correlated instruments to uncover ‘typical’ value and hidden divergences.

Additional Volatility Indicators

Beta – measures a security’s volatility compared to the broad market or another security.

Bollinger %b – translates the distance between price and Bollinger Bands into an oscillator plot.

Bollinger Bandwidth – calculates the percentage distance between upper and lower Bollinger Bands, seeking to identify high odds turning points.

Choppiness Index – measures whether a market is engaged in a trend or a trading range.

Chaikin Volatility – generates an oscillator that applies Moving Average Convergence Divergence (MACD) to accumulation-distribution rather than price. A crossover above a zero line indicates strength and accumulation while a crossover below a zero line indicates weakness and distribution.

Donchian Channel – constructs upper, lower, and mid-range bands through examination of price extremes over a chosen time period. The highest price over the chosen period marks the high band while the lowest price over the chosen period marks the low band.

Donchian Width – measures the price difference between the high and low bands of the Donchian Channel.

Fractal Chaos Bands – draws bands around price action, with the slope determining whether or not the security is trending or flat.

Moving Average Deviation – measures volatility by examining how an asset’s price has deviated from the selected moving average over time.

Moving Average Envelope – plots a band over price, with top and bottom extremes calculated as a pre-chosen percentage above and below a moving average.

Prime Number Bands – identifies the highest and lowest prime numbers in a trading range over a given period and plots the output as a band across price.

Relative Volatility – is a variation of Relative Strength Index (RSI) that measures the direction of volatility over the specified time period, using standard deviation calculations.

Standard Deviation – examines how far price stretches away from a central mean price over time.

STARC Bands – also known as Stoller Average Range Channel Bands, are plotted above and below a simple moving average, highlighting extreme levels that can elicit potent buy or sell signals.

Ulcer Index – predicts the drawdown, depth, and duration of asset declines through examination of highs and lows over time.

What Is Backwardation?

Backwardation of commodities hit the steepest level in nearly 15 years this week, driven by a worldwide shortage of raw materials. Massive stimulus, low-interest rates, and the light at the end of the pandemic tunnel have driven this surge in demand, which is coinciding with the first commodity uptrend in a decade. Metals, agriculture, and the energy markets have all been moved by this historic impulse, which ironically predicts lower commodity prices in the coming months.

Understanding backwardation requires learning three key terms. First, the spot price denotes the current commodity price. Theoretically, anyone can walk into a commodity store and walk out with that commodity at the spot price, which changes over time due to the market forces of supply and demand.  Second, the futures contract denotes an agreement to buy or sell the commodity at a specified delivery date in the future, with contract maturity as short as a month or up to 10 years in the future.

Third, the futures curve illustrates the relationship between the spot price and futures prices. A futures curve is in backwardation when the slope is declining, predicting the commodity price will be lower ‘n-months’ into the future. Conversely, a futures curve is in contango when the slope is rising, predicting the commodity price will be higher ‘n-months’ into the future. This information is so actionable it can be used to gauge market sentiment, in addition to pricing.

What is Backwardation?

Simply stated, a commodity futures contract and spot market enter backwardation when shorter-term pricing is higher than longer-term pricing. As in 2021, this phenomenon can reflect intense short-term scarcity that forces suppliers of these commodities to raise prices at a rapid rate. This is significant because futures with longer maturities have to include inventory carrying and storage costs in addition to fundamentals and market-driven demand estimates.

Backwardation can be short-term (bottlenecks that will soon be eased) or long-term (supply and demand imbalances that persist for months or years). In the current phenomenon, futures traders expect that short-term scarcity will ease as production and supply ramp-up, putting a dampening effect on longer-dated contracts. However, backwardation can also end with futures ramping up to higher prices to match spot prices, generating a nearly perfect storm for rising inflation.

Decade-long cycles drive commodity prices and backwardation may set off warning signs that demand has overtaken supply on a semi-permanent basis, set to generate significant inflationary pressure. However, the curve’s downslope indicates that expectations remain within boundaries, at least in the short-term, reacting to balanced conditions. As a result, those tasked with rate analysis have to watch the futures curve, looking for signs of stress that can translate into higher prices.

Traders seek to profit from backwardation by selling short at the spot price and buying back at the futures contract price. In theory, the practice will eventually restore normal conditions, inducing the spot price to fall until it is lower than or equal to longer-dated securities. Expiration can help or hurt this process, as illustrated during the 24 hour period ahead of April 2020 expiration, when the expiring WTI crude oil contract fell below minus $40 due to a massive short-term exodus.

Contango vs. Backwardation

Contango, also known as forwardation, is the opposite of backwardation.  This market condition occurs when each successively longer-dated futures contract costs more than the next shorter-dated futures contract, generating an upward slope.  For example, when a futures contract rotates on a monthly bases, the price of the July contract will be higher than the price of the June contract, which will be higher than the May contract, and so on. Futures contracts can shift rapidly between contango and backwardation, or get stuck in one state that persists for years.

It is assumed that spot prices will rise to meet futures prices when contango is in effect. As a result, market players will sell short higher-priced futures contracts and attempt to buy back the exposure through spot prices, pocketing the difference. This technique has a self-perpetrating effect, i.e. generating even greater demand that drives the spot price higher until it matches or exceeds futures prices, ending the contango. The expiration date affects this process, capable of generating high volatility when market forces are in conflict.

Interpreting Backwardation and Contango

Traders engaged in backwardation and contango strategies can get trapped when the spot/futures relationship doesn’t follow expectations. As noted above, both imbalances can result from short-term influences or long-term paradigm shifts. In 2021, we’re coming out of a pandemic that disrupted supply chains and forced factories to shut down but we don’t know if supply can ramp up quickly enough to keep futures prices lower than spot prices. We also don’t know if we’re facing a short-term bottleneck or multiyear phenomenon.

Commodity traders keep close watch on other markets for clues about the persistence of backwardation and contango. The bond market is especially useful in this endeavor because it reflects the investment community’s consensus about interest rates along the yield curve. At the moment, this group of ‘traders’ is more bullish about interest rates than the futures crowd, who have chosen  by consensus to keep longer-term pricing at lower levels than spot pricing.

Finally, backwardation is considered to be a leading indicator, predicting that spot prices will be lower in the future. This prognosis works well if suppliers can boost production quickly and bring supply/demand back into balance, but bullish and bearish signals fail when macro events overtake short-term conditions.  Once again, cross-market verification is an absolute necessity to increase futures curve signal reliability and to reduce whipsaws.

Summary

Backwardation indicates the futures curve is falling, with spot markets and short-term futures contracts priced higher than longer-dated contracts. Conversely, contango indicates the futures curve is rising, with progressively higher prices between spot markets and longer-dated futures contracts.  Both market conditions are normal but can sometimes signal significant long-term shifts in market behavior.

Federal Reserve Inflation Fighting Tools

Introduction

In the past, inflationary pressures were feared by financial market participants and this time is no different as some economists are worried that the Fed’s commitment to low rates will foster inflation. However, Powell countered these fears by saying he’s “very mindful” of the lessons from runaway inflation in the 1960s and ‘70s, but believes this situation is different.

“We’re very mindful and I think it’s a constructive thing for people to point out potential risks. I always want to hear that,” he said. “But I do think it’s more likely that what happens in the next year or so is going to amount to prices moving up but not staying up and certainly not staying up to the point where they would move inflation expectations materially above 2%.”

Powell further added that the Fed considers 2% inflation a healthy level for the economy while also giving the central bank breathing room for policy. Should inflation get out of control, Fed officials believe they have the tools to control it.

This article will explore some of the tools the Fed has at its disposal to combat rapidly rising inflation should it pose a threat to the economy.

Why Does the Federal Reserve Aim for Inflation of 2 Percent over the Longer Run?

According to Federal Reserve documents, the Federal Open Market Committee (FOMC) judges that inflation of 2 percent over the longer run, as measured by the annual change in the price index for personal consumption expenditures (PCE), is most consistent with the Federal Reserve’s mandate for maximum employment and price stability.

When households and businesses can reasonably expect inflation to remain low and stable, they are able to make sound decisions regarding saving, borrowing, and investment, which contributes to a well-functioning economy.

For many years, inflation in the United States has run below the Federal Reserve’s 2 percent goal. It is understandable that higher prices for essential items, such as food, gasoline, and shelter, add to the burdens faced by many families, especially those struggling with lost jobs and incomes.

At the same time, inflation that is too low can weaken the economy. When inflation runs well below its desired level, households and businesses will come to expect this over time, pushing expectations for inflation in the future below the Federal Reserve’s longer-run inflation goal. This can pull actual inflation even lower, resulting in a cycle of ever-lower inflation and inflation expectations.

If inflation expectations fall, interest rates would decline too. In turn, there would be less room to cut interest rates to boost employment during an economic downturn. Evidence from around the world suggests that once this problem sets in, it can be very difficult to overcome. To address this challenge, following periods when inflation has been running persistently below 2 percent, appropriate monetary policy will likely aim to achieve inflation modestly above 2 percent for some time. By seeking inflation that averages 2 percent over time. The FOMC will help to ensure longer-run inflation expectations remain well-anchored at 2 percent.

Furthermore, the Fed has repeatedly said it will keep short-term rates anchored near zero and continue its monthly bond-buying program until it sees not only a low unemployment rate but also a jobs recovery that is “inclusive” across income, gender, and racial lines.

Powell believes the Fed has the Tools to Manage Inflation

Generally speaking, we now know why the Federal Reserve wants to see inflation at 2%, but Powell and his policymakers want to allow inflation to move above this mandated level until he is confident the economy has fully recovered from the shock of the pandemic. This is what spooked investors in March, but they have since calmed down with stocks hitting record highs.

Powell’s confidence in his ability to fight inflation may be responsible for the sudden surge in demand in equity prices and the market’s seemingly acceptance of rising Treasury yields.

As inflation rises throughout 2021, there will be pockets of volatility because some investors will believe the Fed is behind the curve by leaving its current policy of low-interest rates and aggressive bond-buying unchanged.

However, should it suspect that inflation is getting out of hand, it usually turns to open market operations, the federal funds rate, and the discount rate to stem the inflationary surge.

Open Market Operations

The Fed’s first line of defense against runaway inflation is typically open market operations. In implementing this policy, it sells securities like Treasury notes to member banks. The move reduces bank capital, giving them less to lend. Essentially, it removes money from the economy. As a result, banks raise interest rates and that slows economic growth, dampening inflation.

Federal Funds Rate

The Federal Funds Rate is probably the most well-known of the Fed’s tools. It’s also part of its open market operations. It’s the interest rate banks charge for overnight loans they make to each other. This is a very easy tool for the Fed to manipulate. Its purpose to pull money from the economy and slow inflation.

Discount Rate

This is the interest rate the central bank charges member banks to borrow funds from the Fed’s discount window. Once again the process of raising rates removes money from circulation, thereby reducing the money available for loans and mortgages. This helps slow down economic growth or inflation.

Reserve Requirement

The most powerful tool at the Fed’s disposal is the Reserve Requirement. The Fed eliminated the reserve requirement, effective March 26, 2020. This occurred at the beginning of the COVID-19 pandemic. The last thing the Fed wanted during that critical time was for banks to be pulling money out of the economy.

Once the economy regains its footing and as inflation hovers at or over 2%, the Fed has the power to reinforce the reserve requirement rule that will force member banks to hold more capital in reserve. This is another tool that removes money from the economy, and thus trimming inflation.

Summary

Federal Reserve Chair Jerome Powell and other Fed policymakers recently voted to keep short-term borrowing rates steady near zero, while continuing an asset purchase program in which the central bank buys at least $120 billion of bonds a month. Despite this move, inflation continues to run below 2 percent.

Treasury yields have been rising rapidly for weeks because the market is expecting inflation to heat up, which could force the Fed to change policy sooner than expected. The Fed however says that won’t be enough to change policy that seeks inflation above 2% for a period of time if it helps to achieve full and inclusive employment.

While some economists fear the Fed is inviting risks to the economy by allowing inflation to run above the 2% mandate, Fed policymakers believe they have the weapons to control inflation and to make changes quickly enough to push inflation back to or below 2%.

What are NFTs? Everything you Need to Know About Non-Fungible Tokens

NFT definition and advantages

These are essentially cryptographic assets on blockchain with unique identification codes and metadata. These codes and metadata make them individual and unique.

One characteristic of an NFT, therefore, is that it cannot be copied or duplicated.

An example of an NFT from the real world would be a piece of art, such as the Mona Lisa. While Leonardo Da Vinci is known for numerous pieces of art, there is only one Mona Lisa.

While you can trade one Litecoin for another, you can’t trade one Mona Lisa for another Mona Lisa.

For the world of blockchain, a key advantage of NFTs is that they cannot be copied. And so, unlike the Mona Lisa, NFTs can be bought and sold without the possibility of fraud.

In the art world, a lot of money is spent to authenticate pieces of work before being sold. An NFT does not need middlemen to ensure authenticity.

What is fungible vs. non-fungible?

According to the Cambridge Dictionary, fungible or fungibility means simply interchangeable. “This is a characteristic of most financial instruments and market assets.”

By contrast, non-fungible property/assets/funds are not easy to exchange or mix with other similar goods or assets.

In other words, stocks, Certificates of Deposits, Cryptos, etc. are fungible assets.

An example of a non-fungible asset would be land or even diamonds. While land is a simple one to classify, each individual diamond is also unique. Each diamond has a different cut, size, grade, and so forth and therefore can’t be interchangeable with another diamond.

How Are NFTs Created?

NFTs are very simple to create, unlike blockchains and cryptocurrencies.

A number of NFT market places allow users to freely create an NFT and no programming knowledge is required.

Market places that currently allow users to freely create NFTs include OpenSea, Raible, or Mintable.

Creating art NFTs is particularly popular and these market places cater for just that.

How do NFTs work?

NFTs are digital tokens that are on a blockchain ledger. Once created, the market then trades the NFTs across market places.

Once an NFT is created, there is a proof-of-ownership that must be stored securely in an NFT wallet.

It is the proof-of-ownership that is ultimately the tradeable and non-fungible asset.

Once created, the blockchain ledger records the NFTs and their unique identifying codes. The blockchain ledger then also records each sale and resale and ownership.

This not only prevents the copying of an NFT but also removes fraudulent claims of ownership or even claims over creation.

Why do NFTs have value?

This is simply a case of supply and demand. The key here is the supply side that drives up the value of NFTs. Since there is only one individual asset, high demand can lead to significant increases in value.

Taking the Mona Lisa as an example, experts estimate the value of the Mona Lisa at more than $800m. Had Leonardo da Vinci painted numerous Mona Lisa paintings to exactly the same quality, these would be fungible. Their value would also be significantly less than the single painting thought to be edging towards $1 billion.

As the NFT market expands, the number of NFTs are likely to increase significantly. At this stage, demand will likely become the key price dictator.

Market appetite will continue to dictate value. A unique NFT of interest versus one of little interest to collectors and investors will vary significantly in price.

For example, Twitter CEO Jack Dorsey recently tweeted a link to a tokenized version of his first-ever written tweet.

Bids have reportedly reached in excess of $2.5 million. Other Jack Dorsey tweets are unlikely to have a collectible value, however, even though each tweet is unique.

How do I buy or trade NFTs?

For those looking to buy or trade NFTs, identifying the right marketplace is the first step.

There are numerous market places at present that cater to different areas of the collectible world.

For instance, NBA Top Shot is a marketplace for those looking to buy video highlights. Cryptoslam! is a site that lists the largest market places by sales volume for those looking to enter the NFT space.

There is also Sorare, which is a fantasy soccer marketplace, where you can manage, and buy and sell virtual teams. At the time of writing, Sorare ranked fifth on the all-time sales list, with $24.41m in total sales.

CryptoPunks ranks 2nd on the all-time sales volume list has 10,000 uniquely generated characters. Each can be officially owned and proof of ownership is logged on the Ethereum blockchain.

While there are many market places, those wanting to purchase an NFT will need an NFT wallet in order to store any purchased NFTs.

There are a number of NFT wallet providers in the marketplace. As with cryptos, NFT holders must store-purchased NFTs securely. Hard wallets would be more secure, protecting NFT holders from hackers.

When it comes to purchasing NFTs, some market places support purchases with a credit card. Others, however, require purchases with Ethereum.

For Ethereum purchases you will need to fund your NFT marketplace account with Ethereum to proceed.

To purchase your NFT, most market places sell NFTs in an auction. You simply need to place your bid and wait until the conclusion of the auction.

If your bid was successful, your account would be debited and your NFT wallet credited with your newly purchased NFT.

What are the most expensive NFTs?

Of late, NFT market news has flooded the crypto and mainstream newswires. With tech-savvy investors looking to be first to market, there have been a number of eye-watering bids for NFTs.

The largest NFT market places ranked by sales volume (all-time) at the time of writing include:

  • NBA Top Shot: Total sales $386.55m.
  • CryptoPunks: Total sales $161.32m.
  • Hashmarks: Total sales $43.71m.

For a full list of the largest marketplace NFT sales over the last 24-hours, 7-days, 30-days, and all-time, visit Cryptoslam.io.

Here you can view sales over a specified time period, the price change over the specified time period, and the number of buyers and transactions.

Taking a look at NBA Top Shot, there have been a total of 2.46m transactions that have led to total sales of $385.56m.

What is NBA Top Shot?

An NBA-Dapper Labs collaboration, delivering an on-line platform for trading virtual basketball cards.

These all exist on the blockchain making them unique and impossible to copy. More importantly, for some, authentication is immediate.

With the added advantage of virtual tech, it’s not just virtual basketball cards that are drawing attention.

There are also “moments” that are selling for 6 figure sums. Moments are video clips of the more popular players from the NBA.

One LeBron moment reportedly sold for $208,000…

Looking at the numbers for the broader NFT market, the last 30-days has been headline-grabbing.

From the $385.56m total sales across NBA Top Shot, more than $300m came from the last 30-days.

Looking at individual NFT sales

Christie’s Auction sold Beeple NFT for a whopping $69.3m. This is by far the largest sale to date.

The next 4 largest NFT were the sales of CryptoPunk characters. Sale prices ranged from $7.6m for CryptoPunk #3100 to $1.3m for CryptoPunk #4156.