More on Economics & Investing
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Adam Hayes
3 years ago
Bernard Lawrence "Bernie" Madoff, the largest Ponzi scheme in history
Madoff who?
Bernie Madoff ran the largest Ponzi scheme in history, defrauding thousands of investors over at least 17 years, and possibly longer. He pioneered electronic trading and chaired Nasdaq in the 1990s. On April 14, 2021, he died while serving a 150-year sentence for money laundering, securities fraud, and other crimes.
Understanding Madoff
Madoff claimed to generate large, steady returns through a trading strategy called split-strike conversion, but he simply deposited client funds into a single bank account and paid out existing clients. He funded redemptions by attracting new investors and their capital, but the market crashed in late 2008. He confessed to his sons, who worked at his firm, on Dec. 10, 2008. Next day, they turned him in. The fund reported $64.8 billion in client assets.
Madoff pleaded guilty to 11 federal felony counts, including securities fraud, wire fraud, mail fraud, perjury, and money laundering. Ponzi scheme became a symbol of Wall Street's greed and dishonesty before the financial crisis. Madoff was sentenced to 150 years in prison and ordered to forfeit $170 billion, but no other Wall Street figures faced legal ramifications.
Bernie Madoff's Brief Biography
Bernie Madoff was born in Queens, New York, on April 29, 1938. He began dating Ruth (née Alpern) when they were teenagers. Madoff told a journalist by phone from prison that his father's sporting goods store went bankrupt during the Korean War: "You watch your father, who you idolize, build a big business and then lose everything." Madoff was determined to achieve "lasting success" like his father "whatever it took," but his career had ups and downs.
Early Madoff investments
At 22, he started Bernard L. Madoff Investment Securities LLC. First, he traded penny stocks with $5,000 he earned installing sprinklers and as a lifeguard. Family and friends soon invested with him. Madoff's bets soured after the "Kennedy Slide" in 1962, and his father-in-law had to bail him out.
Madoff felt he wasn't part of the Wall Street in-crowd. "We weren't NYSE members," he told Fishman. "It's obvious." According to Madoff, he was a scrappy market maker. "I was happy to take the crumbs," he told Fishman, citing a client who wanted to sell eight bonds; a bigger firm would turn it down.
Recognition
Success came when he and his brother Peter built electronic trading capabilities, or "artificial intelligence," that attracted massive order flow and provided market insights. "I had all these major banks coming down, entertaining me," Madoff told Fishman. "It was mind-bending."
By the late 1980s, he and four other Wall Street mainstays processed half of the NYSE's order flow. Controversially, he paid for much of it, and by the late 1980s, Madoff was making in the vicinity of $100 million a year. He was Nasdaq chairman from 1990 to 1993.
Madoff's Ponzi scheme
It is not certain exactly when Madoff's Ponzi scheme began. He testified in court that it began in 1991, but his account manager, Frank DiPascali, had been at the firm since 1975.
Why Madoff did the scheme is unclear. "I had enough money to support my family's lifestyle. "I don't know why," he told Fishman." Madoff could have won Wall Street's respect as a market maker and electronic trading pioneer.
Madoff told Fishman he wasn't solely responsible for the fraud. "I let myself be talked into something, and that's my fault," he said, without saying who convinced him. "I thought I could escape eventually. I thought it'd be quick, but I couldn't."
Carl Shapiro, Jeffry Picower, Stanley Chais, and Norm Levy have been linked to Bernard L. Madoff Investment Securities LLC for years. Madoff's scheme made these men hundreds of millions of dollars in the 1960s and 1970s.
Madoff told Fishman, "Everyone was greedy, everyone wanted to go on." He says the Big Four and others who pumped client funds to him, outsourcing their asset management, must have suspected his returns or should have. "How can you make 15%-18% when everyone else is making less?" said Madoff.
How Madoff Got Away with It for So Long
Madoff's high returns made clients look the other way. He deposited their money in a Chase Manhattan Bank account, which merged to become JPMorgan Chase & Co. in 2000. The bank may have made $483 million from those deposits, so it didn't investigate.
When clients redeemed their investments, Madoff funded the payouts with new capital he attracted by promising unbelievable returns and earning his victims' trust. Madoff created an image of exclusivity by turning away clients. This model let half of Madoff's investors profit. These investors must pay into a victims' fund for defrauded investors.
Madoff wooed investors with his philanthropy. He defrauded nonprofits, including the Elie Wiesel Foundation for Peace and Hadassah. He approached congregants through his friendship with J. Ezra Merkin, a synagogue officer. Madoff allegedly stole $1 billion to $2 billion from his investors.
Investors believed Madoff for several reasons:
- His public portfolio seemed to be blue-chip stocks.
- His returns were high (10-20%) but consistent and not outlandish. In a 1992 interview with Madoff, the Wall Street Journal reported: "[Madoff] insists the returns were nothing special, given that the S&P 500-stock index returned 16.3% annually from 1982 to 1992. 'I'd be surprised if anyone thought matching the S&P over 10 years was remarkable,' he says.
- "He said he was using a split-strike collar strategy. A collar protects underlying shares by purchasing an out-of-the-money put option.
SEC inquiry
The Securities and Exchange Commission had been investigating Madoff and his securities firm since 1999, which frustrated many after he was prosecuted because they felt the biggest damage could have been prevented if the initial investigations had been rigorous enough.
Harry Markopolos was a whistleblower. In 1999, he figured Madoff must be lying in an afternoon. The SEC ignored his first Madoff complaint in 2000.
Markopolos wrote to the SEC in 2005: "The largest Ponzi scheme is Madoff Securities. This case has no SEC reward, so I'm turning it in because it's the right thing to do."
Many believed the SEC's initial investigations could have prevented Madoff's worst damage.
Markopolos found irregularities using a "Mosaic Method." Madoff's firm claimed to be profitable even when the S&P fell, which made no mathematical sense given what he was investing in. Markopolos said Madoff Securities' "undisclosed commissions" were the biggest red flag (1 percent of the total plus 20 percent of the profits).
Markopolos concluded that "investors don't know Bernie Madoff manages their money." Markopolos learned Madoff was applying for large loans from European banks (seemingly unnecessary if Madoff's returns were high).
The regulator asked Madoff for trading account documentation in 2005, after he nearly went bankrupt due to redemptions. The SEC drafted letters to two of the firms on his six-page list but didn't send them. Diana Henriques, author of "The Wizard of Lies: Bernie Madoff and the Death of Trust," documents the episode.
In 2008, the SEC was criticized for its slow response to Madoff's fraud.
Confession, sentencing of Bernie Madoff
Bernard L. Madoff Investment Securities LLC reported 5.6% year-to-date returns in November 2008; the S&P 500 fell 39%. As the selling continued, Madoff couldn't keep up with redemption requests, and on Dec. 10, he confessed to his sons Mark and Andy, who worked at his firm. "After I told them, they left, went to a lawyer, who told them to turn in their father, and I never saw them again. 2008-12-11: Bernie Madoff arrested.
Madoff insists he acted alone, but several of his colleagues were jailed. Mark Madoff died two years after his father's fraud was exposed. Madoff's investors committed suicide. Andy Madoff died of cancer in 2014.
2009 saw Madoff's 150-year prison sentence and $170 billion forfeiture. Marshals sold his three homes and yacht. Prisoner 61727-054 at Butner Federal Correctional Institution in North Carolina.
Madoff's lawyers requested early release on February 5, 2020, claiming he has a terminal kidney disease that may kill him in 18 months. Ten years have passed since Madoff's sentencing.
Bernie Madoff's Ponzi scheme aftermath
The paper trail of victims' claims shows Madoff's complexity and size. Documents show Madoff's scam began in the 1960s. His final account statements show $47 billion in "profit" from fake trades and shady accounting.
Thousands of investors lost their life savings, and multiple stories detail their harrowing loss.
Irving Picard, a New York lawyer overseeing Madoff's bankruptcy, has helped investors. By December 2018, Picard had recovered $13.3 billion from Ponzi scheme profiteers.
A Madoff Victim Fund (MVF) was created in 2013 to help compensate Madoff's victims, but the DOJ didn't start paying out the $4 billion until late 2017. Richard Breeden, a former SEC chair who oversees the fund, said thousands of claims were from "indirect investors"
Breeden and his team had to reject many claims because they weren't direct victims. Breeden said he based most of his decisions on one simple rule: Did the person invest more than they withdrew? Breeden estimated 11,000 "feeder" investors.
Breeden wrote in a November 2018 update for the Madoff Victim Fund, "We've paid over 27,300 victims 56.65% of their losses, with thousands more to come." In December 2018, 37,011 Madoff victims in the U.S. and around the world received over $2.7 billion. Breeden said the fund expected to make "at least one more significant distribution in 2019"
This post is a summary. Read full article here
Sofien Kaabar, CFA
3 years ago
How to Make a Trading Heatmap
Python Heatmap Technical Indicator
Heatmaps provide an instant overview. They can be used with correlations or to predict reactions or confirm the trend in trading. This article covers RSI heatmap creation.
The Market System
Market regime:
Bullish trend: The market tends to make higher highs, which indicates that the overall trend is upward.
Sideways: The market tends to fluctuate while staying within predetermined zones.
Bearish trend: The market has the propensity to make lower lows, indicating that the overall trend is downward.
Most tools detect the trend, but we cannot predict the next state. The best way to solve this problem is to assume the current state will continue and trade any reactions, preferably in the trend.
If the EURUSD is above its moving average and making higher highs, a trend-following strategy would be to wait for dips before buying and assuming the bullish trend will continue.
Indicator of Relative Strength
J. Welles Wilder Jr. introduced the RSI, a popular and versatile technical indicator. Used as a contrarian indicator to exploit extreme reactions. Calculating the default RSI usually involves these steps:
Determine the difference between the closing prices from the prior ones.
Distinguish between the positive and negative net changes.
Create a smoothed moving average for both the absolute values of the positive net changes and the negative net changes.
Take the difference between the smoothed positive and negative changes. The Relative Strength RS will be the name we use to describe this calculation.
To obtain the RSI, use the normalization formula shown below for each time step.
The 13-period RSI and black GBPUSD hourly values are shown above. RSI bounces near 25 and pauses around 75. Python requires a four-column OHLC array for RSI coding.
import numpy as np
def add_column(data, times):
for i in range(1, times + 1):
new = np.zeros((len(data), 1), dtype = float)
data = np.append(data, new, axis = 1)
return data
def delete_column(data, index, times):
for i in range(1, times + 1):
data = np.delete(data, index, axis = 1)
return data
def delete_row(data, number):
data = data[number:, ]
return data
def ma(data, lookback, close, position):
data = add_column(data, 1)
for i in range(len(data)):
try:
data[i, position] = (data[i - lookback + 1:i + 1, close].mean())
except IndexError:
pass
data = delete_row(data, lookback)
return data
def smoothed_ma(data, alpha, lookback, close, position):
lookback = (2 * lookback) - 1
alpha = alpha / (lookback + 1.0)
beta = 1 - alpha
data = ma(data, lookback, close, position)
data[lookback + 1, position] = (data[lookback + 1, close] * alpha) + (data[lookback, position] * beta)
for i in range(lookback + 2, len(data)):
try:
data[i, position] = (data[i, close] * alpha) + (data[i - 1, position] * beta)
except IndexError:
pass
return data
def rsi(data, lookback, close, position):
data = add_column(data, 5)
for i in range(len(data)):
data[i, position] = data[i, close] - data[i - 1, close]
for i in range(len(data)):
if data[i, position] > 0:
data[i, position + 1] = data[i, position]
elif data[i, position] < 0:
data[i, position + 2] = abs(data[i, position])
data = smoothed_ma(data, 2, lookback, position + 1, position + 3)
data = smoothed_ma(data, 2, lookback, position + 2, position + 4)
data[:, position + 5] = data[:, position + 3] / data[:, position + 4]
data[:, position + 6] = (100 - (100 / (1 + data[:, position + 5])))
data = delete_column(data, position, 6)
data = delete_row(data, lookback)
return dataMake sure to focus on the concepts and not the code. You can find the codes of most of my strategies in my books. The most important thing is to comprehend the techniques and strategies.
My weekly market sentiment report uses complex and simple models to understand the current positioning and predict the future direction of several major markets. Check out the report here:
Using the Heatmap to Find the Trend
RSI trend detection is easy but useless. Bullish and bearish regimes are in effect when the RSI is above or below 50, respectively. Tracing a vertical colored line creates the conditions below. How:
When the RSI is higher than 50, a green vertical line is drawn.
When the RSI is lower than 50, a red vertical line is drawn.
Zooming out yields a basic heatmap, as shown below.
Plot code:
def indicator_plot(data, second_panel, window = 250):
fig, ax = plt.subplots(2, figsize = (10, 5))
sample = data[-window:, ]
for i in range(len(sample)):
ax[0].vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1)
if sample[i, 3] > sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5)
if sample[i, 3] < sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
if sample[i, 3] == sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
ax[0].grid()
for i in range(len(sample)):
if sample[i, second_panel] > 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5)
if sample[i, second_panel] < 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5)
ax[1].grid()
indicator_plot(my_data, 4, window = 500)
Call RSI on your OHLC array's fifth column. 4. Adjusting lookback parameters reduces lag and false signals. Other indicators and conditions are possible.
Another suggestion is to develop an RSI Heatmap for Extreme Conditions.
Contrarian indicator RSI. The following rules apply:
Whenever the RSI is approaching the upper values, the color approaches red.
The color tends toward green whenever the RSI is getting close to the lower values.
Zooming out yields a basic heatmap, as shown below.
Plot code:
import matplotlib.pyplot as plt
def indicator_plot(data, second_panel, window = 250):
fig, ax = plt.subplots(2, figsize = (10, 5))
sample = data[-window:, ]
for i in range(len(sample)):
ax[0].vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1)
if sample[i, 3] > sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5)
if sample[i, 3] < sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
if sample[i, 3] == sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
ax[0].grid()
for i in range(len(sample)):
if sample[i, second_panel] > 90:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5)
if sample[i, second_panel] > 80 and sample[i, second_panel] < 90:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'darkred', linewidth = 1.5)
if sample[i, second_panel] > 70 and sample[i, second_panel] < 80:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'maroon', linewidth = 1.5)
if sample[i, second_panel] > 60 and sample[i, second_panel] < 70:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'firebrick', linewidth = 1.5)
if sample[i, second_panel] > 50 and sample[i, second_panel] < 60:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5)
if sample[i, second_panel] > 40 and sample[i, second_panel] < 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5)
if sample[i, second_panel] > 30 and sample[i, second_panel] < 40:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'lightgreen', linewidth = 1.5)
if sample[i, second_panel] > 20 and sample[i, second_panel] < 30:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'limegreen', linewidth = 1.5)
if sample[i, second_panel] > 10 and sample[i, second_panel] < 20:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'seagreen', linewidth = 1.5)
if sample[i, second_panel] > 0 and sample[i, second_panel] < 10:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5)
ax[1].grid()
indicator_plot(my_data, 4, window = 500)
Dark green and red areas indicate imminent bullish and bearish reactions, respectively. RSI around 50 is grey.
Summary
To conclude, my goal is to contribute to objective technical analysis, which promotes more transparent methods and strategies that must be back-tested before implementation.
Technical analysis will lose its reputation as subjective and unscientific.
When you find a trading strategy or technique, follow these steps:
Put emotions aside and adopt a critical mindset.
Test it in the past under conditions and simulations taken from real life.
Try optimizing it and performing a forward test if you find any potential.
Transaction costs and any slippage simulation should always be included in your tests.
Risk management and position sizing should always be considered in your tests.
After checking the above, monitor the strategy because market dynamics may change and make it unprofitable.
Desiree Peralta
2 years ago
How to Use the 2023 Recession to Grow Your Wealth Exponentially
This season's three best money moves.
“Millionaires are made in recessions.” — Time Capital
We're in a serious downturn, whether or not we're in a recession.
97% of business owners are decreasing costs by more than 10%, and all markets are down 30%.
If you know what you're doing and analyze the markets correctly, this is your chance to become a millionaire.
In any recession, there are always excellent possibilities to seize. Real estate, crypto, stocks, enterprises, etc.
What you do with your money could influence your future riches.
This article analyzes the three key markets, their circumstances for 2023, and how to profit from them.
Ways to make money on the stock market.
If you're conservative like me, you should invest in an index fund. Most of these funds are down 10-30% of ATH:
In earlier recessions, most money index funds lost 20%. After this downturn, they grew and passed the ATH in subsequent months.
Now is the greatest moment to invest in index funds to grow your money in a low-risk approach and make 20%.
If you want to be risky but wise, pick companies that will get better next year but are struggling now.
Even while we can't be 100% confident of a company's future performance, we know some are strong and will have a fantastic year.
Microsoft (down 22%), JPMorgan Chase (15.6%), Amazon (45%), and Disney (33.8%).
These firms give dividends, so you can earn passively while you wait.
So I consider that a good strategy to make wealth in the current stock market is to create two portfolios: one based on index funds to earn 10% to 20% profit when the corrections end, and the other based on individual stocks of popular and strong companies to earn 20%-30% return and dividends while you wait.
How to profit from the downturn in the real estate industry.
With rising mortgage rates, it's the worst moment to buy a home if you don't want to be eaten by banks. In the U.S., interest rates are double what they were three years ago, so buying now looks foolish.
Due to these rates, property prices are falling, but that won't last long since individuals will take advantage.
According to historical data, now is the ideal moment to buy a house for the next five years and perhaps forever.
If you can buy a house, do it. You can refinance the interest at a lower rate with acceptable credit, but not the house price.
Take advantage of the housing market prices now because you won't find a decent deal when rates normalize.
How to profit from the cryptocurrency market.
This is the riskiest market to tackle right now, but it could offer the most opportunities if done appropriately.
The most powerful cryptocurrencies are down more than 60% from last year: $68,990 for BTC and $4,865 for ETH.
If you focus on those two coins, you can make 30%-60% without waiting for them to return to their ATH, and they're low enough to be a solid investment.
I don't encourage trying other altcoins because the crypto market is in crisis and you can lose everything if you're greedy.
Still, the main Cryptos are a good investment provided you store them in an external wallet and follow financial gurus' security advice.
Last thoughts
We can't anticipate a recession until it ends. We can't forecast a market or asset's lowest point, therefore waiting makes little sense.
If you want to develop your wealth, assess the money prospects on all the marketplaces and initiate long-term trades.
Many millionaires are made during recessions because they don't fear negative figures and use them to scale their money.
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joyce shen
3 years ago
Framework to Evaluate Metaverse and Web3
Everywhere we turn, there's a new metaverse or Web3 debut. Microsoft recently announced a $68.7 BILLION cash purchase of Activision.
Like AI in 2013 and blockchain in 2014, NFT growth in 2021 feels like this year's metaverse and Web3 growth. We are all bombarded with information, conflicting signals, and a sensation of FOMO.
How can we evaluate the metaverse and Web3 in a noisy, new world? My framework for evaluating upcoming technologies and themes is shown below. I hope you will also find them helpful.
Understand the “pipes” in a new space.
Whatever people say, Metaverse and Web3 will have to coexist with the current Internet. Companies who host, move, and store data over the Internet have a lot of intriguing use cases in Metaverse and Web3, whether in infrastructure, data analytics, or compliance. Hence the following point.
## Understand the apps layer and their infrastructure.
Gaming, crypto exchanges, and NFT marketplaces would not exist today if not for technology that enables rapid app creation. Yes, according to Chainalysis and other research, 30–40% of Ethereum is self-hosted, with the rest hosted by large cloud providers. For Microsoft to acquire Activision makes strategic sense. It's not only about the games, but also the infrastructure that supports them.
Follow the money
Understanding how money and wealth flow in a complex and dynamic environment helps build clarity. Unless you are exceedingly wealthy, you have limited ability to significantly engage in the Web3 economy today. Few can just buy 10 ETH and spend it in one day. You must comprehend who benefits from the process, and how that 10 ETH circulates now and possibly tomorrow. Major holders and players control supply and liquidity in any market. Today, most Web3 apps are designed to increase capital inflow so existing significant holders can utilize it to create a nascent Web3 economy. When you see a new Metaverse or Web3 application, remember how money flows.
What is the use case?
What does the app do? If there is no clear use case with clear makers and consumers solving a real problem, then the euphoria soon fades, and the only stakeholders who remain enthused are those who have too much to lose.
Time is a major competition that is often overlooked.
We're only busier, but each day is still 24 hours. Using new apps may mean that time is lost doing other things. The user must be eager to learn. Metaverse and Web3 vs. our time? I don't think we know the answer yet (at least for working adults whose cost of time is higher).
I don't think we know the answer yet (at least for working adults whose cost of time is higher).
People and organizations need security and transparency.
For new technologies or apps to be widely used, they must be safe, transparent, and trustworthy. What does secure Metaverse and Web3 mean? This is an intriguing subject for both the business and public sectors. Cloud adoption grew in part due to improved security and data protection regulations.
The following frameworks can help analyze and understand new technologies and emerging technological topics, unless you are a significant investment fund with the financial ability to gamble on numerous initiatives and essentially form your own “index fund”.
I write on VC, startups, and leadership.
More on https://www.linkedin.com/in/joycejshen/ and https://joyceshen.substack.com/
This writing is my own opinion and does not represent investment advice.
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Nathan Reiff
3 years ago
Howey Test and Cryptocurrencies: 'Every ICO Is a Security'
What Is the Howey Test?
To determine whether a transaction qualifies as a "investment contract" and thus qualifies as a security, the Howey Test refers to the U.S. Supreme Court cass: the Securities Act of 1933 and the Securities Exchange Act of 1934. According to the Howey Test, an investment contract exists when "money is invested in a common enterprise with a reasonable expectation of profits from others' efforts."
The test applies to any contract, scheme, or transaction. The Howey Test helps investors and project backers understand blockchain and digital currency projects. ICOs and certain cryptocurrencies may be found to be "investment contracts" under the test.
Understanding the Howey Test
The Howey Test comes from the 1946 Supreme Court case SEC v. W.J. Howey Co. The Howey Company sold citrus groves to Florida buyers who leased them back to Howey. The company would maintain the groves and sell the fruit for the owners. Both parties benefited. Most buyers had no farming experience and were not required to farm the land.
The SEC intervened because Howey failed to register the transactions. The court ruled that the leaseback agreements were investment contracts.
This established four criteria for determining an investment contract. Investing contract:
- An investment of money
- n a common enterprise
- With the expectation of profit
- To be derived from the efforts of others
In the case of Howey, the buyers saw the transactions as valuable because others provided the labor and expertise. An income stream was obtained by only investing capital. As a result of the Howey Test, the transaction had to be registered with the SEC.
Howey Test and Cryptocurrencies
Bitcoin is notoriously difficult to categorize. Decentralized, they evade regulation in many ways. Regardless, the SEC is looking into digital assets and determining when their sale qualifies as an investment contract.
The SEC claims that selling digital assets meets the "investment of money" test because fiat money or other digital assets are being exchanged. Like the "common enterprise" test.
Whether a digital asset qualifies as an investment contract depends on whether there is a "expectation of profit from others' efforts."
For example, buyers of digital assets may be relying on others' efforts if they expect the project's backers to build and maintain the digital network, rather than a dispersed community of unaffiliated users. Also, if the project's backers create scarcity by burning tokens, the test is met. Another way the "efforts of others" test is met is if the project's backers continue to act in a managerial role.
These are just a few examples given by the SEC. If a project's success is dependent on ongoing support from backers, the buyer of the digital asset is likely relying on "others' efforts."
Special Considerations
If the SEC determines a cryptocurrency token is a security, many issues arise. It means the SEC can decide whether a token can be sold to US investors and forces the project to register.
In 2017, the SEC ruled that selling DAO tokens for Ether violated federal securities laws. Instead of enforcing securities laws, the SEC issued a warning to the cryptocurrency industry.
Due to the Howey Test, most ICOs today are likely inaccessible to US investors. After a year of ICOs, then-SEC Chair Jay Clayton declared them all securities.
SEC Chairman Gensler Agrees With Predecessor: 'Every ICO Is a Security'
Howey Test FAQs
How Do You Determine If Something Is a Security?
The Howey Test determines whether certain transactions are "investment contracts." Securities are transactions that qualify as "investment contracts" under the Securities Act of 1933 and the Securities Exchange Act of 1934.
The Howey Test looks for a "investment of money in a common enterprise with a reasonable expectation of profits from others' efforts." If so, the Securities Act of 1933 and the Securities Exchange Act of 1934 require disclosure and registration.
Why Is Bitcoin Not a Security?
Former SEC Chair Jay Clayton clarified in June 2018 that bitcoin is not a security: "Cryptocurrencies: Replace the dollar, euro, and yen with bitcoin. That type of currency is not a security," said Clayton.
Bitcoin, which has never sought public funding to develop its technology, fails the SEC's Howey Test. However, according to Clayton, ICO tokens are securities.
A Security Defined by the SEC
In the public and private markets, securities are fungible and tradeable financial instruments. The SEC regulates public securities sales.
The Supreme Court defined a security offering in SEC v. W.J. Howey Co. In its judgment, the court defines a security using four criteria:
- An investment contract's existence
- The formation of a common enterprise
- The issuer's profit promise
- Third-party promotion of the offering
Read original post.
Maria Stepanova
3 years ago
How Elon Musk Picks Things Up Quicker Than Anyone Else
Adopt Elon Musk's learning strategy to succeed.
Medium writers rank first and second when you Google “Elon Musk's learning approach”.
My article idea seems unoriginal. Lol
Musk is brilliant.
No doubt here.
His name connotes success and intelligence.
He knows rocket science, engineering, AI, and solar power.
Musk is a Unicorn, but his skills aren't special.
How does he manage it?
Elon Musk has two learning rules that anyone may use.
You can apply these rules and become anyone you want.
You can become a rocket scientist or a surgeon. If you want, of course.
The learning process is key.
Make sure you are creating a Tree of Knowledge according to Rule #1.
Musk told Reddit how he learns:
“It is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles, i.e. the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang onto.”
Musk understands the essential ideas and mental models of each of his business sectors.
He starts with the tree's trunk, making sure he learns the basics before going on to branches and leaves.
We often act otherwise. We memorize small details without understanding how they relate to the whole. Our minds are stuffed with useless data.
Cramming isn't learning.
Start with the basics to learn faster. Before diving into minutiae, grasp the big picture.
Rule #2: You can't connect what you can't remember.
Elon Musk transformed industries this way. As his expertise grew, he connected branches and leaves from different trees.
Musk read two books a day as a child. He didn't specialize like most people. He gained from his multidisciplinary education. It helped him stand out and develop billion-dollar firms.
He gained skills in several domains and began connecting them. World-class performances resulted.
Most of us never learn the basics and only collect knowledge. We never really comprehend information, thus it's hard to apply it.
Learn the basics initially to maximize your chances of success. Then start learning.
Learn across fields and connect them.
This method enabled Elon Musk to enter and revolutionize a century-old industry.