Connect with us


Investors want money back from alleged $33 million crypto fraud



Another alleged crypto trading scam is in the books. This one involves a stock trader, a financial advisor, a surgeon, and an enterprise called Q3 I LP.

Michael Ackerman, a former broker with the New York Stock Exchange (NYSE), was charged in February with stealing substantial funds from the more than $33 million raised from over 100 investors. Now, aggrieved investors have formed a legal representing body known as the Q3 Investment Recovery Vehicle to hold him and his alleged partners, former Wells Fargo Advisors employee James Seijas and Florida surgeon Quan Tran, financially accountable for their losses.

Q3 allegedly lured investors into the scam with the promise of a high-returns trading algorithm via Facebook groups such as the Physician Dads’ Group. Ackerman was the alleged developer of an investment algorithm that lured investors to the group; he claimed to have used it in the past to trade stocks, but that it was just as successful when used for trading crypto.

According to a Securities and Exchange Commission (SEC) complaint from February, however, “Ackerman invested no more than $10 million of the $33 milion raised from investors in cryptocurrencies and the profits generated by the Algorithm were minimal, at best.

Instead, Ackerman allegedly purchased five properties between 2018 and 2019 with investors’ money, including a 150+ acre plot in Montana and a $3 million beach house in Florida. He also allegedly spent funds on new cars and jewelry.

The SEC further stated that the company—which at the time, was operating under the name Q3 Holdings LLC—had charged its customers licensing fees to give them access to Ackerman’s trading algorithm. This resulted in another $4 million in payments, but the SEC says he failed to notify their limited partners of these payments. Moreover, Ackerman allegedly falsified account information to show 15% returns.

These and other financial discrepancies were initially discovered by Tran and Seijas late last year, according to an unsealed affidavit filed by Homeland Security Investigations’ special agent John Rodriguez. Upon visiting Ackerman in Ohio following a hospital stay, Tran and Seijas apparently gained access to his computer and discovered what Tran referred to as a big difference between the assets that Ackerman “had been reporting to us and the balance in the trading account.”

Upon confronting Ackerman about what appeared to be missing money, Ackerman allegedly told Tran and Seijas that he had moved it to a more secure trading account but refused to tell them anything more. Tran later notified the SEC about the situation, and federal prosecutors have charged Ackerman with wire fraud and money laundering.

While Tran and Seijas are not facing criminal charges, the Recovery Group is attempting to hold them accountable for their purported lack of judgement regarding Ackerman’s dealings. Both are alleged to have simply passed along whatever documents Ackerman manufactured to limited partners without question.

Furthermore, the company did not have a fund administrator like most other hedge funds, and both men allegedly forwarded screenshots from Ackerman’s cellphone as performance updates to limited partners—a strange way to roll considering Seijas has a background in finance.

Source link

Continue Reading


  1. EmeryPum

    May 15, 2020 at 12:17 am

    asian cam girls [url=]hotcamgirls1[/url] girl webcam.

  2. Arthurerype

    May 15, 2020 at 1:29 am

    web cam site [url=]adultcamsites1[/url] jasmin cam site.

  3. Arthurerype

    May 15, 2020 at 5:31 pm

    amateur webcam sex bestonlinesexcams1 free couple live sex cam.

  4. DavidZed

    May 21, 2020 at 6:55 pm

    dirty sex cams dirty sex chat now naked sex cams hawaiian sex cams.

  5. DavidThymn

    May 29, 2020 at 8:18 pm

    new live sex cams night sex cams private sex cams.

  6. Sanfordscogy

    May 30, 2020 at 11:44 am

    live sex cams xhamster sexcams00 all live cams and sex chat models.

  7. DannyAdome

    June 1, 2020 at 1:39 am

    free naked sex cams fre sex cams sex cams near by.

  8. DannyAdome

    June 3, 2020 at 7:38 am

    shared sex cams free black live sex cams watch ebony free sex web cams.

Leave a Reply

Your email address will not be published. Required fields are marked *


Fintechs are moving into bitcoin, but expect crypto startups to stay on their home turf – The Block Crypto




Continue Reading


Crypto trade is not a scam




It is an era of digitalization that developed digital wallets. The advent of digital currency dates back to just a little over one decade when Bitcoin was introduced as the first digital currency into this world.

Bitcoin is a unique currency as the world of cryptocurrency is different. In the beginning, it seemed like fiction as no one had ever thought of an intangible virtual currency. People have ever stored their currency notes in wallets and banks, but the storage of cryptocurrency is done differently.

Bitcoin is a revolution

Bitcoin has revolutionized the world but troubled the governments of various countries. Every government issues its fiat money unique to the country, but cryptocurrency is a universal currency for all countries. It’s like one size fits all. Thus, the standard of cryptocurrency is different. For instance, Bitcoin is one crypto coin, but it can be used worldwide with the same value. Specific fiat currency doesn’t have equal value in all countries. For instance, the value of the Unites State dollar (USD) is not the same in every country.

Bitcoin has worried governments but sounds useful to investors

The trouble of the governments of various countries starts with the scams by the Bitcoin trading platforms. The trading on these platforms is not regularized by the governments that puta red flags on the transactions. Despite issuing warnings by the governments, the interest of investors in Bitcoin and a few other cryptocurrencies have not lessened because of promising outrageous returns in crypto trading. The scams are apparent when trading occurs in an unregulated environment. Still, cryptocurrency sounds to be too useful to investors. It is genuinely helpful to those who understand Bitcoin, its working, mining, and usefulness.

Trade-in Bitcoin securely

One can be good at the crypto trading when the pros and cons of this trade are known to him. The best for this situation would be to join a reliable platform like bitcoin system. One can see how this platform works. It is never recommended to jump into any trade without a proper insight of its pros and cons. It is not only for crypto trade but also other trades such as stocks, forex, commodities, etc. No doubt, things have changed drastically. Modern trading platforms are more advanced, automated, and secured. But online trading carries an inherent risk of data theft or hacking that is unavoidable even on a fully secured platform.

Bitcoin investment is for secured future

Anyway, scams will continue to occur, but the interest of more investors will be created in crypto trade that is lucrative and rewarding. It is a time to take advantage of growing Bitcoin revolution. Crypto trade is an emerging industry of contemporary time. Bitcoin is the most versatile cryptocurrency in the world. Trading a versatile crypto coin in an emerging industry is an excellent thing for a bright future. What’s today will be more in future. It is understood that the future value of Bitcoin is going to be much more than what it is today. Potential investors are already alert, and the newbies can try this opportunity for their secure future.

Bitcoin trading is a boon

It is anticipated investment in Bitcoin can bring a return up to 300X per day or 9000X per month the value of the investment. It is unquestionably a massive return one cannot expect in any other investment portfolio. Furthermore, trading in Bitcoin has become much more comfortable with advance software that enables everyone to trade Bitcoin and other cryptocurrencies with ease. Bitcoin trading is a boon for a hefty return and secure future. Bitcoin trading can be taken as a side business or primary source of income, based on your investment potential, but Bitcoin is not expected to frustrate investors anyways.

Valid reasons to invest in Bitcoin

One good advice for the secure future is to invest and trade in Bitcoin for the following reasons:

  • Cryptocurrencies offer a level of independence impossible with other means. Your money is thus yours alone.
  • Crypto trade is an independent, safer alternative to more traditional investment solutions.
  • Both joining and taking part in crypto trade are quite simple.
  • Bitcoin trade has high volatility and high liquidity.
  • Crypto trade has favorable forecasts. The profits can be earned on a day-to-day basis without a hassle.
  • This investment can bring incredible returns in a short period.

Source link

Continue Reading


A General Strategy on How to Select a Crypto Fund, Part 2




With about 800 crypto funds relying on a new asset class, which has its own properties, it is essential to assess them through an appropriate framework. We provide a basic framework of useful metrics to assess the true risk of a crypto fund as a quantitative screening tool. Short-listed funds can then be assessed in more detail through a classic due diligence process.

Assessing the return/risk profile of a directional trading crypto fund

Assessing the expected return of a directional fund

Investors in a directional fund should first have a clear understanding of the dynamic of the fund’s overall strategy in order to realize where the performance will come from and over what period before assessing whether the risk taken to achieve such results is worth it. This is achieved through discussions with the fund manager.

Warning: If a fund manager refuses to explain any of the fund’s strategies, beware!

When asking about a fund’s strategies, a truthful and experienced manager should be able to explain it in plain English. If a fund manager doesn’t want to disclose anything stating that it’s a trade secret, you could still try to understand what the fund tries to achieve by analyzing its past track record. However, in such a case, it’s unlikely that the manager will provide daily returns of the strategy for a more granular analysis, which may thus be worthless.

A transparent fund manager inspires trust, a secretive one inspires defiance, but even if a manager is transparent about strategy, investors should verify that these pitches from fund managers are credible and not take their word for granted. The Bernie Madoff Ponzi scheme was just that. Madoff explained that he was trading S&P 100 options as the basis of his strategy. Why not? But given the size of this specific market (~$100 million daily on average), there was no way he could have been trading the size of his fund ($6 billion), but he still lured many naïve investors.

Understanding the fundamentals of the strategy

Directional funds try to achieve their goals in different ways, and investors have to understand in which market environments they are going to perform well or not; some funds may perform very well during smooth trending markets but can be crushed during times of high volatility, whereas funds performing well during hectic markets can dramatically underperform in strong trending markets.

No single strategy can perform well in every market environment, as each strategy is designed to only fully capture specific moves and avoid being crushed otherwise. Directional funds tend to embed different strategies, each designed to capture specific market moves; but since these strategies are usually blended together, the resulting blend should perform well during most market environments, but will always underperform the best single strategy in a given market environment.

Understanding the strategy timeframe

Understanding the timeframe through which a fund strategy works — i.e., intraday and/or on a several-day basis — and the broad expectations of the strategy in terms of capturing market movements — e.g., captures 80% of an upward move, 30% of a downward move on average — are necessary to make a meaningful comparison against a potential benchmark.

In the example just quoted, such a fund would underperform a passive index representative of the traded underlying asset during strong upward movements but should prove its value when the passive index reverses course by limiting the losses, leading to a better performance against the passive index but over a full up/down market cycle. 

Assessing the risk profile of a directional fund

In order to assess the risk profile of a directional fund, an advanced — i.e., nonlinear — hedge fund analysis framework is useful, but metrics of a crypto fund cannot be compared with the metrics of a traditional hedge fund — e.g., volatility, Sharpe ratio, etc.

We will assume that the past behavior of a fund is expected to continue more or less in the near future if the manager’s strategy is robust and well designed.

A nonlinear analysis framework

If an instrument behaves the same during different market conditions, it is said to have a linear behavior, but if it behaves differently during different market conditions, it is said to have a nonlinear behavior.

For example, when a fund gains 1% every time the broad market gains 1% and loses 1% every time the broad market losses 1%, it is linear; but when a fund gains 1% every time the broad market gains 1% and loses 2% every time the broad market losses 1%, it is nonlinear, as its behavior during negative markets doesn’t have the same amplitude as during positive markets.

Assessing the nonlinearity of a fund

The question is: “Is a given fund linear or nonlinear?” The quick answer is that most active funds will be nonlinear, but there’s a statistical test to answer the question more precisely, the Jarque–Bera test for normality.

However, metrics from a nonlinear framework can also be used to assess linear instruments, but not the other way around. 

Nonlinear risk metrics

The four main metrics of a linear framework adapted to assess nonlinear asset behaviors are volatility, correlation, beta and value at risk. 

Simple time series are used in the section below to illustrate the purpose.

1. Volatility

Volatility measures the degree of dispersion of returns around their mean. The higher the volatility, the higher the dispersion of the returns. If an asset has a linear behavior, a high dispersion of returns around their mean indicates that returns can be far above but also far below their mean, and this is generally considered as an easily understandable measure of risk. However, if the asset has a nonlinear behavior, overall volatility can be highly misleading, either over or underestimating the risk of loss.

In order to assess the behavior of a nonlinear asset from a volatility point of view, we will split the metric into two sub-metrics: positive volatility and negative volatility. Positive volatility is a classic volatility measure but is only applied to the positive returns of the asset. Likewise, negative volatility is a classic volatility measure but is only applied to the negative returns of the asset. Thus, we assess the dispersion of the returns on the positive side and on the negative side. If the asset is linear, these two metrics are close to each other.

Example: Let’s consider three funds, A, B and C as having had the following returns over the same period:

Fund A: { -3%; -8%; 5%; 58%; -1%; 2; 48%; -2%; 1%; 38% }

Fund B: { -3%; -8%; 5%; 12%; -1%; 2; 6%; -2%; 1%; 4% }

Fund C: { -45%; -8%; 5%; 12%; -1%; 2; 6%; -2%; 1%; 4% }

High volatility does not equate high risk

The volatility of Fund B is 5.3%, whereas the volatility of Fund A is 23.1%. Thus, if considering the overall volatility as a risk measure, then Fund B is much less risky than Fund A, whereas Fund C lies between.

When assessing the positive and negative volatility of funds A, B and C, we have:

Volatility of Funds A, B and C

Looking at the positive and negative volatility of each fund leads to a very different conclusion from just looking at their overall volatility: Fund C having the highest negative volatility and the lowest positive volatility is actually the riskiest of the three funds, whereas fund A having the highest positive volatility and the lowest negative volatility is the least risky, and fund B lies in between.

In fact, by taking a closer look at the returns of the three funds, Fund A contained its losses as much as Fund B but was able to capitalize on three strong returns that Fund B couldn’t capture. On the other hand, Fund C is similar to Fund B but has only been heavily hit once, whereas Fund B hasn’t.

Therefore, would one rather invest in a fund that delivers good returns, controlling the downside, but without any upswing either (Fund B), or invest in a fund that controls the downside as well, but which can deliver a winning lottery ticket from time to time (Fund A)?

Assessing the volatility of a crypto fund with a nonlinear framework is the only way to assess its true risk from a volatility point of view — i.e., understanding what contributes to high volatility.

Debunked myth #1: A crypto fund with overall high volatility doesn’t necessarily equate a highly risky one.

2. Correlation

Correlation measures how an asset is moving in relation to another one. The closer an asset is to 1, the more the assets will move in sync; the closer an asset is to -1, the more the assets will move in the opposite direction one from each other.

Again, measuring the overall correlation of a nonlinear asset can lead to misleading conclusions about how one asset moves in comparison with another.


Fund A: { -9%; 13%; -1%; 15%; -9%; 1; 28%; -6%; -2%; 0% } 

Fund B: { 5%; 13%; 1%; 28%; 6%; 1; 25%; -5%; 2%; -1% }

Benchmark: { -28%; 2%; -33%; 34%; -19%; -15; 21%; -10%; -6%; -5% }

High correlation does’t mean move in tandem

The correlation of Fund A to the benchmark is 0.81, which is similar to the correlation of Fund B to the benchmark. By looking at how these two funds correlate with their common benchmark, they are identical when assessing their overall correlation.

Now assessing the positive and negative correlations of Funds A and B with their benchmark, we have: a more subtle manner to assess the correlation of a fund with a benchmark. It consists of breaking the global correlation measure described above into two sub-correlation analyses: The positive correlation is the measured correlation of the fund with a benchmark only during positive returns of the benchmark, whereas the negative correlation is the measured correlation of the fund with a benchmark only during negative returns of the benchmark. The positive and negative correlation measures range like the standard correlation measure between -1 and +1 with the same meaning.

Therefore, an investor should look for a fund that has a high positive (i.e., the closest to +1) positive-correlation, meaning the fund moves up when the benchmark moves up, and a low negative (i.e., the closest to -1) negative-correlation, meaning that the fund moves up when the benchmark moves down.

Correlations of funds A and B

Fund A exhibits a moderate positive positive-correlation with its benchmark (0.23) and a moderate positive negative-correlation with its benchmark (0.30), whereas Fund B shows a very high positive positive-correlation with the benchmark (0.97) and a medium negative negative-correlation with its benchmark (-0.45).

This means that Fund A moved more or less in sync with its benchmark either on the upside or the downside, whereas Fund B moved upward when the benchmark was up most of the time but moved also upward from time to time when the benchmark was moving down. This is exactly the characteristic of a fund investors should look for, but this is only visible in a nonlinear framework.

Debunked myth #2: A high global correlation of a crypto fund to a benchmark doesn’t necessarily mean that the fund will move in sync with the benchmark most of the time.

3. Beta

The beta measures the amplitude of how an asset is moving compared to another. Its value is a rough estimate of how much an asset will move vs. another one considered. A value above 1 means that an asset moves more than 1x than another one in the same direction; a value between 0 and 1 means that an asset moves less than 1x than another one in the same direction. Negative values can be interpreted as positive values in terms of multiplying effect, but with moves on the opposite directions.

Note: The beta of an asset vs. another should only be calculated if there’s a statistically significant correlation between the two assets.

Example: Let’s consider the two funds used previously with the correlation analysis, which were both highly correlated with the benchmark (0.81).

Fund A: {-9%; 13%; -1%; 15%; -9%; 1; 28%; -6%; -2%; 0%}

Fund B: {5%; 13%; 1%; 28%; 6%; 1; 25%; -5%; 2%; -1%}

Benchmark: {-28%; 2%; -33%; 34%; -19%; -15; 21%; -10%; -6%; -5%}

Beta doesn’t always mean "move as much as"

The beta of Fund A to the benchmark is 0.46, and the beta of Fund B to the benchmark 0.43 — i.e., both funds have a similar beta to their benchmark. But are they really equal?

Assessing the positive and negative beta of Funds A and B with their benchmark, we have: 

Beta of funds A and B

Unsurprisingly, when looking at the beta of these two funds through a nonlinear prism, we have a different story. Fund A tends to capture on average about 11% of an up or down move of its benchmark, whereas Fund B tends to capture on average 48% of an up move of its benchmark while capturing -15% of a negative move of its benchmark — i.e., capturing 15% of the amplitude of the down move of its benchmark, but delivering it in positive terms instead.

Just like with the correlation, investors should seek to invest with funds showing an as-high-as-possible positive positive-beta and an as-high-as-possible negative negative-beta vs. the funds’ benchmarks. 

Debunked myth #3: The overall beta of a crypto fund has no value unless it is assessed in a nonlinear manner.

4. Value at Risk

The value at risk, or VaR, is an estimate of how much an investment might lose, with a given probability, given normal market conditions, and in a set time period.

Example: VaR (Fund, 95%) = -7.5% means that over the considered period, the fund can lose more than -7.5% with 5% (= 100%–95%) probability. In other words, there’s a 95% chance that the fund will lose less than -7.5% over the considered period.

There are many ways to compute the VaR of an asset that go beyond the scope of this paper, but again, if the nonlinear behavior of the asset is not taken into account in estimating the VaR, the results lead to false conclusions.

However, given the often-hectic behavior of digital assets, it is difficult to assess their VaR, no matter the model used, and the obtained results may not be of great help to calibrate risk. This is why VaR is not really used to assess crypto funds. 

Comparing the risk metrics of traditional hedge funds and crypto funds

Now that the main die-hard myths about fund metric analysis have been debunked, another misleading analysis aspect of crypto funds is to compare the metrics side by side with the well-known metrics of traditional assets.

Essentially, digital assets are way more volatile than their traditional cousins, and some of their metrics can be of several orders of magnitude different: from annualized return and volatility to the Sharpe and Sortino ratios.

Sharpe ratio

For example, a Sharpe ratio above 1 is more of an exception rather than the norm for funds dealing with traditional assets, as their annualized return is usually in the 5%–15% range and an annualized volatility of 10%–15% that doesn’t imply insignificant returns from their means. 

However, with Bitcoin (BTC), for example, its annualized return from 2016 to date has been slightly above 100%, while its annualized volatility is close to 85%, leading to a ratio above 1 despite its frequent booms and busts.

Thus, the Sharpe ratio of a good crypto fund — one that is able to provide to capture most of the upside of its underlying asset while protecting on the downside — can be in a high single to a low double-digit range, which can appear highly suspicious if compared to the Sharpe ratio of a typical hedge fund.

Sortino ratio

The same is even more true for the Sortino ratio. For example, Bitcoin has a 30% annualized downside volatility, which is roughly three times that of the S&P 500, meaning negative returns reaching three times further than the ones of the S&P 500, which leads to a three times lower value of the denominator of the Sortino ratio of Bitcoin. However, if Bitcoin has an annualized return 10 times bigger than that of the S&P 500, the numerator of the Sortino ratio of Bitcoin will be 10 times higher than the numerator of the Sortino ratio of the S&P 500. Thus, when calculating the Sortino ratio of Bitcoin, dividing a numerator that is 10 times bigger (than the one of the S&P 500) by a denominator that is 3 times bigger (than the one of the S&P 500), we obtain roughly a ratio for Bitcoin that is about 3.3 (=10/3) times higher than that of the S&P 500. More precisely, the Sortino ratio of Bitcoin is above three, whereas the Sortino ratio of the S&P 500 is about 0.8.

Therefore, for a good crypto fund, posting a high annualized return over limited downside volatility can easily lead to a high double-digit Sortino ratio.


Drawdowns are bounded metrics between 0% and -100%, contrary to the unbounded metrics that are the Sharpe and Sortino ratios described above. Thus, an investor can compare side by side the drawdowns of a crypto fund to the ones of a traditional fund without having to take into account the scaling of the metrics.

However, investors have to understand that the magnitude of drawdowns of crypto funds can be more substantial than the ones of a fund trading only traditional assets, as the digital assets can swing more wildly. For example, a 40% drawdown for a crypto fund can be “equivalent” to a 15% drawdown for a traditional fund, but the crypto fund lost is nevertheless more than the traditional fund. The idea is just to put things into perspective here.

A loss due to a drawdown is never pleasant to experience, especially when it is a big loss; therefore, investors have to pay more attention to the shapes of the fund drawdowns. The shape of a drawdown refers to the shape described by the drawdown curve of a fund. These shapes are triangles more or less tilted, which tell how the fund manager dealt with losses and are highly instructive, as we will detail below.

Let’s consider these three funds:

Fund A: { 1%; 3%; -1%; 5%; 2%; -23.5; 2%; 6%; -2%; 3%; 1%; 5%; 2%; -3%; 6%; 3% }

Fund B: { 1%; -2%; -1%; -0.5%; -2%; -1.5%; -2%; 0.5%; -2%; -3%; -1%; -2%; -1%; 23%; -1%; 2% }

Fund C: { 2%; -1%; 3%; 1%; -0.5%; 1%; -0.5%; -19%; 21%; -3%; 2%; 1%; -0.5%; 2%; 0%; 1% }

They all have the same performance (around +5%) and maximum drawdown (around -20%) over the same period, but the shapes of their drawdowns depict a very different story for each fund.

Drawdown shapes matter

Generally, there are three cases:

1. A sudden loss followed by a steady recovery over several weeks. This is the shape of the drawdowns one could expect. At some point, the fund manager’s strategy is caught wrong-footed and a sudden, steep loss occurs. As discussed earlier, as the old Wall Street adage says “markets take the elevator down, but the stairs up” — i.e., a sudden panic move downward happens quickly, but it takes time for the markets to calm down and realize that what caused the panic move in the first place is over, which explains the slow recovery. These drawdowns are normal and inherent to the strategy. Investors have to simply make sure that all of the past major drawdowns were about the same magnitude, showing the robustness of the underlying strategy; bad trades occur, but they are always controlled and will eventually recover.

Drawdown curve type A

2. Continuous and increasing losses over several months recovered in just a few weeks. Such drawdowns are more problematic, as they may show that the manager’s strategy hasn’t worked for a long time, but facing investors’ redemptions, the fund manager went “all in” in order to stop the bleeding: It’s make or break. However, such drawdown shapes can sometimes also be explained by the way the strategy works and may not be a sign of a gambling fund manager. This is why it is always important to understand what the fund strategy tends to capture in order to assess its behavior.

Drawdown curve type B

3. A sudden loss, followed by a quick recovery. These drawdowns can take place from time to time and are usually linked to a market dislocation, leading to a fast and deep loss followed by an equally strong recovery.

Drawdown curve type C

Finally, when looking at fund drawdowns, having data-sampling as precise as possible is key: Looking at drawdowns on a daily basis or on a monthly basis can lead to very different conclusions.

If managers just report their performance on a monthly basis, as is generally the case, only the change of the fund’s net asset value, or NAV, between the last day of the current month and the last day of the previous month are disclosed. There’s no information about what occurred during the month. For performance-reporting purposes, that’s fine, but for risk assessment, this can be highly misleading.

Indeed, if the fund witnessed a 30% drawdown during the month that fully recovered by the end of the month, then looking only at monthly NAVs won’t show it, and investors will have a false sense of confidence by assuming that the fund never had any 30% drawdown in this example. Reporting performance on a daily basis shows what happened from day to day, which is far more informative than just from month to month.

For passive index, drawdowns measured on a daily or monthly basis are very close because there’s no active management involved. However, with actively traded strategies, short but steep drawdowns can occur from time to time, and if investors are not aware of that possibility, they may be in for a rude awakening, possibly panicking and selling their holdings. 


Crypto funds come in different shapes and sizes, as we have briefly described in this article.

No matter their nature, since they are all dealing with highly volatile underlying assets, they tend to exhibit nonlinear behavior, which requires a proper framework to analyze them. Through a nonlinear analysis of such funds, we have highlighted that:

  1. A crypto fund with overall high volatility doesn’t necessarily equate to a highly risky one. 
  2. A high global correlation of a crypto fund to a benchmark doesn’t necessarily mean that the fund will move in sync with the benchmark most of the time.
  3. The global beta of a crypto fund has no value unless it is assessed in a nonlinear manner.

Another point we touched upon is that comparing metrics of traditional funds vs. crypto funds is like comparing apples to oranges, given the very different nature of the underlying instruments traded.

We concluded on the drawdowns of crypto funds, which, to us, are a very powerful risk metric when properly analyzed. If an investor had to look at just one risk metric to assess the risk taken vs. the delivered performance, it would be the fund drawdowns, not just their depth, but also their shapes.

We gave some directions on which metrics to look at and analyze, but metrics without their context are meaningless. This is why such an analysis should always be conducted under the supervision of the professional fund manager’s explanations about his strategy.

This is part two of a two-part series on how to sort crypto funds — read part one with an overview of the main types of crypto funds here.

This article does not contain investment advice or recommendations. Every investment and trading move involves risk, you should conduct your own research when making a decision.

The views, thoughts and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.

David Lifchitz is the chief investment officer and managing partner at ExoAlpha — an expert in quantitative trading, portfolio construction and risk management. With over 20 years of experience in these fields and 8+ years in information technology with financial firms, he has notably been the former head of risk management at the U.S. subsidiary of Ashmore Group, which had $74 billion in assets under management in 2018. ExoAlpha has developed proprietary, institutional-grade trading strategies and infrastructure to operate seamlessly in the digital asset markets applying strong risk management principles.

Source link

Continue Reading