Client approach as part of investment strategy
"We are your comfort zone in times of uncertainty."
Comfort
The company's name, Comfort Zone Investments, did not come about by accident. The basic principle of cooperation with the client is to provide a comfortable solution. We recognize that attractive returns are the supporting structure. However, for you to feel genuinely pleased with your investment solution, we must guarantee the following basic building blocks:
Fig. 1 Comfort
Safety
Years of experience managing client investments have taught us the most important thing. To be a transparent partner. When we enter into a contractual relationship with clients, they place a great deal of trust in us. We accept it with a big responsibility. What does the client investment process look like for us?
An Investment Management Agreement is signed mutually, determining the initial investment amount.
The client is given funding instructions. He sends the money to a collection account with Raiffeisenbank, a.s. After the funds are withdrawn, the client receives a confirmation of payment directly from the bank statement.
From this collection account, we send the funds to the account of the LYNX broker. After The client receives a payment confirmation directly from the broker's account statement.
We set up a web-based client zone with detailed daily reports.
Your funds do not flow to offshore companies but are safely deposited with transparent partners. In addition, you have a perfect overview of your investment, thanks to daily reports.
Transparency
Most funds send clients monthly reports on the status of their investment. There can be many reasons why they do so with such low frequency. However, the most important reason is to ensure that the client does not see how his investment is performing during the month. Our philosophy is different: we want the client to have an overview every day. We want to be as transparent with the client as we can be. We are a family company, and we will treat the client as a family member: we prefer to communicate the results carefully. This is why we insist that the client clearly understands the investment strategy and is aware of all fees. In the first year, there are no entry or administration fees. The performance fee is then 20% of the total return in the form of a high watermark.
You can see what the client area looks like at this link, where the client has daily reports.
Email:
Password: comfort
Liquidity
The investment strategy is very liquid. Our investment strategy is very capital flexible, thanks to dynamic asset management. Suppose you need to dispose of funds immediately. In that case, we can make a transfer back to your account, usually within three business days. In the event of termination of the contractual relationship, we have a notice period of one month. It is not uncommon for funds to have a notice period of three months. The recommended investment horizon is a minimum of five years.
Hedging
We can very advantageously hedge the client's investment against currency risk in the form of futures contracts. We hedge the currency risk of the Euro against the US dollar, which is the investment strategy's base currency.
Zone
The client must be in the "zone", i.e., in total symbiosis with the investment strategy. We offer the client a meaningful investment and the opportunity for the necessary financial education to better integrate with the philosophy of the investment strategy and be better prepared for trading patterns.
Fig. 2 Zone
Technology
We use state-of-the-art technology. The industry standard is the Python programming language. It serves us to research and develop strategies through many libraries (allowing us to apply the latest artificial intelligence technologies). It also allows us to accurately utilize advanced trading orders in the markets.
Automation
We have an advanced execution system that allows us to optimize execution costs and increase the potential return of an investment strategy.
The human individual is an emotional creature and makes mistakes, especially in markets where he or she struggles with fear on the downside and greed on the upside every day. Experienced traders know very well what we are talking about. The algorithm has no feelings and is machine accurate. Do you really think a human can compete with it?
Statistical Advantage
Do you play chess, poker, or blackjack? Then know that the biggest pros have perfectly mastered the probability of success of their moves. They are very well versed in the statistical advantage of their actions. The same is true of our investment strategy. We use a statistical advantage. It has been validated with robust tools on a sufficiently large sample of data. We tested thousands of time series of individual stocks and ETFs over a sample of more than 20 years. Older data would not have been relevant given the very different liquidity of the markets before 2000. The basic idea of the strategy was tested back to the 1960s on daily S&P 500 data, and the results have been very optimistic. The original idea to create this trading strategy was to find a statistically significant market inefficiency. Capacity-wise, the strategy uses 70% of the available time in the markets to buy and hold. The remaining 30% of the time is set aside to improve the overall riskiness of the strategy. The less you are in the markets, the better you can protect capital. In terms of historical testing, this fundamental principle of "70% in / 30% out" allows you to achieve higher returns and better risk control than the S&P 500 benchmark. Another essential factor is that the robustness of the strategy has been tested in other global markets. In all these markets, it beats the benchmark - the most representative stock indices of the countries concerned. The strategy has performed decently on the London, Euronext + Xetra (France, Germany, Belgium, Netherlands, and Italy), Switzerland, India, Hong Kong (in HK the model is spoiled by the extra taxation), and Tokyo stock exchanges. At the same time, we have also tested the basic idea on stock futures markets in Canada, Australia, Brazil, Russia, and South Korea. The results clearly indicate that the strategy can work in markets other than the US. This offers us great diversification opportunities with higher capital management in the future.
Artificial Intelligence
When two do the same thing, they are never the same. And with the application of artificial intelligence, it is no different. Case in point: a few years ago, we came across a potential collaboration with a Ph.D. student working at CERN (European Organization for Nuclear Research) in Switzerland. We provided him with all the historical data he needed. The brief was clear: Use AI to create a competitive investment strategy. Our Ph.D. student and scientist with an excellent classification for working with time series processed the data. He applied all kinds of AI tools to it. The result? He failed to produce anything meaningful. His conclusion was that financial markets are so efficient that even artificial intelligence is not up to the task. But we look at the matter from a slightly different perspective:
It's not enough to be a mathematical scientist. You also need to be an expert in financial markets.
AI will learn on the wrong data set if you don't find a sustainable statistical advantage with fundamentals.
And as you know, the devil is in the detail:
The foundation of any successful AI implementation is to provide valid data to train it. In our case, we offered it with the 70% of data that gives it a statistical advantage on its own, i.e., very decent results in terms of stability and profitability. The other 30% of the data is very volatile, and the AI would be wrong to learn about it. You always have to give the AI a training set to learn a predefined activity. Thanks to its incredible computational capabilities, it will learn it and outperform us on a given set. The AI can figure out what to improve with the training set to get the best results according to the predefined goals. We have used AI wisely: we have taken advantage of the statistical advantage of "70% in / 30% out" and provided the AI with a dataset that itself has a statistical advantage and is valid. And when you give AI the correct data, it can do remarkable things. Artificial intelligence can:
beat humans at chess and poker, for example,
mimic voices,
change faces in videos,
write fake news,
participate in Internet discussions,
post comments under news articles,
detect negative comments, and if users are discouraged from posting them,
can identify fake social media accounts,
help scientists discover new stars and planets, develop new drugs and vaccines,
can predict cardiac arrest, epileptic seizures,
can read lips,
identify a person based on their gait,
paint portraits, compose classical music, poems, and novels.
Give us one single relevant reason why it should not better predict the evolution of financial markets?
Asset Portfolio
We monitor around 1,300 stocks and about 300 ETFs (this basket is continuously changing) daily on a filtering basis. These baskets of ETFs and stocks are then updated over time with emerging stocks and ETFs that meet our criteria. Conversely, other assets may drop out of the basket because they no longer meet the criteria. According to our proprietary statistical tools, a total of approximately 1,600 assets are filtered. After applying them, we are typically left with an average of 1000 signals per day during a strong bull market and about 400 signals during a bear market. The signals alone show significant potential in terms of a return. The next step required developing a scoring system to rank and order these signals from best to worst in terms of the probability of significant returns. The best ones are then selected into separate portfolios of stocks and ETFs. On average, we then open 20 positions each day (5 ETFs and 15 stocks), with some days not using all available capital to better protect risk.
We clearly conclude from our in-depth analysis that the application of machine learning as a scoring methodology adds a very significant edge. The most innovative data science approaches to working with time series were developed. The system was trained on stocks only (excluding ETFs, which is so purely out-of-sample on the entire data history) from 1998 to early 2008, along with 2011, 2013, 2014, and 2017 to include data reflecting more recent times in the development. The rest of the data, all remaining years, were used as pure out-of-sample. This model is very robust because the out-of-sample test offers virtually identical returns and their stability to the in-sample test. Our system consists of 50% ETFs and 50% stocks in terms of capitalization. This ratio will vary depending on the total assets under management towards a more significant allocation to portfolio ETFs, allowing for significantly higher capitalizations. One of the biggest advantages of the investment strategy is that although it appears very complex at first, it is simple and easy to grasp, especially in terms of the portfolio selection itself, which adapts very well and robustly to the constantly changing market conditions.
Continuous Improvement
You know the status quo regarding our investment strategy. We have done a lot of work in research and development. Our confidence is growing over time, and our knowledge of the financial markets is also increasing. Developing new strategies and solutions - that's what we do today and every day.
Investments
Another pillar of cooperation with the client is to ensure the stability and diversification of the investment. The strategy is unique, no one else has it, and therefore it will be a natural diversifier in your portfolio. In short, it is very lowly correlated to anything you can think of.
Fig. 3 Investments
Stability of Returns
As a reminder, the investment strategy has been trading since May 2021. You can see its performance in the Performance section.
We will now look at the historical performance, which is the basis on which we have concluded to use the strategy for management:
In Chart 1, you can see the strategy's performance (red curve) compared to the SPY ETF representing the S&P 500 (blue curve) as a benchmark. All returns are net of client expenses and are net of any reinvestment. It turns out that the strategy can be an excellent and functional complement to a buy and hold equity index strategy. The correlation coefficient of daily returns is only 0.38, and that of monthly returns is 0.29 (Table 1).
Chart 1 Evolution of capital allocation to sectors from 2007 to May 2021
The overall results show (Table 1) that historical performance significantly outperforms our benchmark S&P 500. Moreover, the product is very lowly correlated with the S&P 500. Of course, there are periods within historical performance where the equity index beats the strategy, roughly 30% of all months tested. But one data point is most interesting: the investment strategy, according to historical performance, was able to be profitable in the crisis years of 2008 and 2020, so the strategy has held up in tough times, and these are significant signs of its overall robustness.
Table 1 Historical performance of the strategy and comparison with the S&P 500
Diversification
The strategy is rebalanced daily and selects from these assets in the form of ETFs and equities:
Energy,
Materials and Metals,
Real Estate,
Healthcare,
Blockchain,
Cryptocurrencies,
IT,
Communication technogie,
Heavy Industry,
Financial sector,
Inverse ETFs and others...
Thanks to full automation, the portfolio intelligently adapts daily to constantly changing market conditions based on a futuristic portfolio manager - an artificial intelligence unit. Over time, the evolution of ETFs and stocks changes across the historical backtest (Chart 2). It is a clear demonstration of efficient and daily adaptive diversification. In addition to the traditional sectors, we created a Short ETF sector (in the long portfolio) that includes inverse ETFs and VIX. The Others section mainly contains ETFs that do not clearly define sectors, such as bonds, currencies, or commodities. For the Materials sector, gold and silver ETFs make up a significant portion of the portfolio.
Chart 2 Evolution of capital allocation to sectors from 2007 to May 2021
In 2007, before the mortgage bubble burst, the strategy traded Real Estate (Real Estate), but this changed significantly after the arrival of the crisis in 2008, and Real Estate traded very limitedly. Similarly - until 2011 the strategy traded Financials bringing a significant advantage over the overall market. Between 2010 and 2013, exposure was primarily in Materials (Gold and Silver) - often over 40% within the ETFs portfolio. There was virtually no trading in Health Care (Healthcare) until 2013. Still, this sector became a more and more significant part of the portfolio over time. Interestingly, during periods of crisis, inverse ETFs were traded, representing the short side of the strategy, especially during the most volatile 2008-2009 and the onset of the 2020 pandemic. This lends credibility to our model and demonstrates its ability to adapt to very rapidly changing market conditions.
The above shows that although we are exposed to equities and ETFs, the strategy and portfolio achieve considerable neutrality through diversification across different sectors.
Exclusivity
The limited capacity of the investment strategy is linked to the cost of trading. There are two main ones:
Broker and exchange commission costs per capital size.
These are the costs we pay to the broker to buy and sell assets. As the AUM increases, we will become progressively more attractive partners for the broker. Therefore, we will pay less in commissions due to the volume discount, which will ultimately benefit our client. The historical tests (see Table 1) reflect the costs at lower capitalizations and assume a more pessimistic scenario. However, broker commissions have declined highly in recent years due to increasing competition. This is what favors dynamically driven investment strategies of this type.
Slippage costs
Slippage is the difference between the asset's expected and actual filled price. It is not rare to see the argument, "You are executing too many trades, and your trading costs are too high." This is only partly true: Yes, we do execute an average of 3,000 trades a year, which is obviously a lot, but if we want to react adaptively and dynamically daily to the changing world of global markets, there is no other way. The higher the liquidity in a given market, the more minor the slippage in execution. So if you are trading Apple stock (AAPL), for example, the slippage cost will undoubtedly be much lower than for a smaller trading company with many times less capitalization. We need to allocate not only large caps but also small caps and ETFs (on average, we open 5 ETFS positions and 15 stocks per day).
We carefully monitor these costs in live trading and compare them to our forecast costs. And again, we have a much higher slippage in historical testing than in reality. So we are working with a significant margin.
Anyway, because of these costs, we are a capacity-constrained. So we are not creating an investment strategy for the masses. We will monitor our costs carefully as we increase our capitalization. Once we reach the limit, the fund will be closed to further investors. And that makes us exclusive.
If you've read this far, you already have a pretty concrete idea of the investment strategy, core philosophy, and overall approach to clients. Moving on, we'll look at a more detailed description of the investment strategy itself - discussing examples of what assets have traded over time in, for example, 2008 and 2020 - years of extremely high volatility. In the last part of this study, we'll look at its basic statistical properties. The goal is to educate and prepare the client for the natural behavior of the investment strategy.
Overall performance of the investment strategy
Let's start with the return curve expressed as a percentage without reinvestment (Chart 3):
Chart 3 Investment strategy and its historical return from 1 January 2007 to 28 May 2021
It can be misleading to see historical performance over a more extended period, especially at 14.5 years, when displaying the yield curve. The individual corrections don't look that complicated from a psychological workload perspective. But once you go through them, they are very unpleasant and painful experiences. Any investor who has invested in risk assets for the long term will tell you that. For this reason, you need to prepare for such periods accordingly. And here lies the great advantage of algorithmic trading:
Thanks to tens of thousands of simulated trades, you can rely on long historical performance and a large statistical sample.
Let us now focus on the individual performance indicators of the investment strategy in Table 2:
Table 2: Investment strategy performance indicators
Average annual return
A critical parameter of interest to clients is the average annual return. After deducting all expenses, it is 26.91% in our historical testing. However, we must pause here for a moment. The years 2008, 2009, and 2020 were highly profitable and, in many ways, extreme. For this reason, we have calculated an average annual return excluding these years of 17.25%. We will then be delighted for future years if we achieve an average yearly return along these lines.
Risk Management
Maximum Drawdown
We reached our maximum drawdown in the crisis year 2008, precisely 16.40%. However, the positive sign is that the strategy was able to recover quickly from that drawdown and virtually make a new peak in one month. It went through its longest drawdown during 2015 and 2016 for almost a full calendar year. Many executed trades, namely 44755 for nearly 14.5 years of testing, are worth mentioning regarding statistical advantage. Such a relevant statistical sample charges us with optimism if we are currently going through the inevitable drawdowns.
Monte Carlo analysis
Maximum historical drawdown is undoubtedly an important metric. However, it comes from the past. Let's assume we will achieve similar results in average annual returns and stability to those of historical testing. However, history does not repeat itself. Each new volatile period that awaits us in the future may be somewhat different. For this reason, we need to simulate scenarios of potential developments to prepare for the worst possible situations we may face. That's what the Monte Carlo analysis tool is for.
Monte Carlo analysis aims to quantify the effect of randomizing a sequence of trades to determine the worst possible drawdown that a strategy may face in live trading. When using Monte Carlo analysis, we consider the order of historical daily returns. If you change the order of these returns, performance metrics such as net profit, average annual return, or percentage of successful trades do not change over the entire statistical sample of data. However, it is guaranteed to change the maximum possible drawdown. Scenarios will be created where negative daily returns are consecutive in one period, giving us a better idea of the worst-case variations of maximum drawdowns. We have 3,514 trading days, from which we run 1,000 Monte Carlo simulations and obtain the following results (Table 3):
Table 3 Monte Carlo analysis: results of 1000 simulations of the evolution of the investment strategy in the future to determine the worst-case drawdown
As a reminder (Table 2): our historical maximum drawdown was 16.40%. Table 3 shows that such a scenario was favorable for the strategy. However, we prefer to assume worst-case scenarios and thus can determine that:
With a 95% probability, the maximum drawdown in the future should not be more than 30.50%.
If we take the worst possible scenario out of thousands of simulations, the maximum drawdown would be 53.38%. In this case, however, it would already be terrible luck with a probability of 1 in 1000 in live trading. The 30.50% can therefore be considered a metric that our clients should follow in the future.
Performance in individual years
Let us now look at the performance in individual years (Table 4).
Table 4: Annual returns and drawdowns of the investment strategy
Overall, the best performing years were 2020 (97.28%) and 2009 (43.81%). The least profitable years were 2018 (4.67%), followed by 2015 and 2016 (only around 9%).
An interesting metric is the average maximum annual drawdown of 8.46%. As mentioned, the biggest drawdown occurred in 2008. However, a positive sign is that in 2020, when the S&P 500 fell 34%, the strategy faced a maximum drawdown of only 9.33%.
Monthly returns
In Table 5, you can see the monthly returns of the strategy:
Table 5 Table of monthly returns of the strategy
The average monthly return is 2.33%. The worst losing month was January 2008, specifically -11.56%. The most profitable month was November in 2020, specifically 19.18%. The winning percentage of profitable months is 78%.
Note: We started live trading on May 14, 2021, and our return in that month was 0.96%. Under historical testing, you see a performance of -1.62%. The difference is because, in Table 3, you see the entire month, i.e., the results since May 1.
Historical performance in selected years
When the global economy thrives, stock indices deliver considerable bounty. In recent years, except in 2018, especially in the US. People often believe that diversification is achieved by investing in equities across continents. That may be true, of course, but if there is an economic crisis with all the trimmings, virtually all the stock indices of the world of different countries will take the hit. Some more and some less, but let's face it - that's not precisely the diversification you're looking for. Experienced investors are looking for an alternative solution, and depending on historical performance, the strategy may be an alternative. In addition to appreciating during economic booms, it has performed exceptionally well during crisis years. Let's start with the most crisis year that today's younger generations no longer remember: 2008.
2008 - 2009
During these years, the investment strategy performed very well. Not only did it manage to beat the S&P 500 benchmark by an abysmal 103%, but it was even significantly profitable (Chart 3). The overall correlation of the portfolio with the S&P 500 was only 30%, i.e., the correlation coefficient is equal to 0.3.
Chart 3 Evolution of the investment strategy vs. the S&P 500 from January 2008 to May 2009
Before the portfolio was able to adapt to the crisis of early 2008, it went into a slight drawdown (Chart 11) due to the collapse of the Chinese ETFs. It was thus unable to cope (quite naturally) with the sudden and intense volatility. Over time, however, it has already started to beat the benchmark S&P 500 very significantly. It adapted to the high volatility of the markets and became the clear winner.
Chart 4 Portfolio evolution during the crisis period from January 2008 to May 2009
The strategy started to allocate more and more of its total capitalization to inverse ETFs (red) over time (Chart 4). Some were already traded throughout 2008 because the market was already downturned. These ETFs met the conditions for inclusion in the portfolio. In particular, these ETFs were SKF (short financials) and EEV (short emerging markets ETFs x2). These two ETFs had the most significant impact on the overall portfolio in terms of return, namely SKF 7% and EEV 4%. In addition to inverse ETFs, GDX (gold miners) were among the largest contributors to performance at 5%. Then VEU (all world outside the US) 3% and UWM (Russell 2000 x2) 3%. Our AI manager smartly picked sectors such as the Commodities Index, Agriculture Fund, Corporate Bonds, TIPS Bonds, TLT Bonds, Dollar Index, etc. One of the model's greatest strengths is its ability to adapt quickly and dynamically, and as can be seen, even in most crisis scenarios. The model is not blindly focused only on equities. Still, thanks to the wide range of ETFs, it can very cleverly target bullish sectors or commodities in particular. Such an actively managed automated portfolio is competitive with discretionary traders who have to react to changing market conditions manually and with their own decisions. Given the given choices in our model, it is then reasonable to ask whether they could hold up under such extreme conditions.
In addition to ETFs, WM (waste management), AIG (insurance), and MGM (resorts - tourism) were among the top-performing stocks within the portfolio. We then wrote off the worst performers with losses with UYG (Financials x2), SLV (silver), and EWZ (Brazil x2). This selection created volatile swings during the market's highest downturns but ultimately added 20% to the portfolio from September 2008 to the end of the year.
2010 - 2011
During this period, the strategy showed strong performance, mainly due to trading gold and silver ETFs, significantly represented in the portfolio from 2009 to 2012. Energy and semiconductors were other significant contributors to total return.
Chart 5 Evolution of the investment strategy vs. the S&P 500 in 2010 and 2011
The volatility of the S&P 500 during this period was very significant, but the strategy handled it with excellence (Chart 5). Even the considerable drop in gold and silver in 2011 did not negatively impact the portfolio. You can see that our benchmark, the S&P 500, dropped significantly in a very short period. Still, the strategy came out of the situation as a clear winner. To be specific: from 9/21 to 9/27, gold and silver experienced an extremely sharp decline. However, during this period, our strategy was able to adapt and traded only on 9/22 and 9/23. As a result, our portfolio lost only 1%. The next day it was very exposed in energy. Subsequently, on 9/27, we traded very heavily in gold and silver, which was again in an uptrend. These withdrawals enriched the portfolio with a 3% return.
Chart 6 Portfolio evolution in 2010 and 2011
Chart 6 shows how more and more capital has been allocated to the materials (Materials) that gold and silver represent over time.
2014 - 2016
Over these two years, the strategy had an average annual return of just over 5%. It was highly correlated to the S&P 500 benchmark.
Chart 7 Evolution of the investment strategy vs. the S&P 500 from September 2014 to September 2016
The strategy only narrowly beat the S&P 500 benchmark over this period (Chart 7). Exposure was mainly in Health Care - specifically biotechnology. In 2016, the portfolio shifted back to a predominantly metals focus.
Chart 8 Portfolio evolution from September 2014 to September 2016
Portfolio evolution has continued to evolve and adapt to changing market conditions (Chart 8). This is clearly the most robust inherent feature of the system that makes our product competitive in any market regime.
2018
2018 was a very volatile year for US equities (Chart 9). Specifically, from October 2018 to December 2018, the market experienced an extremely rapid decline. This year, the strategy has not fared well, but at the same time has not performed poorly at all. It was still more profitable than the benchmark S&P 500. As recently as mid-November, the portfolio was still at a 12% gain YTD. The downturn came later and our portfolio went into a drawdown and ended the year with only a 4% return.
Chart 9 Evolution of the investment strategy vs. the S&P 500 in 2018 and early 2019
The strategy fell 2.5% on December 27, 2018, the day after the big run-up in the S&P 500.
This decline was primarily due to trading in energy stocks and ETFs.
Chart 10 Portfolio evolution in 2018 and early 2019
The top-performing sectors during this period included cannabis, aerospace, Chinese internet, and lithium. Conversely, the NASDAQ 100 ETF (QQQ), natural gas, and oil posted the largest losses.
2020
2020 was a specific year. We saw a panic during March when the S&P 500 plunged 34%. However, the markets eventually became extremely bullish and there was a resurgence in one of the strongest bull markets in history. How did the strategy perform in such volatile conditions (Chart 11)?
Chart 11 Evolution of the investment strategy vs. the S&P 500 from January 2020 to October 2020
It managed to beat the benchmark S&P 500 by a whopping 62% and finish with a 65% return during this period.
Chart 12 Portfolio evolution from January 2020 to October 2020
The portfolio adapted very well this year as well and for this reason, it clearly beat the S&P 500 benchmark (Chart 12). Since this is a recent past that we all still remember honestly, let's take a closer look at the detailed description of the trades by month:
January - February 2020
Energy, cannabis, mining, gold, semiconductors were traded
March 2020
Most Profitable:
TLT - Bunds
GDX - gold miners
VXX - VIX ETF
USMV - stocks with minimal volatility
EDV - treasuries
Top Performers
Semiconductors
Corporate Bunds
Bonus:
DRIP - short oil x3**, Gaming, Cannabis, Healthcare, IT
April - October 2020
Energy, Real Estate, Aerospace, ARK ETFs
Summary
Finally, we will try to make a summary of our series and precisely the strengths, weaknesses, opportunities, and potential threats that characterize the strategy:
Weaknesses
Higher volatility (at the cost of higher returns)
Monte Carlo analysis: Maximum drawdown of up to 30.50% with a 95% confidence interval
2 years of stagnation between 2015 and 2016
The standard deviation of annual returns 23%: Higher variability
Strengths
Potentially high returns
Systematic trading in the form of algorithms: automated execution
Strong statistical advantage verified in global markets going back decades
Futuristic portfolio manager
Coverage of the full spectrum of global markets
Great portfolio trading in crisis years
The huge statistical sample of trades (44755)
Threats
History may not repeat itself (but there is a strong statistical advantage on the strategy side)
Failure of the client to handle inherently volatile outcomes
Inability to manage periods of natural stagnation
Opportunities
Live results from May 2021
5-year investment horizon (strategy should generate new peaks faster than traditional assets)
Possess a unique and intelligent investment strategy that no one else has
The transparent contractual relationship with no administration fees in the first year