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 He sends the money to a collection account with Raiffeisenbank, a.s. After the funds are withdrawn, the client receives a confirmation of payment directly from the bank statement.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
Conclusion
This study is intended to serve as a guide for our clients on the natural behavior of the strategy. Live then trading from May 2021, interim results updated daily can be found here.
It should be said that historic results are no guarantee of future results. But there is no financial product that can give any warranties, although you would certainly like to hear that. But we can safely conclude that:
We have developed a competitive investment product that uses the latest technology in the form of artificial intelligence and is also based on statistical advantage and common sense.
The strategy held up well in the crisis years of 2008 and 2020 when it was even more profitable than in the years of economic growth
But it has also had phases where it has been similarly or slightly less profitable than the benchmark S&P 500
Today's times require flexibility and constant adaptation of these ETFs, ideally daily, as the strategy can do.
The portfolio changes every single day and executes a significant amount of trades across time. This is supported by evidence of robustness in the form of a large statistical sample. This is something a traditional portfolio manager would not be able to accommodate.
Automation enables precision in executions and adds the necessary order, which is provided by the ML AI unit. This unit is not affected by the negative emotions of fear and greed. It makes decisions practically and efficiently based on growing significantly in the given periods. Moreover, since it is only 70% exposed to the markets, its results can be incredible against stock indices.
The strategy is also burdened by commission fees, exchange fees, and, most importantly, our performance fees (20% high watermark). It is thus automatically at a disadvantage compared to the S&P 500. It, therefore, has to be highly efficient and its predictions very accurate.
Our strategy is the ideal partner for the next decade, which is threatened by high inflation. Therefore, other assets than equities may be more attractive in terms of returns.
To learn more about the deep historical price trends of the most important underlying assets such as stocks, gold, cryptocurrencies, bonds, and real estate, read the educational article below.
It may help you that our investment strategy described above is right for you:
How can we protect ourselves from rising inflation and a possible economic crisis?
Do you really like your savings? Do you value them? Have you already invested somewhere, are you doing well so far, and feel that nothing bad can happen to you? Well, millions of investors before you have had the same feeling and ended up broke. History speaks. Clearly, we just often ignore it. But today, do something for yourself: Give it time, and by the end of your reading, you may finally see the light at the end of the tunnel in a world of inflationary pressures. And if it's about protecting your hard-earned money, it's certainly worth it. Be honest with yourself and admit that:
You have a bigger money problem than you admit
Let's face it, your savings are depreciating every day with inflation accelerating. And with that comes stress and a fundamental question:
How and where to invest your hard-earned money to fight inflation?
We live in an era often compared by the greatest investment matadors like Warren Buffet, Ray Dalio, and Michael Burry to the mania of the 1920s. You know all too well what happened after 1929. After the initial panic of the COVID-19 pandemic, every asset you can think of literally skyrocketed. Tens of percent appreciation per year is no longer a shining exception but a dogma. And always in history, when there was unbridled optimism among investors, the stock traders showed their huge success. But remember: Those in positions in 1929, 1987, 2000, 2008, and 2020 could tell the story. One thing is sure at this time.
Virtually all assets are overvalued, and markets overheated. But not everyone will have to be a loser. The prepared and educated ones will walk away as winners for the next decade ahead. They will be charged with the vision of long and steady growth after the crisis. We would liken this to the purchase of Microsoft stocks in 2002. Today, in retrospect, surely everyone would have wanted to buy it at the time. But when it was 2002, and there was fear in the markets, it wasn't easy for an investor to have the time and courage to buy the stock. And that is always the case in times of crisis. So how to be among the potential winners? If you're looking for answers, read on patiently. You now have a good foundation because you already know:
The fundamental question of our days is:
How long will stocks, cryptocurrencies, and other assets continue to rise before the bubble bursts? Unless you have a crystal ball, you won't know the answer. But the important thing is that you admit you have a problem: What to invest in during these challenging times? If you want a solution, take the time to read on.
Please note, however, that you will not find the Holy Grail here, nor will you find a quickie. We come up with a strong statistical advantage. You'll find a scientific approach to trading with us, a professional investment product backed by complex data. We stand behind it and are duly proud of it.
But first, a little bit of history, so you understand that you can not ignore history. Let's go in order and discuss the individual underlying assets (Fig. 1).
Fig. 1 Underlying assets you can invest in as an investor
The biggest mistake is to think that history does not repeat itself
Let's start with what many consider to be security and inflation protection. Gold. Maybe you'll change your mind about it...
Gold
Gold is undoubtedly the most famous commodity. After all, every one of you has come across enticing marketing slogans like "Gold: A safe haven in times of uncertainty." Well, in the 1980s and 1990s, you would hardly have thought so (Chart 1):
Chart 1 Gold price between 1970 - 2021
In February 1970, the price of gold per troy ounce stood at USD 36.56. The 1970s were characterized by very high inflation, especially due to the oil crisis that erupted in 1973. Gold was a real safe haven at that time, and its value rose very sharply except in 1974 when it made a minor correction. After that, it continued to rise very sharply in price until January 1980, when it reached USD 677 per troy ounce. By the way, inflation in the US was around 13%! People were being pushed to invest their savings somewhere. But there was a recession, and it was challenging to find quality companies to invest in. So, logically, gold was on offer. It already had a decade of extraordinary growth under its belt. Those who succumbed to the gold rush, bought late in the last years of this decade, watched the price collapse for over 27 years until September 2007. Only then did they get back to the original value of their investment. (See Chart 1). Unless they ended up in the grave long ago.
The biggest fallacy of today's investors, then, is the assumption that such a situation may not happen again in the future. Moreover, it is highly questionable whether gold will work as a hedge against inflation in our time when we have long since gone off the gold standard. There have been fundamental structural changes in the global economy. The range of possible and available investments in different underlying assets is much broader than it used to be in the past. You should consider all of the above before investing more than is prudent in gold as a safe haven.
Stocks
From recent history, many consider stocks to be a safe investment. Yet, they don't. From 2009 to March 2020, they experienced the most successful length and returns in recent decades. Without exaggeration, growth to heaven (author's note: Or hell?).
In February and March 2020, the most crucial stock index, the S&P 500, plunged by more than 34% due to the COVID-19 pandemic. Only to create a new all-time high in late August, making up for the decline in the shortest time in history. The entire investment world was left in mute amazement. Insane growth in a situation where the pandemic rages on, and the world is in permanent uncertainty with disruptions in supplier-customer chains leading to very high inflation? No, this is not a joke. This is the reality of these days and the loose monetary policies of national banks. It is understandable then that many see equities as an endless money machine churning out golden nuggets. But what about history (Chart 2)?
Chart 2 S&P 500 between 1970 - 2021
So, for example, if you succumbed to the dot.com bubble in August 2000 and bought the S&P 500 index in the form of an ETF, you did not have good investment years. The stock index value did not return to its peak until August 2007, but it did not stay there for long, as the mortgage crisis was coming on in October 2007 and peaked in March 2009. It then safely surpassed the 2000 level in March 2013 and has continued higher to date. So those who invested at the peak of the bubble in 2000 only really began to appreciate in value after 2013, 13 years later, and that, as you will admit, is a very long time to go crazy. This period has put countless families, not just American families, into existential crises, which is the saddest part of the story.
What does this not-so-distant history imply? We need to be cautious and invest in an investment strategy prepared for the next crisis. But before we introduce it, let's look at real estate, government bonds, and finish with cryptocurrencies, represented by bitcoin.
Real Estate
There is an opinion among most people that real estate prices cannot fall. They will only get more expensive... As you will see from the following example, this may or may not be accurate. It depends on many factors that nobody can predict in advance.
In Chart 3, you can see the evolution of the average selling price of homes in the US since 1970. The data can be considered relevant as it comes from FRED - Federal Reserve Economic Data, part of the US Federal Reserve.
Chart 3 Average Sales Price of Houses Sold for the United States - 1970 - 2021
If you succumbed to the housing fever in 2007 and took out a very easy to secure a mortgage, the property will get a new high in 6 years in 2013. When you bought a house in 1989, you had to wait another 5 years for new price highs. What does that say about the real estate market?
Fearing that the 2007 and 2008 scenarios in the USA will not be repeated, most central banks are tightening the conditions for taking out mortgages. Young families cannot even afford to take a mortgage, let alone pay for the entire property (unless they have parents who are secured). These are, in short, variables that you have to take into account when projecting possible prices in the future. Everything is determined by supply and demand. Of course, it can (and in the long run it will) continue to get more expensive, but you really don't have and never will have a guarantee of endless house price increases. On the other hand, the assets analyzed, together with government bonds, offer the highest stability (at the expense of lower yields, it should be noted).
Government Bonds
Hit by very high inflation in the 1970s, central banks reacted by rising government bond yields, as you can see in the example of the interest rate on 10-year government bonds (Chart 4).
Chart 4 US government 10-year bond yields 1970-2021
Thus, at that time, savers could hedge against inflation with the safest investment ever because the state was the creditor, and it had historically fulfilled its obligations. In 1980, for example, the yield on 10-year government bonds was almost 16%! Unfortunately, we are in a very different and much more difficult situation today. Government bond yields are at historic lows. Although most governments also offer inflation-covering bonds, there is a catch. Have you looked into how the official inflation figure is calculated today, then used as a basis? The CSU defines the inflation rate as increasing the average annual consumer price index. For example, the consumer basket in the Czech Republic from which inflation is calculated includes food goods (food, beverages, tobacco), non-food goods (clothing, furniture, household goods, drugstores and sundries, transport and leisure goods, personal care goods, etc.) and services (repair, housing, household operations, health, social care, transportation, leisure, education, catering and accommodation, personal care and financial services). This composition may vary from country to country. Still, the rising prices of real estate and materials in the Czech Republic are not included in the consumption basket.
As you can see from this straightforward example, government bonds have a catch. They certainly don't cover real inflation, but they cover paper inflation. So, where to invest? To keep money in accounts from losing value, we are forced to invest not in bonds as security, but in the risky assets, we have already mentioned. By far, the riskiest (but it must be added the most profitable in recent years) has been investment in many cryptocurrencies, especially Bitcoin and Ethereum.
Cryptocurrencies
In the age of bubble inflation, the general public always thinks that a given asset will continuously grow skyward. Therefore, we are faced with the fundamental question of what will happen in the coming years to the world of cryptocurrencies, represented by the most famous Bitcoin, with Ethereum already breathing down its neck. The value of Bitcoin has risen by literally hundreds of thousands of percent since 2011. The problem is a question asked by some of the most sophisticated investors who are miles away from the crypto world:
What is the intrinsic value of cryptocurrencies?
It's a very tricky subject. Everything is virtual. Cryptocurrency advocates, for example, say that since the gold standard was abolished, the U.S. dollar and its associated Western world currencies have virtually no value. But the problem is that it is the official tender that can still buy a house, a car, and other consumer goods. We're not in El Salvador yet. Instead, central banks and governments are trying to fight the world of cryptocurrencies. Strict regulation is the only solution, but a complete ban can never be ruled out. China is a clear example.
Another problem is that, in the age of trying to save the planet, the vast amount of energy used to mine cryptocurrencies is being increasingly addressed.
But really, the most considerable risk for investors is the tremendous volatility, which has virtually no limit. In a matter of days, investors can lose most of their investment, and the historical development of cryptocurrencies has shown this many times.
The world of cryptocurrencies is becoming more and more correlated with stock indices. Investors are simply investing in it in the same way as in stocks. It is beginning to happen that in volatile periods its value falls in the same way as in stocks. So would you divide your basket between stocks and cryptocurrencies and be safe in an economic crisis? You can forget about it.
What conclusion to draw from this? Do cryptocurrencies look like a bubble that will burst? One would believe, based on historical experience, that they do. However, if cryptocurrencies become part of our everyday lives and function regulation, their value could grow further. One thing is sure. No asset will not go through a crisis period that will see its value fall for many years to come. You can see in Chart 5 that Bitcoin started to fall between 2014 and only returned to its peak a full 4 years later. But the undeniable fact is that it has been shown that it can make new and new peaks for many years. And suppose people continue to believe in its intrinsic virtual value (and other cryptocurrencies). In that case, we can expect to see new peaks continue. Of course, the question is with what kind of price drops. With volatility this high, no one could be surprised to see even more than 90% declines. However, blockchain technology, currency stacking, DeFi, stablecoins are all compelling digital technologies that bring a fresh wind to the shabby world of finance. The question is how governments will respond to them. As we know, China has banned cryptocurrencies, and India is about to do so. These are the variables that need to be reckoned with. Many advise investing in cryptocurrencies up to a maximum of 10% of total assets due to the erratic volatility. Even to us, this advice seems sensible.
Chart 5 Bitcoin logarithmic price scale from August 2010 to December 2021
Diversification
It is no exaggeration to say that the phrase "portfolio diversification" is the most widely used term in the world of professional investing. Diversification is a business strategy that seeks to reduce risk by not relying on a single product. By spreading its activities across different areas, its assets across multiple companies, currencies, etc.
If we humanize this definition, we can express it: "Do not carry all your eggs in one basket." A professional investor's primary goal is to protect money first and then make money. Numerous diversification techniques are expressed in mathematical equations (for example, Markowitz and his portfolio theory). There's not much point in wading through them. It would only delay you. We prefer to focus on simple logic.
We have presented the historical price history of gold representing commodities, equities as expressed by the major stock index, the S&P 500, average house price sold in the USA, 10-year US Treasury bonds, and Bitcoin from the world of cryptocurrencies.
We omit Bitcoin due to a lack of data. We allocate the portfolio in the same proportion - that is, a quarter to gold, the S&P 500 stock index, government bonds, and real estate. So let's take a look at how diversification worked for us in this case (Chart 6):
Chart 6 Portfolio diversification
Since 1970, you would have achieved a very attractive appreciation of around 3336% with reinvestment of funds, that's certainly fine, but for example, by investing purely in the S&P 500, you would have achieved a higher appreciation, specifically 4971% and in gold a very similar 4926%. This is followed by 10-year Treasury yields at 1874% and US real estate at 1572%. When we then look at the overall portfolio, while it is less profitable than gold or equities, it still offers greater diversification in the form of stability. There's a catch, though:
Even with this even allocation of capital among these 4 core assets, you will experience long years waiting for appreciation (Chart 6). So, for example, in 1980, you would wait 7 long years for a new peak. Then from 1999 to 2003 (it should be said here that such a diversified portfolio would have greatly helped equity investors who had to wait a full 14 years for a new peak). Then 2012 to 2017, a full 5 years. So let's pour ourselves some clear wine: Yes, diversification certainly helps, but even that won't guarantee every single year of positive returns. And that's what motivated us to find our own investment solution that responds dynamically to changing global markets and, if possible, is profitable over a very short investment horizon.
Summary
So let's summarize. We are in a difficult situation:
Inflation is the enemy of our wallets. Our savings are depreciating in savings accounts.
Investing in government bonds is questionable because real inflation is much higher than reported. Bond yields will only partially protect your savings. Central banks do not react and raise interest rates as they did in the past. They are tied to rising sovereign debts, and raising rates would risk making countries insolvent and bankrupt.
Central banks are transferring the problem and responsibility to savers. We are being pushed more and more to invest our savings in risky assets such as shares, real estate, gold, and cryptocurrencies. But as history shows, in severe financial crises and panics, most underlying assets behave similarly. People have to sell them on a massive scale, and they fall in value. So diversification is out of the question when volatility explodes in a short and intense period. All assets will suffer. Such is the law of panic and supply and demand crises.
Equal diversification between stocks in the S&P 500, gold, government bonds, and real estate is undoubtedly better than betting on one card. On the other hand, as we have demonstrated, you can experience years of downturns and stagnation even with this option.
So the fundamental challenge is to find a solution that provides better stability in the investment while making its returns more attractive than, for example, bonds. The ideal scenario would be to have an investment strategy that ensures returns even in crisis years.
Solution
We are a strong team of investors who have been involved in the markets for more than 20 years. We have chosen not to become victims of inflation and not put our savings at risk. At the same time, we do not want to be beaten by the next economic crisis, which is sure to come. It is only a question of time.
On the contrary, we want to be among the winners if the economic crisis comes in full force. That is our dogma, and that is what we follow. Our philosophy of life. The greatest challenge of all. We realize that to protect our families, our assets, and our entire future, we need:
To create an investment strategy that will benefit from stable economic growth and bring capital protection in the turbulent and crisis times of the coming economic recessions.
It sounds simple, but it takes a lot of knowledge and experience to achieve something like this. You need to find and take full advantage of lasting statistical advantages in the markets. Advantages are so substantial that they will stand up to any tough test. We have been looking for this for virtually our entire adult lives. It took us more than a decade of working together to finally discover our permanent statistical advantage. Our solution that no one else has. Our baby, an algorithm that we are duly proud of and trust. We've tested it first-hand. We're taking it out into the ossified and slowed-down world of traditional mutual funds that will just tell you: "Sorry, everyone loses in financial crisis." It's not true. Remember, there are only winners and losers in a crisis. That is the law of financial markets. We have the same responsibility to you as we have to our families. Our company was created primarily to protect family assets. Only when we verified that our investment strategy was trading as expected did we offer the investment strategy to other educated investors.
Cooperating with us, you will quickly understand that we bring change, a fresh wind. The courage to deliver an unconventional but functional and unique solution. A Futuristic Portfolio Manager that uncompromisingly seeks out and trades daily those stocks and ETFs (publicly traded funds) that are the right choice at any given time. The Futuristic Portfolio Manager is driven by an artificial intelligence unit that can evaluate vast amounts of time series. This unit learns and trades a broad portfolio accurately without emotion. This way, it ensures a real high-quality diversification. In short:
We have a solution that is here to permanently protect you from real inflation