By Andreas Roell


I have been lucky enough in my career to be part of new phases of innovations in long-established industries.  At the start of my career, the Internet first emerged and the dot com companies began appearing. Then, I founded one of the first digital marketing agencies during a time when advertisers put all of their money into TV or radio commercials.  Later, I incepted one of the first VOD platforms in the Middle East when the entire Arabic World was glued to TV screens.  All of these experiences had several common themes, but the one I would like to highlight today is the fact that individuals (i.e. consumers, users, customers, etc.) find it very difficult to categorize a new offering in relation to what they are familiar with.


Fast forward to today and I am having the same experience in the asset management and hedge fund world. As you probably know, AlphaTrAI is an AI-based asset management firm. In our many conversations with investors, I have noticed a natural tendency and desire to label us somewhere within the categories that they are familiar with.  Of course, this is not the first time a firm similar to ours has arrived on the scene. It happened when technical traders started to arrive and also with the advent of quantitative funds.


As a result, a clear explanation of how we classify into other categories of hedge funds has helped us make faster progress. So, I thought I would share with you how we categorize the various hedge fund offerings.



I recognize that this might be a simplified view, however, I have learned that a simplified approach to a more complex discussion makes a lot of sense.  Since the space is constantly evolving, it would be great to get from you any ideas to further build this out. Who knows? Maybe the power of social media makes our collective end product a “standard” or commonly adapted communication tool.


By Katherine Paulson


As a marketer in the financial industry for over twenty years, I have experienced many challenges and limitations due to the highly regulated environment.  Although I often envied my professional peers who had tangible “products” that they can see and touch, at the end of the day, I was happy to meet the challenge.  Namely, because I wanted to simplify and bring transparency to the complex nature of financial products.  I’ve always felt a deep responsibility to my audience because they were making real-life choices with their hard-earned savings that could impact their retirement and financial future.


Entering into the world of private investing and hedge funds adds another level of complexity and scrutiny for marketers.  In fact, prior to September 2013, hedge funds were not permitted to advertise at all. Click here to read more. Now, however, with the signing of the JOBS Act, the ban on general solicitation for certain funds has been lifted.  This change creates many new opportunities for hedge fund managers like AlphaTrAI to bring transparency and education to our clients.


When you factor in the trauma of the financial crisis in 2008 and the recent revelations about the impact of social media on retail investors, the financial industry has woken up to the importance of their brand and messaging.  We more clearly realized that building the public’s trust and reputation was paramount.  In an industry dominated by analysts, mathematicians, and regulators, this was not an easy realization.  Marketing often took the back seat in priority, but no longer is this the case. 


It is an exciting time because these new perspectives and rules have opened up opportunities for hedge fund managers to spread awareness of our industry.  We can now promote using what many other products take as a given, including print and digital magazines, newspapers, TV, podcasts, websites, LinkedIn, Twitter, Facebook and other social media platforms.  Click here to read more about rule 506(c)


This new freedom enables stronger communications between our potential investors.  While we understand that hedge funds are an exclusive market, they are still an important part of the global financial ecosystem.  This is why it is important to keep in mind the audience we are messaging to is clear, honest, and specific.  


I believe we are on the verge of a breakthrough in hedge fund marketing.  Much like our products, the marketing of hedge funds is emerging.  At AlphaTrAI we are in a position to both disrupt the asset management industry with our AI-enabled fund as an emerging asset class and in our marketing methodology.  This moment offers us the opportunity and responsibility to connect with our potential clients as we never have before.


At AlphaTrAI, we strive to provide the most reliable, sustainable, and insightful algorithmic-based financial products.  I’d like to highlight the last part of our vision.  Bringing insights, providing education, transparency, and awareness in an innovative way – these are the tenets that we work toward achieving.  I remain optimistic and passionate about the future of hedge fund marketing, because so much progress has been made.  As marketers, we will need to seize upon this opportunity as pioneers in influencing marketing methodologies going forward emphasizing education, awareness, and transparency.


By Andreas Roell


In part 1 of my post, I left you with a question.  What would be the lasting impact of these unprecedented recent stock market events we just experienced?  It is still uncertain if this is a blip or the start of a new market dynamic.  


I did predict a strong likelihood of regulatory scrutiny which started last week with the new U.S. Treasury Secretary Janet Yellen, calling a meeting of key financial regulators to discuss market volatility driven by retail trading in GameStop and other stocks. I believe this is just the beginning of scrutiny from the regulators that will continue.


Back to my question regarding market dynamics, we have all witnessed the power and influence of social media and its ability to turn politics upside down, and we can now clearly see it can do the same for the financial markets. 


Social media sentiment and trend analysis is one that has been around for some time, but we will likely hear more about it as a shiny object for algorithmic / NLP traders.  Rest assured, this trend is something that will be discussed heavily throughout this year and beyond.


What I am more interested in is monitoring how much of a fad this scenario is or not. A couple of key points here:


  • I still believe that retail investors will ultimately get hurt by this directly or indirectly.  The same “mainstreamers” that are taking down the big hedge funds have their 401(k) and retirement savings invested with them. 


  • The more volatile markets become (for me volatility simply means more confusing and less systematic) the harder and frustrating it will be for retail investors which will translate into frustration and heartburn.


Instead of heralding a new wave of investor populism, the rise and fall of GameStop’s stock may end up reinforcing what the financial industry has known for a very long time.   Namely, the fact that they have infinitely more access to tools, data, and resources than the average retail investor.   Click here to read more about who benefited during the Reddit trading frenzy.


At AlphaTrAI, we do not solely rely on the historical data because it is fragile to these specific and unpredictable events. However, our algorithms daily monitor market events and we remain steadfast in our approach to have a high appreciation for tail risk events. 

By Andreas Roell


In light of the recent, unprecedented circumstances surrounding the U.S. equity markets, I wanted to provide you with my perspective and insights on how our algorithmic approach differentiated us.


Before I go into the specifics of our algorithmic models’ autonomous decision making, I encourage you to read this great explanation and analysis in The New York Times click here.


This retail investor mobilization has exposed some phenomenal, sometimes hidden dynamics of our financial systems. First of all, the magnitude of retail investors has grown (with the help of the stay-at-home mentality of the pandemic) to a record 25% of the stock markets’ activity. This is up from only 10% in 2019. Contributing factors include the wave of no-fee, direct trading platforms, such as Robinhood or Fidelity, and the free-flowing group dynamics of social media. I learned an interesting tidbit during a recent conversation I had this past week with a long-standing member of the now-famous Reddit thread r/wallstreetbets, who told me that the GameStop cry for long positions started back in 2020 without aiming to mobilize as a “let’s go after the evil hedge fund shorters”. It was simply a single post calling out detection of a high short position that was made. It reminded me of my early days in social media marketing, where I frequently referred to the video from the 2009 Sasquatch music festival (see here) that shows the power of group dynamics. 


The convergence of these multiple types of dynamics is truly unique to what just happened last week. Since there has never been a period in stock market history like this, one is faced with the challenge where the previous playbook that typically analyzed historical data for decision making cannot be applied to find the best decision process. 


At AlphatrAI, our algorithms immediately classify such an occurrence as what is known in the science world as a “non-stationary time-series” data event. It simply means that a stream of event data that occurred through the course of this “retail revolt” has never been seen before in stock market history. So all traders, including quantitative, technical, algorithmic, and even human traders, who solely rely on historical events to drive their present investment decisions, were at an absolute loss this week. We saw this with Melvin Capital, who manages a human-based short trading hedge fund with a phenomenal track record. In my summarized perspective, they were completely caught flat-footed and did not have a hedging or counter game plan when the retail long traders started building momentum. Such reliance on a single “signal” strategy is something that we frequently talk about with the investor community. A sustainable, long-term viable investment strategy must be one that does not rely on a limited signal strategy and must be agile enough to adapt to new “game” situations.


These events highlight that the majority of funds are based on portfolios designed to deal with frequently observed, small downturns in the market. This approach is necessarily fragile to the large rare risks (so-called tail events) that can occur over small to large time horizons.  Here is a brief description. These are the events that matter most when maintaining sustainable performance in the financial markets. The high appreciation for these tail risk events is a fundamental invention of our models. We use Machine Learning to leverage information as effectively as possible. We use this information to construct portfolios that we believe are designed to achieve gains while managing tail events that can be catastrophic for other portfolios. If you have not had the chance, I encourage you to read our white paper here, which discusses the depth of the negative impact on a portfolio from such events and AlphaTrAI’s view on portfolio construction. 


How do we strive to avoid catastrophic losses?

  • Ensembles of models that diversify not just our exposure to asset classes but also diversify our exposure to behaviors/models/strategies.
  • Dynamic models that assess risk and reward balance daily and automatically adjust exposure to the market.
  • Evaluating and selecting strategies based on probabilistic models of their extreme (tail) risk characteristics.



The lasting impact of these recent stock market events is uncertain. Nobody knows if they are a blip or the start of a new market dynamic. However, I strongly believe that we have not seen the last in terms of regulatory scrutiny. A future change in trading is likely, whether it is limitations to short trades, reduction of full market access to retail investors, or others. What I can tell you now is that this “unprecedented” or “non-stationary” event will not be the last we will have in our lifetime. 


The stock market is dynamic, it is riskier than what the majority of investors think. It requires an ability to manage risk as much as possible while making decisions for a brand new scenario as quickly as possible. Here at AlphaTrAI, we continue to see how successful models can navigate through this incredibly difficult situation. All in an autonomous fashion. I feel fortunate to have launched our fund at a time that has given us the chance to prove how the models perform during some of the most difficult times in stock market history. Last week (and maybe this coming week, and beyond) was just another proof point that we are proud of. 

By Andreas Roell


Last week, I had the pleasure of speaking on a virtual panel to an audience with a mix of quantitative and traditional asset managers and advisors. The conversation very quickly shifted to the topic of how to make allocators and investors comfortable with algorithmic investing versus what they are currently doing. 


As our discussion progressed, it occurred to me that there is a good deal of work to be done to help bring allocators and investors along the educational journey to give them a confident foundation of how algorithms work and what questions to ask.


However, what I became more curious about is the level of questions or diligence that algorithmic/quantitative asset managers go through in comparison to traditional asset managers. Obviously, it is our responsibility to increase credibility and time will help in driving comfortability among investors. At the same time, however, I feel that there is an opportunity for algorithmic asset managers like ourselves to take a look in the mirror when exposed to perhaps imbalanced scrutiny.


Here is an example of what I am referring to. In a recent conversation with a fellow asset manager, I was asked questions specifically around our algorithms, such as: How do they work? What are the specific data sets that are used? What market triggers drive decisions? What are the specific signals that make our models work? Among others.


I tried to answer them as thoroughly as I could without giving away trade secrets and felt pretty satisfied with my answers, but one never knows, as some of the questions cannot be simply answered without getting into details.


Since he was a fellow manager of a hedge fund (over $200mm in AUM) who focused on “human based” investment strategies, in this case a sector specific pair trading approach, I started to turn the tables on him and began asking him counter questions.


Questions like:  What are some examples of pairs that were successful for him last year? How does he typically identify a pair? How long does a pair signal last for him?  


A couple of his responses were surprising. One was, “You are asking me more detailed and technical questions than my investors.  Neither seed or institutional investors have ever asked me these questions in my 30 year investment career.” Second, “I am not able to tell you all the details as I would give you my recipe.” Funny, I thought, since these are questions I face in every conversation that I have with investors.


This contrarian perspective was validated a week later when I talked to the head of all products at one of the most well known financial institutions in the world. He as well appeared to start “geeking out” on us with curiosity the longer our conversation went. Here again, once he was done firing question bullets at me, I asked him simply if these are the types of questions his clients ask him when deciding or choosing from their offered menu of investment products. The clear answer was a laughing no. 


I started thinking about why this is the case. Why is there so much more interest, diligence or simply curiosity in the detailed mechanics of investment decisions when it comes to algorithmic trading solutions than we have with traditional fund offerings?


It reminded me of my digital marketing days, when I started my first agency in the late 90s and helped a large portion of leading hotel companies use the early days of the internet to generate direct reservations. There, I had to compete for media budgets with the traditional formats, such as television ads. During that time, I just could not understand why every dollar I spent on an ad for them was held accountable to revenue generation and TV had no clue at all about their impact. Fast forward to now, and this perspective has changed in both directions. TV has lost a significant amount of their media budget share, while also being tracked as much as possible. While on the other side, digital advertising has become also a medium for immediate revenue generation (what is called Direct Response), but also for awareness and branding purposes.


So, the moral of the story here is that over time in advertising, marketers learned from both sides and applied layers from one to the other. This is something that I predict will happen in the asset management industry as well over time.


“Traditionalists” will become accustomed to higher scrutiny and detailed questions about their investment decision process, while the acceptance of algorithmic-at-the-core traders will increase.  The level of understanding and comfort around proprietary decision making processes will increase. And as such, the algorithmic side of the coin will receive less scrutiny.  This is at least my prediction. 


Here at AlphaTrAI, where we are obviously an algorithmic asset manager, we believe that we have an obligation to increase the level of understanding and thus comfort that investors have at this point in time with algorithmic investing. 


We aim to work hard first, at bringing more knowledge of this new approach into the investment community, and at the same time, we know that we need to show up as transparent and less secretive as much as possible. It is our belief that this is the obligation of all algorithmic investors. The more we do it, the better our collective reputation and investor willingness to shift to this new form of asset management will be. Just like what happened during my early days in digital advertising.


Happy AI investing.

By Karyn Williams


Most of us take for granted that asset managers deliver services to investors through investment products. Whether actively managed to pursue alpha or passively managed to track broad indexes, asset managers sell, and investors (advisors) buy them for portfolios. There are over 10,000 such products around the globe competing for a place in your investment portfolio. With advances in artificial intelligence and data science, this is about to change radically. 


Advances in computing power and the availability of real-time data on virtually every aspect of the global economy have changed the investment game. No matter how astute any human fund manager is, no person or team is as capable of processing, prioritizing, and analyzing all of this data as a set of well-calibrated algorithms. 


This state of play puts immense pressure on active asset managers. For those whose processes have not changed appreciably over the past ten years, alpha is increasingly hard to produce. They will have to innovate or otherwise compete with large fund complexes that offer nearly zero cost passive products.


Adding to this pressure are low yields. An investor who targets 5% for his or her investments to support for future goals and wants to keep pace with 2% and cover investment fees of 1.5% has to earn over an 8% annually. But with 10-year Treasury yields below 1%, as of this writing, investors have difficult portfolio choices to make: lower the overall cost of investing (by moving from active to passive products), increase the risk of the portfolio, change the investment objectives, or all of the above. 


A traditional investment product that fits within an asset class will not fill the return gap. They usually comprise between 1% to 5% of a portfolio, so even with expected outperformance it will have a small overall impact. A multi-asset product also may not help. Off-the-shelf and one-size-fits-all, they are designed for an average investor and do not necessarily fit with the rest of the portfolio. Private market and quant products may not help either, presuming the investor can get access to the best products. Not only are fees high, but the potential for large portfolio losses can actually lower long-term portfolio returns. 


Rather than traditional investment products, investors need solutions, and this is precisely what AI and data science can provide.


AI techniques can improve how we measure and manage market risks compared to traditional approaches. Applied to the investor’s challenge, an AI-based solution that maintains a portfolio’s exposure to equity markets while moderating large declines in value, can improve investor outcomes. AI also can better systematize data, interpret investor profiles, and control certain decision biases. An AI-based solution that is designed specifically for an investor’s unique risk capacity and can control biases can further improve investor outcomes. 


To sum up, the next generation of AI-enabled investment solutions not only better measures and manages portfolio risks, they are customized to serve each investor’s specific needs. At scale, AI-based solutions replace the traditional products we see in the market today.


Arguably, one of the roadblocks with the adoption of AI across the investment management industry is attitudinal, with the fear of AI either displacing advisors or making investment decisions that investors do not understand and cannot monitor. 


Approached correctly, with understandable algorithms that are aligned with client objectives and free of conflicts of interest, AI can help investment professionals to serve their clients better. AI frees them to focus on what their clients care about – achieving investment objectives. It replaces complex conversations about the latest products with overall value creation. By creating meaningful value, advisors become even more essential and clients feel confident.


AI and data science can drive a profound and positive transformation for investors. Better risk measurement and management, customization, and a holistic approach will upend products in favor of solutions, driving considerably better outcomes and empowering investors to invest more confidently.

By Katherine Paulson


This week’s blog is an introduction to some of the basic terminology used in Artificial Intelligence (AI) and their definitions.  As someone new to AI, but a veteran of the financial services industry, I made an effort to start learning quickly about the basics when I arrived here at AlphaTrAI. Once I understood these terms, a new world of asset management was open to me and the exciting possibilities that came with it.  


Although it seems like AI terminology just recently started appearing online and in social media, it’s not new, it has been around for decades.  In fact, the term “Artificial Intelligence” was coined in 1956 at Dartmouth College.  The reason for AI’s incredible growth in the last decade is because of these key developments:  


  • Computational Power
  • Access to Algorithms and Libraries
  • New Research
  • Access to Data


The growth in AI investing started in 2011 and continued its upward trajectory with a big acceleration between 2015 through mid-2019 when VC’s invested $60B in AI related startups. 

The Basics


When thinking about AI terminology, let’s begin at the top, Artificial Intelligence, Machine Learning (ML)  and Deep Learning (DL).  Artificial Intelligence encompasses the entire scope, Machine Learning is a subset within AI and Deep Learning is a subset of Machine Learning as illustrated here:

In the simplest terms:


Artificial Intelligence = Computer is doing something human

Machine Learning = This computer is learning

Deep Learning = This computer is learning in a specific way


Two types of Machine Learning


Now, let’s move on to Supervised Learning vs. Unsupervised Learning.  The main thing to remember in Supervised Learning is that it is all about labeled data.  In Supervised Learning, imagine a manager who oversees all of the data and tells the computer exactly what that data is.  The computer is fed with examples, directed to determine what is correct or incorrect and told their differences with the end goal being able to train it to identify its features. Features are attribute information you know to be important for making a prediction. Then, when new data that has never been seen before is introduced, based on the model that the computer created (think of a model being a shortcut to a pattern the computer saw), it creates an output with a prediction of what that new data is.  An example output may be expressed as “65% confident”. 


In Unsupervised Learning, you guessed it, it is the opposite – there are no labels, no manager and no direction.  The computer or system is provided a data set and it organizes the data in its own way.  For example, a popular algorithm in Unsupervised Learning is “clustering”.  Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups.  Clustering algorithms are popular for customer segmentation.


Going back to the basic definitions of AI, ML and DL, remember AI can be described simply as the computer doing anything human-like that can range from autonomous cars to a very sophisticated set of if-then statements that recreate human behavior.  Within AI is ML which is most of what we read and hear about.  So, to differentiate ML vs. DL, we need to distinguish what separates DL.


What is Deep Learning?


Deep Learning is specific to its structure that mimics the human brain, also called Neural Networks.  Neural Networks are set up to model how humans process information.  They are generally more computationally intensive than traditional ML.  Artificial Neural Networks (ANN) can look as simple as one input layer, one hidden layer and one output.  However, Deep neural networks have more than one hidden layer, that is what separates them.  Each hidden layer is a specialized function.  These multiple layers or depth allows you to create much more abstract features that can be extracted from the upper layers which enables the improvement of the overall flow of the network to classify something, an image for example.

To describe this in a simple way, let’s use an analogy.  A person with very poor vision with a prescription of -15 looks at an image and sees a complete blur.

Then, someone hands them a pair of glasses that improves their vision to -7.  Now, they start to see some clusters of pixels in the image.

Next, someone hands them a pair of glasses that improves their vision to -3 and they can determine that there are ears, body and paws in the image.

Finally, they are handed another pair of glasses that allow them to have 20/20 vision and now they see clearly the clusters form an image of their favorite pet.

Deep Learning = Multiple hidden layers that “build” on each other to “extract” higher level features like “paws”.


What is incredible to think about DL is the ability to apply this layered learning to the abstract.  Business applications are boundless.


I hope this brief overview gives you a better understanding of AI’s basic terminology and the next time it comes up in conversation, you won’t miss a beat.

By Andreas Roell


The Merriam-Webster dictionary defines “Authentic” as both “worthy of acceptance or belief as conforming to or based on fact” and “conforming to an original so as to reproduce essential features”.  I caught myself using the phrase “Authentic AI” in one of my discussions and it made me think about this concept that I have been using for years.  What does it truly mean to me and others (yes – I am not the only one using it)? 


Contrary to the standard definition of the word “Authentic” above, the authenticity of AI, is really not a matter of “Fake” to be contrasted with “Real” but rather, about the essential features of AI which need to be acknowledged in a particular use case and what these “authentic” features are.


It does not necessarily mean that algorithms must be custom or built from scratch, if the applicability into a certain application is acceptable.  It simply means, in layman’s terms, that the models fit for a specific use case.


In order to reach the necessary level of requirements, it is essential to deploy a full immersion approach to a specific application or sector. In our case at AlphaTrAI, for us to truly deploy authentic AI, it was required to deeply understand, simulate and develop the necessary scenarios applicable to the highly dynamic and ever-unique conditions of the financial markets. Looking at one of my previous posts on the ever changing rules of the financial markets (you can find here), it should become very clear that off-the-shelf AI models for prediction and detection may function as a base for Authentic execution in financial markets but cannot be the solution. 


In my mind, these are the key building blocks of Authentic AI for financial markets:


  • Models that successfully function for prediction and detection based on very small data sets. This is due to my previous argument that markets cannot be predicted successfully in the long-term if the models are reliant on patterns created from long data periods. Markets are fast and ever changing.
  • Connected to the previous point, is to operate with models that are more “general” in nature when it comes to identification and comprehension of market conditions. Solely relying on models that function on predefined signals are not only limited in possibility of survival (At AlphaTrAI, we operate under the belief that each signal is like a star in the universe that changes in brightness or effectiveness and evaporates at one point) but also become very ineffective in fat tail market scenarios. Ability to detect market rotations in order to identify underlying dynamics of the market. So the answer to this very “tactical” form of algorithmic execution is to either function on, or at minimum, provide an “air cover” model framework that has the ability to recognize market regimes. Such a general concept of algorithms can be compared to an EQ element of a human’s decision making process, which we all have learned the older we get, enables us to make more rational situational decisions.
  • The ability for the algorithmic models to solve the entire problem versus only a portion of it. For financial markets this will mean that an integration of models should not only be able to decide what to trade, but must also have the ability to successfully execute the trades. A common shortcoming of back tests is seen when the models trained are not set up for slippage and executions for high volume positions, making the real trading results vastly different to these simulations. Another example is the integration of trading across a multitude of asset types or short-long positions.


Having been an operator for a while in the financial markets, I have noticed an acceleration of “AI based” financial offerings.  As a result, I see a significant amount of interest in AI strategies from both retail and institutional investors.  Where I am taking a leap is, and what my many years in AI have shown me, is that only an “Authentic approach to AI” can effectively work in financial markets. My findings on the current state of the majority of AI executions are such that are heavily dominated by human engineered rule-based systems, non-fully automated decision making and execution of models with a heavy reliance on a very narrow (often single) set of signals. 


So I encourage you to consider the authenticity of AI before making investments into an AI based investment product. Maybe even use some of my points as part of your due diligence list.


Happy AI investing.

By Andreas Roell


2020 has truly been an unprecedented year in all of our lives.  From the pandemic, to the historical election, to the way most of us just celebrated this holiday season unlike any other.  As always, unprecedented times come with significant insights and learnings.  This is especially true for the financial markets this year.  It will clearly go down in history as the most volatile, highest magnitude, fastest, news-driven and unpredictable market in history.


Many of the systematic, quantitative and algorithmic-based asset managers had to completely restructure how they have operated for years. See Renaissance Capital, as an example here.


Before I jump into some of the root causes of these troubles, I want to give you some fascinating data points. First, I asked individuals on our science team to provide me with some visual analysis of the year 2020 versus some previous years. We used a Pearson’s Correlation Coefficient Prism to display analogies and diverting time periods. The graph clearly shows how 2020 can be analogous to the recession of 2008.


A deeper look at 2020 shows that the majority of the irregularities occurred in the April / May time frame although the remainder of the year has shown also strong tendencies of dissimilarities.


Sifting through market data (through November 2020) I tabulated and identified additional clarity of this fascinating year.

The graph below shows the magnitude of daily point changes in DOW’s history:


Or below: The Top 20 Days of the S&P since 1967 with the Largest Intraday Point Swings.

Surprise, surprise, they are all days in 2020.


Or the largest single day changes of the S&P compared to a previous day with 70% of the top 40 existed in 2020:


Largest Daily Point Gains:

Largest Daily Point Losses:


Or the incredible rally from the election results that saw a 7.25% jump in the first week after the election. Here is a comparison I found to other post-election market behavior:

Source: (By the way this is a very cool history of stock markets and presidential elections)


CNN reported: “Post-election chaos would of course rattle markets, which famously hate uncertainty. The smoother-than-feared election set off a celebration on Wall Street, with the S&P 500 notching its biggest post-election rally since 1932.”

Hopefully, I have provided some convincing data to make you feel good about going through probably the most unusual financial year ever.  Earlier, I promised that I will come back around the issues that quant, systematic and algorithmic funds are facing specifically during a year like 2020.


The root problem is that the typical trade executions and decision making processes rely on historical data. According to Bloomberg, Renaissance clearly stated the problem during their September letter to clients: “It is not surprising that our funds, which depend on models that are trained on historical data, should perform abnormally (either for the better or for the worse) in a year that is anything but normal by historical standards.”


So for me, the question that I would ask myself if I were in Renaissance or a typical quant strategy is this:  What would it mean if 2020 is just a signal of a brand new market regime?  One that is erratic as the retail investors provide over 20% of trade flow – the highest share ever to multiple sources. Or if the speed of automated decision making continuously increases to unprecedented levels.


What if markets continue to charter new dissimilarities and the historical data sets that these traditional algorithmic or quantitative strategies are not relevant anymore?


It means that we will be beginning a new chapter of hedge funds closures potentially – one that is specifically targeting the quantitative, systematic and algorithmic shops that are unable to reinvent their approach.  At a minimum, they must address decision making made with increasingly more sparse data sets or reconfigure existing logic to a higher level of market signal detection.


Without giving away our recipe of course, the key is to advance the authenticity of models to a point where they are self-learning and much more agile.  If the markets of 2020 have proven anything, it has shown that building dynamic models that evolve and make decisions in extreme conditions is the future.

By Andreas Roell


When Google’s DeepMind beat the Go world champion in 2017, the AI world celebrated this accomplishment as a major milestone in the technical advancements of the industry.  Rightfully so, because defeating the number one Go player at the time was considered beyond the reach of even the most sophisticated computer programs.  The ancient board game, invented over 2,500 years ago, is famously complex with more possible configurations than atoms in the observable universe.


To explain briefly, Google’s AlphaGo used a combination of deep learning and neural networks in such an advanced manner, at that point in time, the algorithms continually reinforced themselves to improve by playing millions of games against variations of itself.  This trained a “policy” network to help the models predict the next moves, which in turn created a “value” network to evaluate proposed positions.  AlphaGo then looked at the possible moves and permutations to select the move the network deemed most likely to succeed.


When studying the moves of AlphaGo during its game against world champion Ke Jie, it is astonishing to see that the moves it executed were considered highly unusual or have never been seen before in certain situations of the game by even the most experienced players.  If you have not seen the documentary about AlphaGo, I highly recommend it.  It is available on Youtube here


Often successful algorithmic approaches to the financial markets are compared to AlphaGo’s approach to the game of Go.  The analogies are very logical from an algorithmic perspective when considering the extreme amount of permutations possible such as in the game of Go. However, comparing the level of permutations in Go to the financial markets is not considered a scientific challenge.


In addition, many scientists take the approach that training algorithms on the historical behavior of the markets will provide the model training necessary for algorithms to anticipate, predict and decide on the next trading moves.  That’s why it is so common to hear conversations about how feeding more historical data into models for training will make the executions even better.


However, after analyzing the financial markets at a much deeper level, it becomes clear that the analogies between the game Go and the financial markets are overly simplistic and superficial.  In reality, relying on a similar algorithmic approach will create a significant amount of risk to underperformance and volatility.  


The fact is, the financial markets are significantly more complex than the game of Go. To explain it very simply, our Chief Scientist explained to me that the composition of “opponents”, or to put it nicely, “players” changes on a day to day and minute by minute basis.  Different participants execute their beliefs on the best next move.  They all have different strategies, needs, systems, capabilities, knowledge and even emotions.


In thinking about this complexity in relation to Go, you would have to re-analyze your opponent’s personality without ever seeing him before and prior to your next move.  In addition, you have a high level of outside influences that have an impact on the markets. For example, breaking news and news stories that specifically have a major impact on the trading conditions of this unprecedented year of 2020.  


To make matters worse, news may break in the middle of a trading day providing a significant amount of directional turns throughout the day.  Driving this point home is that 18 out of the top 20 largest intraday directional market swings in market history occurred in 2020.  Looking at 16 out of these 18 trading days, the intraday swing can be correlated with significant breaking news announcements, such as the vaccine trial breakthrough.


This brings me back to my comparison with the game of Go.  The financial markets would be the equivalent of not just dealing with a changing opponent for every move, but an opponent changing before a move is made.  In addition, the opponent also has exclusive permission to change his move after you have made yours. Tricky, right?


Leading an algorithmic-focused asset management firm, I have learned that the key to all of this is that models are built with the following key components to be successful:


  • Ability to detect market rotations in order to identify underlying dynamics of the market
  • Ability to train models based on very small data sets.  The reason for this goes back to my earlier argument that every trade situation is unique and has never been seen before
  • Participate in both long and short positions to provide an opportunity to participate in any market direction
  • Ability to predict and function around market slippage when dealing with intraday trades
  • Ability to function effectively across various type of securities to take advantage of de-correlated conditions typically between two asset types 


I hope this provides you with a high level perspective on how one would either build an algorithmic trading framework or allow a potential investor into an algorithmic-focused offering to conduct more effective due diligence.