Uncharted Waters – Part 1

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.