What a Year It Has Been

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: https://www.winton.com/longer-view/market-impact-of-us-presidential-elections (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.