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  • Davide Accomazzo

Machines versus Humans

Updated: May 23, 2020

In recent weeks, a debate over increasing interest and money flows toward quantitative types of strategies has conquered the headlines.

Blackrock’s manifested switch toward more computer based products kicked off this last round in the never ending debate about machines versus humans in the investing universe. Richard Bookstaber, author of the book “The End of Theory,” chimed in and so did Jason Zweig in the pages of the Wall Street Journal.

In the last twenty years, the trend in Wall Street has been to rationalize the stock picking and asset allocation process by utilizing the ever increasing (and ever decreasing in price) computing power in order to achieve economies of scale and reduce emotional distortions.

This process toward quantification of the investment dynamic is invariably interrupted by the recurrent crises and bouts of “unsurprisingly” unexpected increases in markets volatility.

In the class that I teach at the Graduate School of Business and Management at Pepperdine University - Investments and Portfolio Management – I spend a considerable amount of time in the attempt to have my students develop a sensitivity toward that fine balance between a quantitative approach and common sense based discretion.

This balancing act is a decisive element toward a long and successful experience in the markets. The problem with a monochromatic approach is that the markets are a discounting machine based on numbers and statistics but yet markets are also made of people, an emotionally charged species. Markets also work on frequent periods of asymmetrical information which leads even the most rational approach to a possible disastrous decision.

With my students we work on an interesting exercise to find that balance between discretion and mathematical rigor. We research and then debate openly the Theory of Reflexivity by George Soros. In his book from the mid ‘80s, “The Alchemy of Finance,” Soros discusses reflexivity as one of the most important elements in his approach. As he dismisses classical theories of market efficiencies and rational decision making, he builds a case for an interactive process where the mere decision of an investor to get involved contributes to change that future event he or she is trying to forecast and bet on it.

By debating the issue openly, students work through the process of visualizing their approach to investing and can be positively critical of all the steps involved rather than accepting routine-based decisions.

To build on the issues of reflexivity and rational decision making, Zweig, in his Wall Street Journal article, correctly points out that human judgment varies depending on surrounding conditions. For example, individuals working as a group behave differently than in isolation.

The interaction between data, surrounding conditions and human judgment also make historical data an important yet fluctuating variable. In other words, machines tend to take historical averages as static pieces of information while their validity is, to a great extent, a function of contingencies.

A concluding point, also brought up by Zweig, is the issue with money flows and valuations. As the trend in quantitative strategies reinforces itself, increasing money flows will move in that direction altering the value of the underlying assets and making valuations unattractive. While, momentum can carry the day in shorter time frames, on a longer time horizon, performances will tend to reflect valuations’ reversal to the mean.

So perhaps the debate should not be whether machines are superior to humans when it comes to investing but to what extent and how they should co-exist in order to create superior risk adjusted performance.

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