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How finance evolves

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Evolve

Why is it that stock markets tend to be depressed during winter months? Or that investors with too little emotional response (or too much) tend to be less profitable than those with just the right amount of emotion? Or that traders tend to make more money on days when their levels of testosterone are higher than average?

In a fascinating new book, Andrew Lo builds on the corpus of behavioural science research to outline a new theory of financial markets. His basic point: homo economicus is dead. The hyper-rational human that always optimized every decision, most famously portrayed in the Efficient Markets Hypothesis of Eugene Fama that has ruled the field of finance at least since the 1980s, does not exist. His new book, Adaptive Markets: Financial Evolution at the Speed of Thought, explicates his Adaptive Markets Hypothesis, first proposed in 2004 as a substitute for the Efficient Markets Hypothesis.

In short, the Adaptive Markets Hypothesis accepts that humans are biological beings, and that our biology limits our ability to optimize every decision as the Efficient Markets Hypothesis predicts. Most importantly, though, our ‘irrationality’ is not random. This means that we consistently make the same ‘mistakes’, something that behavioural scientists have known for quite some time. One of these mistakes, for example, is that we often link events together because they happen to occur close to each other. As Lo puts it: ‘We humans are not so much the “rational animal” as we are the rationalizing animal. We interpret the world not in terms of objects and events, but in sequences of objects and events, preferably leading to some conclusion, as they do in a story.’

Telling stories is one way we try to make sense of the world, even if those stories are sometimes false. We do this because, given the environments that we encountered, this was the most evolutionary successful behaviour. But that has consequences: If our environment change, our biological decision-making processes might not be equipped to deal with the new environment. In Lo’s words: ‘“Rational” responses by homo sapiens to physical threats on the plains of the African savannah may not be effective in dealing with financial threats on the floor of the New York Stock Exchange’.

Often the real world is not very different from the survival-of-the-fittest world our ancestors encountered on the African plains. Many times, humans do optimize their behaviour. This is why the Efficient Markets Hypothesis could hold for so long, treating ‘irrational’ behaviour as random outliers that will be averaged out in the marketplace. But as Low demonstrates in countless examples, often humans (and by implication traders) behave ‘predictably irrational’, reacting to fear systematically different than to reward, for example, and opening opportunities for windfall profits on the financial markets. That is why some famous investors, accounting for these predictably irrational heuristics of humans, can be consistently successful.

The good news, though, is that we are not like other animals. We do not have to wait for evolution to take its course, molding us to our environment through natural selection. We have the ability to learn and adjust through trial and error. High-frequency trading is a great example: speed is everything in financial markets, and automated trading programmes have replaced specialist human traders who are just too slow to recognize and respond to the predictably irrational human errors. But even this is changing, as Lo explains: ‘At first, these high-frequency traders made windfall profits, since human specialists were sluggish and inefficient in comparison. However, there ultimately came a point where high-frequency traders were mainly competing with each other. To succeed in this financial arms race, high-frequency trading firms had to invest in faster and more expensive hardware. At the same time, however, these firms were scouring the market for any trace of “juice” that might be left. In a very short amount of time, high-frequency trading was pushing against its natural evolutionary limits. It had unexpectedly become a mature industry, with low margins on trades and low overall profits.’ High-frequency trading is now on the decline, as more and more exchanges start implementing ‘no high-frequency trading zones’. The environment is changing, and those high-frequency traders that do not adapt, will perish.

The book presents not only a fascinating new theory that can explain why some investors continue to be successful despite the prediction of the Efficient Markets Hypothesis, but it also situates this theory within the context of broader developments in finance. We learn why the Efficient Markets Hypothesis was so appealing, why earlier attempts to use evolutionary thinking in finance never caught on, and what this new theory might say about the future of finance. It also has a cautionary word about how we train the next generation of finance gurus: ‘For the mathematically trained economist, it’s sometimes difficult to think in evolutionary or ecological terms, but sooner or later, this way of thinking will be domesticated (another biological metaphor), and will become another standard tool for economists to use, just as molecular biologists use it today.’

Just like the finance industry employed mathematically-inclined engineers and physicists in the last few decades, perhaps biology will be the training-of-choice for the next generation of investment firms. Perhaps. What we do know is that the environment is changing, and that means that traders will have to adapt too if they are to survive, and thrive. As Lo explains: ‘An evolutionarily successful adaptation doesn’t have to be the best; it only needs to be better than the rest.’ Let the games begin!

*An edited version of this first appeared in Finweek magazine of 7 September 2017.

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