As time has gone on, I have grown increasingly fascinated with the science of finance (even as I have yet to digest much of the academic literature on the topic). Instead, I started by dipping my toes into more-easily digestible accounts of people who have been successful applying scientific principles to finance. 

The reason who I have chosen to go down this route is because I have long been of the view that studying the opinions, actions, and scientific directions of talented, successful, and intelligent people will maximise one’s chances of learning something interesting (since you’re essentially piggy-backing on the insights and experience already offered and learned by such individuals). To put it another way, picking up where successful people have left off will allow you to stand on the shoulders of giants more easily and successfully than any other approach. 

To this effect, I have been growing my library with books on and about people with interesting and/or successful ideas on and about finance. Most recently, I read The Man Who Solved The Market, Gregory Zuckerman’s book on Jim Simons and his colleagues at Renaissance Technologies. 

Zuckerman’s book isn’t the best I’ve read: While it offered up interesting tidbits on investing and quantitative finance, it didn’t go into these in any depth. As a result, the book does not offer much insight; it’s more a journalistic account of Renaissance Technologies’ origins and rise to fame, and the personal stories of the people who helped make this happen. In part, I suspect this is because of the secretive nature of the hedge fund that means that not much is known about its insights and methods. In addition, I have also come across suggestions elsewhere that run along the lines of our public understanding of the science of finance suffers from the fact that any meaningful insights will be more profitable if not shared widely. I think there’s some truth to that. 

Yet, Zuckerman’s book does contain some interesting comments and perspectives (even if these would fit on less than a two-page spread) that pretty much boil down to the observation that financial markets are complicated systems and that their movements and undulations are the product of many, many disparate actions and forces. Of these, the company ‘fundamentals’ (e.g. annual reports, conference call transcripts, company accounts) that fundamental investors focus on represent just one small part of a much bigger whole. Instead, a holistic view of companies (and other actors) as being part of an interconnected web of other commercial entities across economies, time, societies, and geographies offers a much richer view of the complicated world in all its glory.

This is also a very non-linear perspective. 

Importantly, a  nonlinear perspective of markets allows an investor to rise above the myopia of many other market players. If executed well—through the identification and application of meaningful, pan-economic signals—this approach will afford a market player a differentiated and superior perspective that will allow them to beat the market—even for long periods of time. 

To wit, financial markets are zero-sum systems, much like a game of poker. For the most part, no new material products are being made and no value is being created (this privilege belongs to people like farmers, engineers, and other growers/creators of material things). As a result, the same amount of money will leave a market as went into it. Effectively, this means that financial markets form a game of wits where the player(s) with the most successful strategies will win. Their winnings will also be bankrolled by the losses of other, less fortunate, players. 

A lot of success under these types of conditions come down to skill (and just a little bit of luck). Indeed, biological metaphors can be apt here, with the zero-sum game of the financial market following a similar playbook to the creative destruction that governs the fate of individual organisms in an ecosystem, where the most fit individuals will enjoy the greatest probability of parenting the next generation. So, too, is the fate of players in financial markets, where successful strategies will see the highest probability of their gains compounding over the long term. Indeed, similar dynamics apply to many other complex and iterative systems. (Company cultures spring to mind.) 

As a result, zero-sum games are inherently unfair, and unless there are no winners whatsoever, the dynamic will only produce winners at the expense of losers. In a zero-sum games, it will be impossible for everyone to perform above average. Zero-sum games in nonlinear systems are however even more unfair than this, because stellar performances will be quite rare. In such systems, there will only ever be a few, differentiated players raking in most of the gains at the expense of much-larger numbers of less-successful ones.

Science will thrive in any complex system where skill (defined as sustained above-average performance) is the main determinant of success. This is because science is a system for sussing out cause and effect and to pull out one of the underlying threads from a complicated tapestry to understand its contributions to the whole.

As such, science (in the form of maths) is the strategy adopted by successful quantitative finance shops like Renaissance Technologies, and it is what has allowed these funds to see the success that they have. In the words of a yet-to-be-successful Simons, “If we have enough data, I know that we can make predictions.”

Science is a method for understanding the underlying dynamics of a system (however complex), and such understanding constitutes skill when the game is played. (A relatively good understanding of poker will see you perform better—to be more skilled—in a game played with a sample of people. The better your understating of the game—and how it is played by other people—the higher your skill and the better your performance regardless of the skill of the other players.) Financial markets are not much different: The better your relative understanding of the inner workings of the markets—and what drives gains and losses—the more skilled you are and the better your performance will be. 

Apart from realisations like this (that a better understanding of markets will translate into skill and outperformance, which is obvious in retrospect), another point that was made by Renaissance staffers in Zuckerman’s book was that the markets are human. 

Benjamin Graham, one of the fathers of value-investing, said that the market is a “voting machine in the short run, but a weighing machine in the long run”. Whatever your feelings about value-investing in general, this point of Graham’s is still true. Markets are made up by people, and as people, they are host to a bewildering array of more or less false beliefs. What Graham alluded to in the quote above is that these follies will stack up over the short run. (When these follies take the form of exuberance, we see bubbles forming, making the luckiest fools rich.) In the long run, however, reality will win out and it is only the most skilful investors who come out on top as their long-held beliefs were shown to be true.

As a result, markets can never be fully efficient: As the air goes out of one foolish belief, there are a hundred more to take its place. Instead, markets are grossly inefficient; being the result of the more-or-less coordinated actions of thousands of individual actors, each one imperfect and subject to their own follies, biases, and beliefs. Yet, even as every folly is unique in its own way, human nature is not. There are only so many ways in which a human being will respond to a given stimulus. As a result, a market will reflect the average collective belief of each player therein; each price-level is the total sum of buys and sells (and the underlying beliefs that motivated those actions) at any given time. 

Quantitative investing takes the view that while it is impossible to understand the market (the system is just too complex), you could potentially understand the psychology of the players therein (even if you’ll never understand their individual motivations or beliefs). The assumption then becomes that human behaviour with regard to markets is predictable, even as the markets themselves are not. Therefore, if you can understand how people have reacted to an event in the past, there is a possibility that you can benefit from people reacting similarly in the future. There’s something very beautiful about this approach, and by using math and removing the emotions, follies, and beliefs from the equation, you can rise above all such human concerns to instead go fishing for the reliable signals that they create in aggregate.

Price data is full of such signals, being the collective sum of thousands of strands of information. If you learn how to decode the sum of all the strands, you therefore won’t need to understand the underlying strands themselves (in the form of annual reports or investor presentations or news items), because all of that information (or, more accurately, the collective interpretation thereof) has been baked into the price. If you can identify reliable-enough signals in the data, you therefore won’t need to forecast cashflows or interest rates, because all that data is already there. This is not all that much different from trying to understand the underlying order in the natural world by dissecting fluid dynamics, cell biology, or star formation. 

As a result, quantitative investing makes use of large datasets, allowing the investor (and their computer models) to slice markets up into segments and to figure out how any given set of price-movements have correlated with market gains and losses in the past. The assumption is that if these correlations exist (and at an acceptable level of statistical significance), money can be made by betting on the pattern holding true also in the future. Of course, the details of quantitative investing is much more complicated than that, and Zuckerberg writes that Renaissance Technologies uses massive data sets spanning everything from finance and economics to society and culture. Yet, if you were to distil quantitative investing down to one key insight, that would be it: That markets are human and human behaviour is quite predictable. 

Now, we can argue how ‘scientific’ this insight is, but I think it identifies a fundamental force to human-made complex systems: The human. The resultant systems are only complex because they emerge from the collective actions of thousands—if not millions of individuals—each governed by reasons best understood to themselves. Naturally, much of the same applies to other complex systems, where individual components can be understood with a fair degree of accuracy, while their collective actions and interactions cannot. 

For those who doubt that human behaviour is predictable and that it produces tell-tale signs in market pricing, I offer up the following exhibit: The ‘anatomy of a crash’ in the S&P500. 

The anatomy of a crash is market-dependent, with the resultant price-movements depending on factors unique to each market. Yet, these individual differences aside, the resultant, collective reaction is quite predictable—as you can see in the chart, above. 

Importantly, in the chart, you can see how the COVID-19 Crisis follows the anatomy of a crash in the S&P500 quite well, the only difference being the exact scale of the price-movements and the duration of these. Indeed, while the Great Financial Crisis played out over the better part of a year (and then some, as shown on the graph), the COVID-19 Crisis played out over a much shorter time-scale (the cyan line above shows the S&P500’s behaviour from the beginning of January to the second-last week of May). Notably, this could also help explain why the market seems to be up despite the economic backdrop: According to this chart, the COVID-19 crisis was an S&P500 crash in fast-forward; playing out over a matter of weeks rather than months (or even years). 

Of course, and I shouldn’t have to add this: That while the COVID-19 Crisis seems to follow the path of the Great Financial Crisis quite well, this does not mean that the pattern will hold in the future. Complex, nonlinear systems are beautiful like that—they always have the power to surprise you, even when you think you know what’s going on. But at least that keeps us busy. There will always be more to understand.