This is the second part of a two part article. The first part can be found here. The first post dealt with the largest and most well known area where computers are replacing human beings in investing and that is the area of passive index investing. In this post I’m going to focus on two newer areas where computers either are replacing human beings, or where computers could potentially replace humans in the future.

Quantitative Investing

The first such area is called quantitative investing. Quantitative investing is essentially investing according to a pre-determined formula or algorithm and having a computer calculate and process the trades necessary to implement those algorithms. Like passive index investing, quantitative investing doesn’t necessarily require computers, however the processing speed of computers makes quantitative investing much more feasible and allows for a much more complicated set of rules to invest by.

There are two types of quantitative investing:

1. Systematic factor investing

2. Statistical arbitrage

In systematic factor investing, the manager examines a period of market history which shows that superior performance was connected to certain factors. Factors are characteristics of securities. These factors could include value, quality, size, and/or momentum. The manager then builds an algorithm which is designed to overweight these factors that are tied to superior performance in a portfolio. Using this algorithm the manager hands over control to a computer to build the portfolio to match the objectives. The end product is a portfolio driven by the algorithm which should in theory deliver high returns (so long as the factors which lead to outperformance in the past continue to outperform in the future).

Statistical arbitrage is a more complicated process where a computer takes note of daily transactions in a security and in the market in general and attempts to squeeze out very small profits (often as low as a penny per security) by identifying mispricings in individual securities as they relate to the overall market. The computers identify dislocations in the market which based on historical precedent will revert to equilibrium. For example, if a firm’s stock price dropped in trading during the day, but there was no corresponding drop in the market and there was no material news around that company, the stat arbitrage manager may buy that security believing that the price should bounce back into equilibrium with the overall market. Stat arbitrage models are designed to be right only a little more than 50% of the time, and they generate only very small profits, however if they are done with large amounts of leverage and many times over, they can generate substantial returns. One well known firm that conducted stat arbitrage is Long Term Capital Management. The problem with Long Term Capital’s model was the excessive leverage which eventually led to its collapse (though some would certainly argue that hubris was the deciding factor in the failure). One thing that was proven by the failure of Long Term Capital Management was that opportunities for stat arbitrage are limited in size.

Both types of quantitative investing involve programming computers to emulate behaviors that were successful in the past in the hopes that they will continue to be profitable in the future. It is here where their usefulness becomes limited. In a hyper competitive and constantly changing market which involves irrational human beings making decisions, there is no perfect formula which can be distilled which will always work as a profitable investment strategy.

Let’s say we are able to devise a formula which is able to “beat the market” consistently and in this instance the advantage we’ve identified is derived from overweighting small company stocks over large company stocks. As soon as other investors take note of the fact that we continue to beat the market they will also take note of the factors that lead to our outperformance. They will in turn adjust their strategies to incorporate more small companies. By doing so this strategy will become mainstream and will no longer be an advantage for us. It may even make the market for small companies so competitive that the relative prices for such companies lead to outright losses in the long term. Again, George Soros's Law of Reflexivity applies and managers must adapt to a market which is constantly adapting to them.

Some quantitative investors have attempted to address this by becoming more dynamic and adjusting their strategy as market conditions change. While this might help in the short term, it doesn’t change the fundamental problem that the algorithm will only work so long as it isn’t copied by others. Ultimately you still need human oversight to make sure that the algorithms do not go off the rails.

Quantitative models therefore have a place in securities analysis and some of the more advanced quantitative strategies may be able to persist for a long time. However ultimately without human intervention they would eventually fail to outperform, because the market would adapt to them. Can a computer be as adaptive as a human being? Well this leads us to the realm of artificial intelligence (AI)…

Artificial Intelligence and Machine Learning

The newest and the area with the most potential for computers to replace humans is in the area of AI. For those of you who are not familiar with what AI actually means, it is the process whereby machines mimic cognitive functions of a human being such as learning or problem solving. The traditional goals of AI research include:

1. Reasoning

2. Knowledge representation

3. Planning

4. Learning

5. Perception

AI is essentially the ability of machines to think. Whereas quantitative investing involves humans giving instructions to a computer and the computer carrying out those instructions, with AI the computer figures out the instructions by itself and then carries them out. Theoretically this means that AI should be able to address the limitations of quantitative investing by thinking, learning, and adapting itself to changing market circumstances.

Probably the most well known example of this is the playing of chess by computers. Grand Masters of chess learn the game by studying past chess matches, and studying the moves and outcomes of those matches. The problem is there are obvious limits to the number of games a human can watch and study. On the other hand a computer that’s powerful enough can study every chess match ever played. That’s why computers are routinely beating Chess masters and no one is surprised when they do. They are able to adapt to the play of their human opponent because of the depth of knowledge.

It’s too early to tell how AI will affect our industry and investing in general. While I think it’s possible that computers will become dynamic enough to become very formidable tools in investing, I don’t think human expertise is becoming obsolete anytime soon. The idea that perfectly rational machines will be able to predict the movements of a sometimes completely irrational market seems very unlikely to me. The difference between playing chess and investing in the market is that chess is a game which is still defined by a fairly small set of rules, which the players have to abide by. The market is a much more complex, much more unpredictable place where often there are no rules which govern the behavior of the participants. Even a computer would have trouble studying every single trade and identifying the reasons for the trade and the subsequent result because a lot of that information exists only in the person’s mind when they made the trade.

What’s more, qualitative factors of a company are by definition impossible to quantify and would therefore be difficult for a computer to understand regardless of how much historical information it is able to draw from.

Is a computer going to understand why people pay a premium for See’s Candy Peanut Brittle over a generic brand of chocolate and caramel covered peanuts? Is the computer going to understand why sales of luxury items go UP when they raise the prices? Is the computer going to understand why Nickelback has sold over 50 million albums while at the same time being ranked as the 2nd worst rock band of the 1990s by Rolling Stone? (to be fair I’m not sure a lot of humans can understand that one)

These are all questions which I think would be difficult for a computer to grasp, and they are just the types of questions which can separate an average investor from a great one. Only time will tell if AI can prove me wrong. For now anyways, my job as a human investor remains safe!