Using Game Theory to Model Market Uncertainty
Here, traps a trader can fall into by not understanding the distinction between uncertainty due to randomness and uncertainty due to actions of other people.
Game theory is a branch of mathematics that all traders, even traders who hate math, need to appreciate. It’s one of the ways to study uncertainty. The more familiar mathematical model of uncertainty is probability theory. In that field, we model things we don’t know as if they are coin flips or casino games. An application to finance is the Random Walk model of security prices. Of course, security prices are not coin flips; prices move up and down in response to market fundamentals and investor preferences and beliefs. But for some purposes it’s useful to treat the prices as random.
In game theory we model uncertainty as choices made by rational entities rather than unpredictable events we cannot influence. Consider the debate about profiling for airport passenger screening. Some people argue that most suicide bombers are young men, so Transportation Security Agency agents should concentrate on them rather than, say, 80-year-old Buddhist monks or mothers with small children. Other people argue that terrorists will quickly note any discrimination by agents and enlist bombers who can be disguised as whatever category gets the least attention.
Regardless of how you stand on that issue, note that the first argument is probabilistic. If historical observation shows that people with certain characteristics are more common among bombers, screeners should devote more attention to people with those characteristics. That treats being a suicide bomber as a random event that can be correlated with observable characteristics.
The second argument is a game theory one. It assumes suicide bomber characteristics are chosen by a rational entity that observes your screening strategy and responds to it in predictable ways.
For a simpler example, consider two sports bettors. One develops a computer simulation model of sporting events and estimates the probability of the favored team covering the spread. That’s treating the event like a casino game in which outcomes have calculable probabilities. His sister uses reasoning like, “Bettors tend to overbet on the teams that rewarded them last time, so I bet on teams after they’ve lost to the spread three times in a row and against teams that beat the spread three times in a row,” or “Big-market teams have more natural bettors than small-market teams and people are more apt to bet on games they attend than games they watch on television, so I bet against the big-market team at home.”
These strategies ignore the game entirely, and focus on bettor behavior, and the predictable actions of bookies to adjust the line to get the same amount bet on both sides rather than to reflect the true probability of winning. You can make money either way. (Hint: the second method is much easier, although it’s wise to pair it with at least some rudimentary statistical analysis of the game itself).
It’s important to remember that both views are models, simplifications of reality. It is not literally true that some lottery drawing in the sky determines who will blow himself up today, nor is it true that there is a single, predictable entity that determines who will try to smuggle explosives onto an airplane.
Sporting events are won and lost on the field by real players (unless, of course, they are fixed, in which case we have a pure game theory problem). Probability theory and game theory can be useful tools to support decisions, but outside of some artificial situations they are not exact descriptions of the world.
How does this relate to trading? I already mentioned the Random Walk model that treats security price changes as draws from a statistical distribution. A trader might use this approach by noticing that prices that go up tend to keep going up, so she buys stocks with good price momentum.
An example of game theory thinking is a trader looking for stocks with high short interest, in which the shorts have lost money and appear to have weak hands (perhaps because a popular short position just lost a lot of money, or perhaps because it’s near the end of a year in which short-bias hedge funds have had poor performance) and the stock has become hard to borrow. If this stock goes up, there may be a short squeeze to send it even higher.
Note that in this example, our probability theory trader and game theory trader are in the same position, buying a stock because it has gone up. But they are in for entirely different reasons. The probabilist is looking at historical data and betting that the future will resemble the past. The gamer is looking at the situation of other traders with positions in the stock, and making a prediction based on reasoning rather than statistics. The probabilist hopes other traders are either ignorant or forced to act irrationally because she wants to profit from their bad trades. The gamer is relying on other traders to act rationally.
Up to this point, we have been abusing the word “theory.” You don’t need a Ph.D. in statistics to test if a momentum rule has made money in the past, and you don’t need high-powered game theory mathematics to notice the signs of an impending short squeeze. This article is not about advanced quantitative trading techniques exploiting sophisticated mathematics. Rather it’s about the traps a trader can fall into by not understanding the distinction between uncertainty due to randomness and uncertainty due to actions of other people.