Tag Archives: Game Theory

Market Games

Recent record highs have focused a lot of attention on the stock market.  The broad market rise is largely due to Fed actions (quantitative easing and a near zero discount window), creating lots of excess cash and nowhere else good to put it. It’s a risky solution that props up markets while inflation is delayed.

But what about individual stocks? In this rising tide market that can gloss over things, how do you better discern individual winners? Of course, company metrics (fundamentals, earnings, balance sheets, etc.) and movers (news and innovation) are the mainstays. Technical analysis can be helpful, but that tends to focus on surface effects. Can big data look behind the scenes?

Just as tons of consumer market data now drive product marketing decisions, the wealth of available corporate stats increasingly influence stock buy and sell decisions, sometimes to a fault.  In this data mining era, we’re much better at correlation than causation, but that’s often good enough.

The individual investor is perhaps the only truly random walk (or uncertain walk) left in the stock market. Since prices are most influenced by large holders and program trades, movements can be partly predicted by comprehensive mathematical models on the big players and their trading strategies. With enough data and processing power, it’s possible to run rich behavior models in predictive mixed strategy games to forecast prices and actions. There’s been some interesting research in this area, and I think we’ll see more. At least while the current bubble continues to grow.