The issue: Less can be more

People often use logic and statistics to determine where to invest money. But to do that, they need to know all relevant alternatives, consequences and probabilities; in other words, these methods require a predictable world.

Since that world doesn’t exist, much research is being done on heuristics: a method whereby people deliberately ignore some available information in order to make quicker decisions. Most people and organizations—even traders—regularly rely on heuristics. My research has shown they’re not only more efficient than complex methods, but may also be more accurate.

Simplify the inputs

Complex problems like investing don’t always require complex solutions. Instead, try these two types of heuristics: recognition and equality.

Recognition heuristics occur when people choose the option they recognize most—so an investor choosing between a known or unknown stock would pick the one that’s more familiar. Recognition-based portfolios, on average, outperform the market, managed funds, and randomly chosen portfolios.

Used in concert with recognition, equality heuristics also have a lot of potential for investors. Using this method, all known alternatives are treated equally to make a decision. An investor with a pool of 100 stocks he recognizes would allocate cash equally among them.

Our research shows the more stocks in which you are investing, the more likely equality heuristics are to outperform more complex processes, such as the mean-variance method.

That happens because mean variance requires investors understand each stock’s performance history, estimate all known parameters, and then make predictions. That’s possible in a controlled environment. But the more stocks you look at, the more likely you are to choose the wrong parameters, make more errors, and generate less return.

Equality heuristics, by contrast, are more intuitive. Investors don’t need knowledge of historical performance or to understand every available stock. So equality heuristics have the potential to be more robust and useful than statistics in real-world conditions.

Understanding heuristics

Complex decision-making methods are often used by financiers because they perform well in optimal circumstances. But in the real world, optimal circumstances rarely exist. There are too many variables and the stock market is so unpredictable that you’d have to analyze 500 years of performance data in order to make accurate predictions about future performance.

Heuristics are often viewed as the ugly cousin of logic and statistics. They’ve been wrongly linked to errors and irrationality, primarily because we have lacked the means to appropriately measure how and when they are best used.

To apply heuristics, advisors could look at their clients’ own stock preferences. Stocks your clients may have read about in the paper or heard about from friends could be a legitimate match for their portfolios. As more research becomes available on when heuristics work best, we may also start to see investing equally across a number of areas as a valid way to optimize clients’ portfolios.

Dr. Gerd Gigerenzer, managing director, Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, as told to Brynna Leslie, an Ottawa-based financial writer.

Originally published in Advisor's Edge