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Taking a Systematic Approach Will Improve Your Finances

Dazed, your mind wanders while you question your mastery of the English language as you try to make it through another dense tomb on how to properly manage your finances. As you re-read the same page twice for the umpteenth time you begin to think, there must be a better way.

Writing in Thinking, Fast and Slow Daniel Kahneman describes some research by a fascinating hero of his.

Paul Meehl was a strange and wonderful character, and one of the most versatile psychologists of the twentieth century. Among the departments in which he had faculty appointments at the University of Minnesota were psychology, law, psychiatry, neurology, and philosophy.

Kahneman continues regarding one of his great influences.

I never met Meehl, but he was one of my heroes from the time I read his Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence. In the slim volume that he later called “my disturbing little book,” Meehl reviewed the results of 20 studies that had analyzed whether clinical predictions based on the subjective impressions of trained professionals were more accurate than statistical predictions made by combining a few scores or ratings according to a rule.

In a typical study, trained counselors predicted the grades of freshmen at the end of the school year. The counselors interviewed each student for forty-five minutes. They also had access to high school grades, several aptitude tests, and a four-page personal statement. The statistical algorithm used only a fraction of this information: high school grades and one aptitude test. Nevertheless, the formula was more accurate than 11 of the 14 counselors. Meehl reported generally similar results across a variety of other forecast outcomes, including violations of parole, success in pilot training, and criminal recidivism. Not surprisingly, Meehl’s book provoked shock and disbelief among clinical psychologists, and the controversy it started has engendered a stream of research that is still flowing today, more than fifty years after its publication.

The number of studies reporting comparisons of clinical and statistical predictions has increased to roughly two hundred, but the score in the contest between algorithms and humans has not changed. About 60% of the studies have shown significantly better accuracy for the algorithms. The other comparisons scored a draw in accuracy, but a tie is tantamount to a win for the statistical rules, which are normally much less expensive to use than expert judgment. No exception has been convincingly documented.

It’s helpful to keep this in mind when managing your finances because expert advice can often be costly and difficult to access for those with lower account minimums. So implementing rules based approaches can often come close to or exceed the strategies implemented by experts at a fraction of the cost while being accessible to all.

Later Kahneman gets into how rules based approaches compete successfully with experts.

Why are experts inferior to algorithms? One reason, which Meehl suspected, is that experts try to be clever, think outside the box, and consider complex combinations of features in making their predictions. Complexity may work in the odd case, but more often than not it reduces validity. Simple combinations of features are better. Several studies have shown that human decision makers are inferior to a prediction formula even when they are given the score suggested by the formula! They feel that they can overrule the formula because they have additional information about the case, but they are wrong more often than not.

Another reason for the inferiority of expert judgment is that humans are incorrigibly inconsistent in making summary judgments of complex information. When asked to evaluate the same information twice, they frequently give different answers. The extent of the inconsistency is often a matter of real concern.

Formulas do not suffer from such problems. Given the same input, they always return the same answer. When predictability is poor—which it is in most of the studies reviewed by Meehl and his followers—inconsistency is destructive of any predictive validity.

The research suggests a surprising conclusion: to maximize predictive accuracy, final decisions should be left to formulas, especially in low-validity environments.

Simple equally weighted formulas based on existing statistics or on common sense are often very good predictors of significant outcomes.

In the realm of managing your finances simplicity has the added advantage of being cheaper than complex options. Another benefit of simple strategies is they are more understandable than complex ones so you are more likely to understand how they work. Which is important when it’s your money on the line.

If deploying simple rules based approaches can be such an asset in assisting decision making in a wide variety of areas. Why aren’t rules based strategies deployed more frequently? Kahneman continues by outlining the hostility to an algorithmic approach to decision making.

They know they are skilled, but they don’t necessarily know the boundaries of their skill. Not surprisingly, then, the idea that a mechanical combination of a few variables could outperform the subtle complexity of human judgment strikes experienced clinicians as obviously wrong.

If you’re interested in a great, yet dense, read to better understand how the mind processes information to make decisions with specific applications in regards to managing your finances. Check out Thinking, Fast and Slow by Daniel Kahneman.

Steve Miller
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Steve Miller

Is a CFA® Charterholder and founder of DebtMD.com. A site devoted to helping people discern insights into their finances. The CFA designation is globally recognized and attests to a charterholder’s success in a rigorous and comprehensive study program in the field of investment management and research analysis.

Steve recently authored Escaping Student Loan Debt to assist student loan holders with developing and implementing strategies to minimize their repayments.

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Steve Miller
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