Experts vs. Checklists: Can algorithms improve investment decision-making?

Monday, Jan 09 2012 by
Experts vs Checklists Can algorithms improve investment decisionmaking

We reviewed in an earlier article Daniel Kahnemann's Prospect Theory, his latest book "Thinking Fast and Slow" and some of the key findings of Behavioural Finance and we'll be discussing a number of the implications for investors in a subsequent piece...

However, in this article, we'd like to dwell in one interesting discussion in Chapter 21 of the book- "Intuition vs Formulas". This discusses in some detail the efficacy and value of checklists & algorithms in addressing some of the predictable flaws in human decision-making -users of Stockopedia will already know that these kinds of tools are a key part of our feature set. 

The Efficacy of Checklists 

Kahnemann writes that a key source of inspiration for his work was the book, Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence by Paul Meehl. Meehl was an American psychologist who studied the successes and failures of predictions in many different settings in the 1940s. He found overwhelming evidence that predictions based on mechanical (formal, algorithmic) methods of data combination outperformed clinical (e.g., subjective, informal, "in the head") methods based on expert judgement. 

A famous example confirming Meehl’s conclusion is the “Apgar score,” invented by the anesthesiologist Virginia Apgar in 1953 to guide the treatment of newborn babies. The Apgar score is a simple formula based on five vital signs that can be measured quickly: Appearance, Pulse, Grimace, Activity,Respiration. It does better than the average doctor in deciding whether the baby needs immediate help. It is now used everywhere and saves the lives of thousands of babies.

Another amusing example of the power of statistical prediction is the Dawes formula for the durability of marriage. This formula apparently does better than the average marriage counselor in predicting whether a marriage will last. The formula is:

“frequency of love-making minus frequency of quarrels.”

Similarly, Andrew McAfee of the Harvard Business Review references a 2000 paper which surveyed 136 studies in which human judgment was compared to algorithmic prediction. Only eight of the studies found that people were significantly better predictors of the task at hand. He also cites the case of Princeton economist Orley Ashenfleter who predicts Bordeaux wine quality using a simple model he developed that takes into accountwinter and harvest rainfall and temperature. Although wine…

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3 Comments on this Article show/hide all

Gunslinger 19th Jan '13 1 of 3


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peterhale 13th May 2 of 3

Are there any articles on how to develop and evaluate quant algorithms?

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iwright7 14th May 3 of 3

In reply to peterhale, post #2

I personally like to combine numerical screening factors and judgement. The Guru and Stockrank screens are effectively checklists and semi-automated algorithms and have become a more important part of my share selection criteria. Those companies than pass well give me a small bunch of, "high probability of gain" stocks.

My next judgement stage is a financial trend checklist. This is largely a numerical target sheet that forces me to look for particular strengths and weaknesses that may have been missed in the screen selection. Its not that a share has to pass each checklist factor, but the greater number it passes well the more confident I become.

The process then becomes much more subjective when outlook, broker bull, news and value become part of the evaluation. From this I produce a short Word summary of the information gleaned. Finally I give the company a "Ian Confidence Score" out of 10. I am quite hard on my scoring and the higher the company scores the greater the % of the portfolio (up to 10%) I will potentially allocate. 9/10 is my top score to date.

My selection forces forces me to re-check and research and probably takes the best part of 2 hours/company before either I discard, or press the button buy. So there is quite a bit of time involved which algorithm selection would negate, but from the standpoint of £earnings/hour this process has by a mile been the most profitable time I spend.

Because I have been through the above process and knowing there is little more I can easily do, I sleep soundly at night confident that whilst I will get some company selections wrong, that overall the probability of gain vs. the market is on my side.

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