Personal Finance and Artificial Intelligence (AI): Don't be Fooled by the Stork!
When we use artificial intelligence for managing our personal finance, we often see it as a barrier protecting our decisions from the impact of our emotions. Yet, it could also be misleading, particularly in correlation and causation analysis. Edouard Camblain, our behavioural finance expert and Head of Strategic Projects & Development at Societe Generale Private Banking, gives us the low down.
Storks and high heels: blurring the line between causation and correlation
Causation error which is broadly defined as:
the erroneous assumption that because one event follows another, the first event is the cause of the second event (known as post hoc ergo propter hoc(1));
the inversion of meaning between cause and effect;
or the erroneous assumption that one thing is the cause of another when in fact they are two effects of one underlying cause.
There are many examples of causation error. The most prominent is the “stork effect” — sometimes used as a synonym of causation error — according to which villages where storks nest have higher birth rates, proving that babies are in fact delivered by storks. But you don’t need to be rocket scientist to see that the most logical explanation is that storks prefer nesting in small, rural villages that in sprawling big cities where the relative birth rate is lower.
In the finance arena, three correlation theories for forecasting economic cycles have been hyped up:
The “hemline index”, introduced in 1926 by the economist George Taylor, which suggests that the length of women’s skirts is an indicator of a country’s economic health;
The “lipstick index”, coined by businessman Leonard Lauder in 2001, according to which lipstick sales are an inverse indicator of economic health;
The “high heel index”, created by IBM in 2011(2) based on a study over a 100-year period, according to which the height of high-heels tend to increase during times of economic recession.
Alas, none of these have proved perfectly accurate. Unrealistic, they fall into the first category of causation errors by assuming that two successive events make one the cause/consequence of the other.
Some even go to absurd lengths in their quest financial market forecasting models(3). David Leinweber(4) is known for taking the satire to the point of irony, demonstrating that between 1981 and 1993 the US S&P index (the index of the 500 largest US listed companies) was 75% correlated with butter production in Bangladesh, 95% including American butter... and 99% when including the number of sheep of both countries!
AI: the champion of correlation let down by causal links
While technological progress such as processing capacity, big data, and artificial intelligence effortlessly identify correlations, they have a much harder time with causal links. Human error — and causation error in particular — could well persist.
Artificial intelligence is often presented as a "miracle” cure for the influence our emotions have on financial decision-making. But the confusion between correlation and causation is one of the effects that can result from our emotions; and it presents a real disadvantage in the area of personal finance, for the search for advanced indicators on investment trends (corresponding to causes) may lead to entirely ineffectual strategies.
And the conviction of having found the winning formula thanks to a solid correlation (bringing an overconfidence bias into play…) runs a high risk of dismissing the rules of caution. And bear in mind that while the above “correlations” did work at certain points in time, they did not hold over the long term.
As a final point, I cannot emphasise enough that even strong historical corrections are in no way a prediction of the future — they are far from being a reliable indication of correlations and/or future performance!
And since I cannot be sure shorter articles are more read than longer articles (causation), or whether it’s all the same (correlation)... I’ll stop here!
(1) Latin for “after this, therefore because of this”.
(4) Author of the book Nerds on Wall Street: Math, Machines and Wired Markets.
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