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What We Can Learn About Investing From the Business of French Wine

Algorithms are better than humans when it comes to predicting future events--whether wine prices or the stock market.

Articles by Betterment Editors

By the Editorial Staff
Betterment Resource Center  |  Published: November 20, 2013

Human memory tends to be biased, and we are inconsistent at summarizing complex information.

In fields where predictability is difficult, like markets or wine prices, experts are often inferior to algorithms.


shutterstock_112195439To gauge the price for a rare vintage Bordeaux, you could travel around the French countryside asking grape growers to recall weather from decades ago.

They would tell you that the most valuable vintages follow a similar pattern. Hot and dry summers yield grapes with higher sugar concentrations, compared to wetter conditions that result in grapes with more diluted flavors.

But one clever economist thought there was a better way to more accurately predict pricing based on weather data and not human intuition. In the 1980s, Princeton labor economist and wine lover Orley Ashenfelter created his formula:

∆ price = -12.15 + (β1 * Winter rainfall) + (β2 * Average summer temperature) + (β3 * Harvest rainfall) +  (β4 * Age of Vintage)

It created an uproar in the professional wine community. Famed wine taster Robert Parker even called the economist “an absolute total sham.”

Why? Ashenfelter’s simple formula meant a merchant could determine the quality of a wine long before it was drinkable, turning the world of wine ratings and prices on its head. Surely you needed an expert who possessed years of experience, and who had drunk lots of wine, to be able to assess a wine’s quality.

Not at all, it turned out.

Algorithms Answer Big Questions

An algorithm, a process for calculating a range of inputs to come up with an output, can make unpredictable events—no matter future wine prices or equities in the stock market—more predictable. This is because algorithms don’t get distracted by irrelevant information, short-term fads, or social factors. In fact, algorithms today are able to beat humans in many specific areas, including chess, Jeopardy, baseball, and medical diagnoses, to name a few.

At Betterment, we use  algorithms to optimize our portfolio—that is the basis of our advice. No intuition, no insider connections, no hunches. Just math.

Shifting a Whole Industry, One Formula at a Time

According to the book “Thinking, Fast and Slow,” author Daniel Kahneman, who won the Nobel Prize for economics in 2002, said that in nearly all fields where predictability is difficult, such as sports to school admissions and market pricing, experts are often inferior to algorithms. Human memory is biased, and we are inconsistent at summarizing complex information. “Whenever we can replace human judgement by a formula, we should at least consider it,” he said.

When it comes to something as significant as your life savings, the goal should be to minimize any margin for error or judgement calls. You should be able to accurately hit the mark you are aiming for with your investments (provided you do your part to save). That’s what Betterment’s automated investing does for you.

Indeed, Betterment’s algorithms do things with a level or precision that DIY investors cannot match, including trading in fractional shares and automatically rebalancing your investments with dividends in a tax-efficient manner. Not only can algorithms handle what humans once did: They can do it better.

Soon after the grapes were harvested—and long before they could be tasted—Ashenfelter predicted that grapes from 1989 would be an excellent vintage. The experts grumbled at the time, but Ashenfelter’s formula proved right—that vintage was deemed excellent by those very tasters a full decade later.

 To be sure, the wine world has now come to accept technology, including pricing algorithms and metrics, as part of the industry. The same is true for investing: based on what we know to be true, we believe algorithms can manage your investments better than manual investment services.

With contributions by Catherine New. 

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