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Cumulative Prospect Theory - Overestimating Success and Underestimating Risk in Thoroughbred Selecti

In any market where there is buying and selling, there is overestimation and underestimation of risk and reward by humans. The Thoroughbred market is no different.

I had a meeting earlier in the week with Dr Jill Stowe, Associate Professor in the Department of Economics at the University of Kentucky. Dr Stowe's background is in behavioural economics but her passion is in thoroughbred markets and she has published some very interesting papers on pricing in thoroughbred markets.

During the conversation (Ian Tapp from Marketwatch was also there), one of the more interesting aspects discussed was the estimation of risk or the probability weighting that buyers undertake at yearling sales. The basis of this is what is known as the cumulative prospect theory, which was proposed by Amos Tversky and Daniel Kahneman. The main observation of cumulative prospect theory is that people tend to think of possible outcomes usually relative to a certain reference point rather than to the final status, a phenomenon which is called framing effect. Moreover, they have different risk attitudes towards gains and losses and care generally more about potential losses than potential gains (loss aversion). Finally, and more specifically to thoroughbred markets, people tend to overweight extreme, but unlikely events, but underweight "average" events.

The Value of Stallion Success at Yearling Sales

In Thoroughbred Yearling (or Weanling and 2YO) markets, one of the more common examples of cumulative prospect theory is the observation that we tend to overpay for the unproven sire, where his genetic merit is unknown and underpay for the proven sire where his genetic merit is known. We do this by overweighing the likelihood of an unproven sire "making it" and paying more for their offspring, despite the fact that we have data to show the high failure rate of stallions, and both underpaying and underestimating the rate of return for stallions that have solid numbers and get results year in year out. Somewhat perversely, as we know more about a stallion, and have better access to data and racetrack results to know his genetic merit, we still underestimate his value.

How do we know this occurs? In the weeks to come I am going to show you through the use of a k-means algorithm for selection just how irrational the market is in terms of selection, how much money is wasted on unproven sires and how we overweight the extreme event (a young sire making it) but underweight the most likely (a proven sire continuing to sire good horses).

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