Originally published in MIT Management, September 19, 2023.
Why It Matters
Relevance-based prediction can be used in finance, politics, and sports for more accurate forecasting.
What if we told you there was a new financial forecasting model accurate enough to predict the outcome of the next U.S. presidential election or next season’s NBA draft prospects?
The approach — relevance-based prediction — relies on a mathematical measure to account for unusualness. In classical statistics, “we’re told to be skeptical of outliers” because they could be data errors, said Mark Kritzman, senior lecturer in finance at MIT Sloan and a co-author of a paper outlining the approach.
But there’s a lot to be gained from emphasizing observations that are different from average, he said.
“Unusual events contain more information than common events,” said Kritzman, president and CEO of Windham Capital Management. “It’s almost like a controlled science experiment — when something really strange happens, that’s when relationships shine through.”
Relevance-based prediction originated in Kritzman’s research efforts in the late 1990s, when he was looking for ways to account for risk when constructing investment portfolios.
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