In 1962, Ed Thorp, famed hedge fund manager and mathematics professor, wrote the book Beat the Dealer in which he proved that it was mathematically possible to overcome house odds in blackjack by properly “counting cards.” Ever since then, entrepreneurial would-be gamblers have learned his and other methods to stack the odds in their favor when playing blackjack. Done properly, it is a known fact that these “gamblers” have a true statistical edge.
Additionally, assuming proper bankroll management, they also know that the only way to monetize their edge is to continue playing even when recent performance has not been successful. Given enough time, the edge will work in their favor and they will earn back their losses and generate profits. The key here is time; however, the stress of mounting losses can make minutes or hours seem like days or weeks (or even years), and may lead a player to give in before they are able to earn back their losses.
One method for minimizing the time needed to earn back losses is to play more hands. Since a single player is limited in the number of hands they can actually play, a more effective strategy is to put together a team of similarly skilled players that each play individually, but share equally in gains and losses.
Just as blackjack players can spread the risk among many players by forming a team with similar skill level, quantitative models can reduce the probability of experiencing extended losses by forming a “team” of similar, but separate models; a methodology we refer to as model ensembling.
By doing so, chances are increased that when a model is on a losing streak, those losses will be offset by another model having better-than-expected outcomes. This should limit the depth of losses and decrease the time needed to earn back lost capital.
As we continually challenge ourselves to identify ways to improve model prediction and minimize exposure to randomness, it seemed natural that we should research the idea of ensembling.