The applicability of self-play algorithms to trading and forecasting financial markets

Posth, Jan-Alexander; Kotlarz, Piotr Kamil; Hadji Misheva, Branka; Osterrieder, Jörg; Schwendner, Peter, 2021. Frontiers in Artificial Intelligence. 4(668465).

The central research question to answer in this study is whether the AI methodology of Self-Play can be applied to financial markets. In typical use-cases of Self-Play, two AI agents play against each other in a particular game, e.g., chess or Go. By repeatedly playing the game, they learn its rules as well as possible winning strategies. When considering financial markets, however, we usually have one player—the trader—that does not face one individual adversary but competes against a vast universe of other market participants. Furthermore, the optimal behavior in financial markets is not described via a winning strategy, but via the objective of maximizing profits while managing risks appropriately. Lastly, data issues cause additional challenges, since, in finance, they are quite often incomplete, noisy and difficult to obtain. We will show that academic research using Self-Play has mostly not focused on finance, and if it has, it was usually restricted to stock markets, not considering the large FX, commodities and bond markets. Despite those challenges, we see enormous potential of applying self-play concepts and algorithms to financial markets and economic forecasts.

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