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Investors try to predict returns of financial assets to make successfulinvestment. Many quantitative analysts have used machine learning-based methodsto find unknown profitable market rules from large amounts of market data.However, there are several challenges in financial markets hindering practicalapplications of machine learning-based models. First, in financial markets,there is no single model that can consistently make accurate prediction becausetraders in markets quickly adapt to newly available information. Instead, thereare a number of ephemeral and partially correct models called "alpha factors".Second, since financial markets are highly uncertain, ensuring interpretabilityof prediction models is quite important to make reliable trading strategies. Toovercome these challenges, we propose the Trader-Company method, a novelevolutionary model that mimics the roles of a financial institute and tradersbelonging to it. Our method predicts future stock returns by aggregatingsuggestions from multiple weak learners called Traders. A Trader holds acollection of simple mathematical formulae, each of which represents acandidate of an alpha factor and would be interpretable for real-worldinvestors. The aggregation algorithm, called a Company, maintains multipleTraders. By randomly generating new Traders and retraining them, Companies canefficiently find financially meaningful formulae whilst avoiding overfitting toa transient state of the market. We show the effectiveness of our method byconducting experiments on real market data.

Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction