  Investors try to predict returns of financial assets to make successful
investment. Many quantitative analysts have used machine learning-based methods
to find unknown profitable market rules from large amounts of market data.
However, there are several challenges in financial markets hindering practical
applications of machine learning-based models. First, in financial markets,
there is no single model that can consistently make accurate prediction because
traders in markets quickly adapt to newly available information. Instead, there
are a number of ephemeral and partially correct models called "alpha factors".
Second, since financial markets are highly uncertain, ensuring interpretability
of prediction models is quite important to make reliable trading strategies. To
overcome these challenges, we propose the Trader-Company method, a novel
evolutionary model that mimics the roles of a financial institute and traders
belonging to it. Our method predicts future stock returns by aggregating
suggestions from multiple weak learners called Traders. A Trader holds a
collection of simple mathematical formulae, each of which represents a
candidate of an alpha factor and would be interpretable for real-world
investors. The aggregation algorithm, called a Company, maintains multiple
Traders. By randomly generating new Traders and retraining them, Companies can
efficiently find financially meaningful formulae whilst avoiding overfitting to
a transient state of the market. We show the effectiveness of our method by
conducting experiments on real market data.
