  Stock return predictability is an important research theme as it reflects our
economic and social organization, and significant efforts are made to explain
the dynamism therein. Statistics of strong explanative power, called "factor"
have been proposed to summarize the essence of predictive stock returns.
Although machine learning methods are increasingly popular in stock return
prediction, an inference of the stock returns is highly elusive, and still most
investors, if partly, rely on their intuition to build a better decision
making. The challenge here is to make an investment strategy that is consistent
over a reasonably long period, with the minimum human decision on the entire
process. To this end, we propose a new stock return prediction framework that
we call Ranked Information Coefficient Neural Network (RIC-NN). RIC-NN is a
deep learning approach and includes the following three novel ideas: (1)
nonlinear multi-factor approach, (2) stopping criteria with ranked information
coefficient (rank IC), and (3) deep transfer learning among multiple regions.
Experimental comparison with the stocks in the Morgan Stanley Capital
International (MSCI) indices shows that RIC-NN outperforms not only
off-the-shelf machine learning methods but also the average return of major
equity investment funds in the last fourteen years.
