Long-Term Machine Learning for Financial Portfolio Management

Abstract

In asset management businesses, such as portfolio management, it is common to operate in the medium to long term due to the increase in operational burden and transaction costs. However, to compose a longer-term model, the number of usable learning data decreases due to the larger observation interval of the data; hence, the model performance declines. To solve this problem, in this study, a data augmentation was conducted by the combined use of data of multiple time scales, and confirm its effectiveness to keep a better generalization ability of trained models even if the target task of machine-learning methods is longer time scale. In addition, portfolio management was conducted using the constructed model.