Long-Term Modeling of Financial Machine Learning for Active Portfolio Management
This addresses the challenge of long-term modeling for asset managers, but it is incremental as it builds on existing data augmentation techniques.
The study tackled the problem of declining learning precision in financial machine learning for long-term portfolio management due to reduced data, by applying data augmentation using shorter-term time scales, which inhibited generalization performance degradation. As an illustration, they used an autoencoder for a multifactor model and mispricing, confirming effectiveness in stock and FX markets.
In the practical business of asset management by investment trusts and the like, the general practice is to manage over the medium to long term owing to the burden of operations and increase in transaction costs with the increase in turnover ratio. However, when machine learning is used to construct a management model, the number of learning data decreases with the increase in the long-term time scale; this causes a decline in the learning precision. Accordingly, in this study, data augmentation was applied by the combined use of not only the time scales of the target tasks but also the learning data of shorter term time scales, demonstrating that degradation of the generalization performance can be inhibited even if the target tasks of machine learning have long-term time scales. Moreover, as an illustration of how this data augmentation can be applied, we conducted portfolio management in which machine learning of a multifactor model was done by an autoencoder and mispricing was used from the estimated theoretical values. The effectiveness could be confirmed in not only the stock market but also the FX market, and a general-purpose management model could be constructed in various financial markets.