  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.
