Portfolio Management by Anomaly Detection with Autoencoder in Forex Markets

Abstract

There are three main factors known to exist in the foreign exchange market: carry, value, and trend, but the effectiveness of these factors is temporally changed by global market conditions. In this research, we extract latent factors from foreign exchange rates by a data-driven approach using the autoencoder. Then, we evaluate the possibility of market mispricing by the concept of anomaly detection, considering the exchange rate restored by the autoencoder as the theoretical value expressed by the factor-risk premiums. Moreover, assuming that the misprice is immediately modified into the reasonable value, we construct a portfolio of multiple currency pairs based on the modification process of mispricing. As a result, we confirmed the possibility of obtaining the excess return (i.e., alpha) as in the stock portfolio management.