Deep momentum networks with market trend dynamics.

ABSTRACT: Time-series momentum (TSMOM) trading strategies manage positions based on the persistence of return trends. Although long short-term memory (LSTM) deep neural architectures can enhance TSMOM, their performance often deteriorates during abrupt market trend changes. This study aims to improve TSMOM performance, particularly at critical moments marked by significant shifts in long- and short-term trends. To achieve this, we combine short- and long-term signals into a comprehensive market-state representation, employing supervised learning to incorporate these market dynamics into the proposed model. In our experiments, we generate market-state features, referred to as MTDP scores, by numerically capturing changes in market trends via an extreme gradient boosting (XGBoost) process. These MTDP

SUBMITTER: Song J 

PROVIDER: S-EPMC12404547 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature