Kittanai Yamkleeb, Sumalee Sangamuang, and Prompong Sungunnasil
Published in Data Science and Engineering (DSE) Record 2026 Vol. 7 No. 1 pp. 63-81
Abstract
Translating financial return predictions into effective portfolio decisions remains challenging due to the gap between predictive accuracy and investment performance. This study presents a prediction-to-decision framework that integrates deep learning-based return forecasting with Omega-based portfolio optimization. Using daily OHLCV data from 2018-2024, an autoencoder-long short-term memory (AE+LSTM) model is used to generate return forecasts, which are incorporated into a worst-case Omega allocation scheme to account for asymmetric return preferences. Forecasting performance is evaluated against autoregressive and neural baselines using both numerical error metrics (MAE, MSE) and directional measures (Hit Rate), while portfolio performance is assessed under consistent rebalancing rules with transaction costs and compared with equally weighted and mean-variance benchmarks. Out-of-sample backtesting across different market regimes examines annualized return, volatility, sharpe ratio, and maximum drawdown. The results suggest that differences in directional prediction behavior are associated with variations in portfolio-level outcomes under Omega-based allocation. In particular, models with more consistent directional patterns tend to provide a more balanced trade-off between return, risk, and turnover. Overall, the framework offers a systematic approach for examining how predictive signals translate into portfolio decisions across varying market conditions.