Proceedings of the 8th International Conference on Applied Research in Business, Management and Economics
Year: 2025
DOI:
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Integrating Machine Learning Methods for the Prediction in Online Portfolio Selection Problems
Zhonglin Liu, Yuqiao Zhao, Benmeng Lyu, and Wai-Ki Ching
ABSTRACT:
Online portfolio selection (OLPS) is a critical issue in computational finance. It sequentially updates portfolio allocations across multiple investment periods as new information becomes available. The main objective of OLPS is to maximize the final cumulative return, typically achieved through asset price prediction and portfolio optimization steps in each investment period. The properties of financial data, such as non-linearity, make certain machine learning methods applicable to the problem with potential benefits. To explore the effectiveness of integrating machine learning methods on OLPS, this work employs two machine learning models, the Long Short-Term Memory Networks (LSTM) and Extreme Gradient Boosting (XGBoost), on the asset price forecasting stage. These models are integrated with three optimization models: Mean-Variance, Max-Return, and On-Line Moving Average Reversion (OLMAR) to facilitate the decision-making process. For comparison purpose, a traditional price forecasting approach, the Exponential Moving Average (EMA) model, is utilized with the same optimization models as control groups. Numerical experiments are conducted using three commonly used public datasets, and the performance of the OLPS models is evaluated in terms of both final cumulative wealth and risk-adjusted return. The results indicate the advantages of incorporating machine learning models in various circumstances. Among the nine OLPS models, LSTM-based models outperform others in most scenarios. However, the effectiveness of XGBoost-based models varies depending on the optimization models and datasets used.
keywords: Online Portfolio Selection, Machine Learning, LSTM, XGBoost, Exponential Moving Average (EMA)