preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202402.0635.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 10 February 2024 / Approved: 12 February 2024 / Online: 12 February 2024 (08:53:07 CET)

Developing effective trend estimators is a main method to solve the online portfolio selection problem. Although the existing portfolio strategies have demonstrated good performance through the development of various trend estimators, it is still challenging to determine in advance which estimator will yield the maximum final cumulative wealth in online portfolio selection tasks. This paper studies an online ensemble approach for online portfolio selection by leveraging the strengths of multiple trend estimators. Specifically, a return-based loss function and a cross-entropy-based loss function are first designed to evaluate the adaptiveness of different trend estimators in a financial environment. On this basis, a passive aggressive ensemble model is proposed to weigh these trend estimators within a unit simplex according to their adaptiveness. Extensive experiments are conducted on benchmark datasets from various real-world stock markets to evaluate the performance. The results show that the proposed strategy achieves the state-of-the-art performance including efficiency and cumulative return.

Online Portfolio Selection; Online Ensemble Learning; Passive Aggressive Algorithm

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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