{rfName}
AP

Indexed in

Altmetrics

Analysis of institutional authors

Garcia-Fernandez A.f.Author

Share

December 12, 2025
Publications
>
Proceedings Paper
No

APMPO: A Portfolio Management Policy Optimization Framework with Adaptive Reinforcement Learning Algorithm

Publicated to: ARTIFICIAL INTELLIGENCE IN EDUCATION. POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS, DOCTORAL CONSORTIUM AND BLUE SKY, AIED 2023. 2755 CCIS 389-404 - 2026-01-01 2755 CCIS(), DOI: 10.1007/978-981-95-4094-5_27

Authors:

Gu F; Jiang Z; García-Fernández ÁF; Stefanidis A; Su J; Li H
[+]

Affiliations

Universidad Politécnica de Madrid - Author
Xi'an Jiaotong-Liverpool University - Author

Abstract

Portfolio management refers to the strategic process of selecting and allocating financial assets to optimize returns while effectively managing risks. It encounters significant challenges in adapting to financial markets, managing nonlinear financial risks, and achieving sample efficiency in dynamic environments, which can result in poor robustness and decreased long-term performance. To address these challenges, this paper presents Adaptive Portfolio Management Policy Optimization (APMPO), a novel framework that, to the best of our knowledge, is the first to integrate neural stochastic differential equations within a Transformer architecture enhanced by meta-learning-inspired fast adaptation. APMPO introduces a multi-objective optimization scheme incorporating volatility-penalized advantage calculations and employs adversarial training combined with optimized experience replay for enhanced robustness. Comprehensive experiments conducted on five years of Dow Jones Industrial Average (DJIA) constituent stock data demonstrate that APMPO consistently outperforms existing methods, achieving at least 8.97% higher absolute cumulative returns and 24.9% higher Sharpe ratios than its competitors. These results suggest that APMPO provides a reliable and innovative solution to modern portfolio management challenges.
[+]

Keywords

Deep reinforcement learningPolicy optimizationPortfolio managementTransformer neural network

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal ARTIFICIAL INTELLIGENCE IN EDUCATION. POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS, DOCTORAL CONSORTIUM AND BLUE SKY, AIED 2023, Q4 Agency Scopus (SJR), its regional focus and specialization in Computer Science (Miscellaneous), give it significant recognition in a specific niche of scientific knowledge at an international level.

[+]

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-05:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 2.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 2 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 7.
[+]

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: China.

[+]