Research on Asset Portfolio Construction Based on News Sentiment Analysis
Research Article
Open Access
CC BY

Research on Asset Portfolio Construction Based on News Sentiment Analysis

Weiye Ju 1, Qiuyao Jiang 2*
1 University of Southampton
2 Shanghai University
*Corresponding author: jiangqy@ldy.edu.rs
Published on 4 July 2025
Volume Cover
AEMPS Vol.193
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-201-0
ISBN (Online): 978-1-80590-202-7
Download Cover

Abstract

News sentiment can reflect investors’ attitudes toward specific news and their potential investment adjustments based on that sentiment. News sentiment strongly influences investor behavior, offering key advantages to financial market participants. This article uses news sentiment analysis as a new tool and tries to build a portfolio based on this sentiment to test how it works in the capital market. For listed companies, stock prices serve as a clear indicator shaped by investor sentiment—often driven by news coverage. This study uses a CNN-LSTM model to forecast stock prices of target firms and employs FinBERT for sentiment analysis of news from major listed companies (2021–2024). Results show the CNN-LSTM model achieves higher prediction accuracy than methods using only market data or FinBERT. Additionally, a daily rebalanced portfolio was built using the Black-Litterman model, integrating predicted returns and market equilibrium. This portfolio delivered substantial annualized returns and a high Sharpe ratio. The framework provides investors with a novel strategy and offers researchers innovative analytical tools for financial time-series and capital markets.

Keywords:

News sentiment analysis, Stock price forecasting, CNN-LSTM model, FinBERT, Portfolio construction

View PDF
Ju,W.;Jiang,Q. (2025). Research on Asset Portfolio Construction Based on News Sentiment Analysis. Advances in Economics, Management and Political Sciences,193,181-190.

References

[1]. Fabozzi, F. J., Markowitz, H. M., & Gupta, F. (2008). Portfolio selection. Handbook of finance, 2, 3-13.

[2]. Black, F., & Litterman, R. (1990). Asset allocation: combining investor views with market equilibrium. Goldman Sachs Fixed Income Research, 115(1), 7-18.

[3]. Kocuk, B., & Cornuéjols, G. (2020). Incorporating Black-Litterman views in portfolio construction when stock returns are a mixture of normals. Omega, 91, 102008.

[4]. Stoilov, T., Stoilova, K., & Vladimirov, M. (2021). Application of modified Black-Litterman model for active portfolio management. Expert Systems with Applications, 186, 115719.

[5]. Sahamkhadam, M., Stephan, A., & Östermark, R. (2022). Copula-based Black–Litterman portfolio optimization. European Journal of Operational Research, 297(3), 1055-1070.

[6]. Barua, R., & Sharma, A. K. (2023). Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach. Finance Research Letters, 58, 104515.

[7]. Ko, H., Son, B., & Lee, J. (2024). A novel integration of the Fama–French and Black–Litterman models to enhance portfolio management. Journal of International Financial Markets, Institutions and Money, 91, 101949.

[8]. Hung, M. C., Hsia, P. H., Kuang, X. J., & Lin, S. K. (2024). Intelligent portfolio construction via news sentiment analysis. International Review of Economics & Finance, 89, 605-617.

[9]. Zhang, C., Gao, B., Xu, X., & Qin, M. (2025). MFA RPC news sentiment and stock returns. Pacific-Basin Finance Journal, 92, 102779.

[10]. Liu, Y., & Huang, Y. (2025). A Multimodal Deep Learning Framework for Constructing a Market Sentiment Index from Stock News. Big Data Research, 100535.

[11]. Gong, Y., Peng, Y., Xu, L., Chen, K., & Shi, W. (2025). Shipping news sentiment as a predictor of iron ore freight rates: Hybrid evidence from lexicon-based analysis and threshold autoregression modelling. Transport Policy.

[12]. Li, Y., Qiao, Y., & Lei, S. (2025). Ripple effect of ESG sentiment: How news stirs the waves in China's A-share market. International Review of Financial Analysis, 97, 103856.

[13]. Leone, F., Marazzina, D., & Rosamilia, N. (2025). What’s news with you: Price forecasting with global and ESG sentiment scores. Finance Research Open, 100013.

References

[1]. Fabozzi, F. J., Markowitz, H. M., & Gupta, F. (2008). Portfolio selection. Handbook of finance, 2, 3-13.

[2]. Black, F., & Litterman, R. (1990). Asset allocation: combining investor views with market equilibrium. Goldman Sachs Fixed Income Research, 115(1), 7-18.

[3]. Kocuk, B., & Cornuéjols, G. (2020). Incorporating Black-Litterman views in portfolio construction when stock returns are a mixture of normals. Omega, 91, 102008.

[4]. Stoilov, T., Stoilova, K., & Vladimirov, M. (2021). Application of modified Black-Litterman model for active portfolio management. Expert Systems with Applications, 186, 115719.

[5]. Sahamkhadam, M., Stephan, A., & Östermark, R. (2022). Copula-based Black–Litterman portfolio optimization. European Journal of Operational Research, 297(3), 1055-1070.

[6]. Barua, R., & Sharma, A. K. (2023). Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach. Finance Research Letters, 58, 104515.

[7]. Ko, H., Son, B., & Lee, J. (2024). A novel integration of the Fama–French and Black–Litterman models to enhance portfolio management. Journal of International Financial Markets, Institutions and Money, 91, 101949.

[8]. Hung, M. C., Hsia, P. H., Kuang, X. J., & Lin, S. K. (2024). Intelligent portfolio construction via news sentiment analysis. International Review of Economics & Finance, 89, 605-617.

[9]. Zhang, C., Gao, B., Xu, X., & Qin, M. (2025). MFA RPC news sentiment and stock returns. Pacific-Basin Finance Journal, 92, 102779.

[10]. Liu, Y., & Huang, Y. (2025). A Multimodal Deep Learning Framework for Constructing a Market Sentiment Index from Stock News. Big Data Research, 100535.

[11]. Gong, Y., Peng, Y., Xu, L., Chen, K., & Shi, W. (2025). Shipping news sentiment as a predictor of iron ore freight rates: Hybrid evidence from lexicon-based analysis and threshold autoregression modelling. Transport Policy.

[12]. Li, Y., Qiao, Y., & Lei, S. (2025). Ripple effect of ESG sentiment: How news stirs the waves in China's A-share market. International Review of Financial Analysis, 97, 103856.

[13]. Leone, F., Marazzina, D., & Rosamilia, N. (2025). What’s news with you: Price forecasting with global and ESG sentiment scores. Finance Research Open, 100013.

Cite this article

Ju,W.;Jiang,Q. (2025). Research on Asset Portfolio Construction Based on News Sentiment Analysis. Advances in Economics, Management and Political Sciences,193,181-190.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICEMGD 2025 Symposium: Innovating in Management and Economic Development

ISBN: 978-1-80590-201-0(Print) / 978-1-80590-202-7(Online)
Editor: Florian Marcel Nuţă Nuţă, Ahsan Ali Ashraf
Conference website: https://2025.icemgd.org/Lahore.html
Conference date: 23 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.193
ISSN: 2754-1169(Print) / 2754-1177(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

For authors
For editors
For reviewers

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.