Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Management Research and Economic Development
Series Vol. 83 , 24 May 2024
Open
Access | Article
Application of Machine Learning Models in Asset Allocation
* Author to whom correspondence should be addressed.
Abstract
This study embarks on an exploration of machine learning (ML) models in forecasting the trends of stock indices, with a specific focus on different industries within the Chinese market. Moving beyond the confines of traditional linear regression and standard multi-factor approaches, our research adopts a multi-dimensional analytical framework to decode the complex relationships between various factors and the future returns of industry-specific Exchange-Traded Funds (ETFs) in China. The paper innovatively applies both linear and nonlinear ML models to predict directional shifts in ETF returns, a domain not extensively studied previously. We conduct a thorough comparative analysis of these models, assessing their predictive prowess and dissecting the influence of diverse factors on different industry sectors. This investigation reveals distinct patterns and factor sensitivities unique to each sector, offering new insights into their dynamics. The results are pivotal for asset allocation and investment strategies, as they highlight the nuanced role of ML in financial forecasting. By bridging the gap between traditional financial models and advanced ML techniques, our study presents a novel perspective that enriches the strategic planning in financial markets, especially in the context of the rapidly evolving Chinese economy.
Keywords
machine learning, financial assets, price forecasting, Financial asset allocation
References
1. Zhang, Y., & Wang, L. (2022). "Trends in Factor Investing and A-Share Market Dynamics," Journal of Financial Markets Research.
2. Sharpe, W.F. (1964). "Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk," Journal of Finance.
3. Ross, S.A. (1976). "The Arbitrage Theory of Capital Asset Pricing," Journal of Economic Theory.
4. Nguyen, H., & Lee, J. (2019). "Factor Selection in Equity Markets: Using Logistic Regression and Regularization," Journal of Quantitative Finance.
5. Smith, A., & Zhao, X. (2020). "Enhancing Financial Prediction Models with AdaBoost Algorithm," Finance and Data Science
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International 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 Instruction).
- Volume Title
- Proceedings of the 2nd International Conference on Management Research and Economic Development
- ISBN (Print)
- 978-1-83558-431-6
- ISBN (Online)
- 978-1-83558-432-3
- Published Date
- 24 May 2024
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/83/20240720
- Copyright
- 24 May 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited