Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 7th International Conference on Economic Management and Green Development
Series Vol. 34 , 10 November 2023
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Access | Article
Analysis of Multifactor Fundamentals Stock Selection Based on Backtesting
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Abstract
In recent years, quantitative finance has become a major trend for investing which brings stable returns with controllable risks. Among various different quantitative strategies, multifactorial stock selection strategy based on fundamental data (e.g., financial statements, macro- and micro-economy data) is one of the widely investigated strategies. On this basis, this study chooses Chinese listed company to verify the feasibility and effectiveness of the stock selection strategy. To be specific, the Ricequant platform is utilized to realize the backtesting as well as data retrieving in order to estimate and evaluate the performances of the strategies. According to the analysis, several indicators show great ability to gain extra returns compared with systematic risks and market performances. In other words, the feasibility of explicability of the quantitative strategy based on multifactorial model is verified in Chinese market. Overall, these results shed light on guiding further exploration of fundamental analysis of different underlying assets based on multifactorial analysis.
Keywords
quantitative finance, multifactorial model, fundamental analysis
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 7th International Conference on Economic Management and Green Development
- ISBN (Print)
- 978-1-83558-089-9
- ISBN (Online)
- 978-1-83558-090-5
- Published Date
- 10 November 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/34/20231680
- Copyright
- 10 November 2023
- 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