Machine Learning Based Financial Applications of Data
- DOI
- 10.2991/978-94-6463-459-4_17How to use a DOI?
- Keywords
- Machine Learning; Data Finance; Predictive Model; Risk Assessment; Investment Strategy
- Abstract
As a matter of fact, with the rapid development of big data and machine learning technology, the field of data finance is facing huge opportunities and challenges especially in recent years. With this in mind, based on machine learning model, this study makes a deep analysis and research on data finance. Through the application and comparison of multiple machine learning models, this paper finds that different models have different predictive effects on data finance. At the same time, this paper also discusses some frontier problems as well as challenges in the field of data finance in detail. According to the analysis, it provides ideas and references for future research. In the meantime, some practical application cases are added in order to more intuitively demonstrate the application of machine learning models in data finance. Overall, these results shed light on guiding further exploration of data finance in terms of machine learning scenarios.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Hangyi Li PY - 2024 DA - 2024/07/23 TI - Machine Learning Based Financial Applications of Data BT - Proceedings of the 2024 9th International Conference on Social Sciences and Economic Development (ICSSED 2024) PB - Atlantis Press SP - 137 EP - 142 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-459-4_17 DO - 10.2991/978-94-6463-459-4_17 ID - Li2024 ER -