Author:
Liu Haihua, ,Huang Shan,Wang Peng,Li Zejun,
Abstract
<abstract><p>Financial activities are closely related to human social life. Data mining plays an important role in the analysis and prediction of financial markets, especially in the context of the current era of big data. However, it is not simple to use data mining methods in the process of analyzing financial data, due to the differences in the background of researchers in different disciplines. This review summarizes several commonly used data mining methods in financial data analysis. The purpose is to make it easier for researchers in the financial field to use data mining methods and to expand the application scenarios of it used by researchers in the computer field. This review introduces the principles and steps of decision trees, support vector machines, Bayesian, K-nearest neighbors, k-means, Expectation-maximization algorithm, and ensemble learning, and points out their advantages, disadvantages and applicable scenarios. After introducing the algorithms, it summarizes the use of the algorithm in the process of financial data analysis, hoping that readers can get specific examples of using the algorithm. In this review, the difficulties and countermeasures of using data mining methods are summarized, and the development trend of using data mining methods to analyze financial data is predicted.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Reference178 articles.
1. Abdalmageed W, Elosery A, Smith CE (2003) Non-parametric expectation maximization: a learning automata approach. In IEEE International Conference on Systems, 2003.
2. Agrawal L, Adane D (2021) Improved decision tree model for prediction in equity market using heterogeneous data. IETE J Res, 1–10.
3. Ahn JJ, Oh KJ, Kim TY, et al. (2011) Usefulness of support vector machine to develop an early warning system for financial crisis. Expert Syst Appl 38: 2966–2973.
4. Alberici A, Querci F (2015) The quality of disclosures on environmental policy: The profile of financial intermediaries. Corp Soc Resp Env Ma 23: 283–296.
5. Aljawazneh H, Mora AM, Garcia-Sanchez P, et al. (2021) Comparing the performance of deep learning methods to predict companies' financial failure. IEEE Access 9: 97010–97038.
Cited by
9 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献