文章基本信息

标题：A review of data mining methods in financial markets
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作者：Haihua Liu ; Shan Huang ; Peng Wang 等
期刊名称：Data Science in Finance and Economics
电子版ISSN：2769-2140
出版年度：2021
卷号：1
期号：4
页码：362-392
DOI：10.3934/DSFE.2021020
语种：English
出版社：AIMS Press
摘要：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.