Machine learning in finance: Major applications, issues, metrics, and future trends

被引:1
|
作者
Almaskati, Nawaf [1 ]
机构
[1] Univ Waikato, Waikato Management Sch, Hamilton, New Zealand
关键词
Machine learning; supervised learning; unsupervised learning; overfitting; evaluation metrics; asset pricing; SUPPORT VECTOR MACHINE; BANKRUPTCY PREDICTION; CORPORATE BANKRUPTCY; ENSEMBLE METHODS; STATEMENT FRAUD; NEURAL-NETWORKS; STOCK-MARKET; ALGORITHM; MODELS; RISK;
D O I
10.1142/S2424786322500104
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
This paper provides a summary of the current literature related to applying machine learning algorithms in the field of finance with a focus on three main areas: asset pricing, bankruptcy prediction and detection of financial reporting anomalies. The paper also briefly discusses the most popular machine learning techniques used in finance and provides a general overview of some important concepts such as generalization and over- and under-fitting as well as a discussion of potential remedies. Last, the paper summarizes the various indicators and metrics available to evaluate and compare the performance of regression and classification machine learning models before discussing general research trends and potential future research.
引用
收藏
页数:32
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