{"title":"金融中的机器学习:主要应用、问题、指标和未来趋势","authors":"Nawaf Almaskati","doi":"10.1142/s2424786322500104","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":54088,"journal":{"name":"International Journal of Financial Engineering","volume":" ","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2022-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Machine learning in finance: Major applications, issues, metrics, and future trends\",\"authors\":\"Nawaf Almaskati\",\"doi\":\"10.1142/s2424786322500104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":54088,\"journal\":{\"name\":\"International Journal of Financial Engineering\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2022-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Financial Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/s2424786322500104\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Financial Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/s2424786322500104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Machine learning in finance: Major applications, issues, metrics, and future trends
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.