{"title":"机器学习在股票市场预测中的应用","authors":"Memoona Shaheen, M. Arshad","doi":"10.47672/EJT.634","DOIUrl":null,"url":null,"abstract":"Objective: The objective of this study was to examine and determine future directions in regard to future machine learning techniques based on the review of the current literature. \nMethodology: A systematic review has been used to review the current trends from the peer-reviewed journal articles in the past twenty years. For this study, four categories have been categorized, the use of neural networks, support vector machines, the use of a genetic algorithm, and the combination of hybrid techniques. Studies in each of these categorize have been evaluated. \nFinding: Firstly, there is a strong link between machine learning methods and the prediction problems they are associated with. The second conclusion that we can conclude from this review is that past studies need to improve its generalizability results. Most of the studies that have been reviewed in this analysis has only used the machine learning systems through the use of one market or during only a one time period without taking into consideration whether the system would be adaptable in other situations and conditions. Limitations, future trends, as well as policy implications have been defined.","PeriodicalId":55090,"journal":{"name":"Glass Technology-European Journal of Glass Science and Technology Part a","volume":"1 1","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Use of Machine Learning in Stock Market Prediction\",\"authors\":\"Memoona Shaheen, M. Arshad\",\"doi\":\"10.47672/EJT.634\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Objective: The objective of this study was to examine and determine future directions in regard to future machine learning techniques based on the review of the current literature. \\nMethodology: A systematic review has been used to review the current trends from the peer-reviewed journal articles in the past twenty years. For this study, four categories have been categorized, the use of neural networks, support vector machines, the use of a genetic algorithm, and the combination of hybrid techniques. Studies in each of these categorize have been evaluated. \\nFinding: Firstly, there is a strong link between machine learning methods and the prediction problems they are associated with. The second conclusion that we can conclude from this review is that past studies need to improve its generalizability results. Most of the studies that have been reviewed in this analysis has only used the machine learning systems through the use of one market or during only a one time period without taking into consideration whether the system would be adaptable in other situations and conditions. Limitations, future trends, as well as policy implications have been defined.\",\"PeriodicalId\":55090,\"journal\":{\"name\":\"Glass Technology-European Journal of Glass Science and Technology Part a\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2020-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Glass Technology-European Journal of Glass Science and Technology Part a\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.47672/EJT.634\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATERIALS SCIENCE, CERAMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Glass Technology-European Journal of Glass Science and Technology Part a","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.47672/EJT.634","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, CERAMICS","Score":null,"Total":0}
Use of Machine Learning in Stock Market Prediction
Objective: The objective of this study was to examine and determine future directions in regard to future machine learning techniques based on the review of the current literature.
Methodology: A systematic review has been used to review the current trends from the peer-reviewed journal articles in the past twenty years. For this study, four categories have been categorized, the use of neural networks, support vector machines, the use of a genetic algorithm, and the combination of hybrid techniques. Studies in each of these categorize have been evaluated.
Finding: Firstly, there is a strong link between machine learning methods and the prediction problems they are associated with. The second conclusion that we can conclude from this review is that past studies need to improve its generalizability results. Most of the studies that have been reviewed in this analysis has only used the machine learning systems through the use of one market or during only a one time period without taking into consideration whether the system would be adaptable in other situations and conditions. Limitations, future trends, as well as policy implications have been defined.
期刊介绍:
The Journal of the Society of Glass Technology was published between 1917 and 1959. There were four or six issues per year depending on economic circumstances of the Society and the country. Each issue contains Proceedings, Transactions, Abstracts, News and Reviews, and Advertisements, all thesesections were numbered separately. The bound volumes collected these pages into separate sections, dropping the adverts. There is a list of Council members and Officers of the Society and earlier volumes also had lists of personal and company members.
JSGT was divided into Part A Glass Technology and Part B Physics and Chemistry of Glasses in 1960.