Mojtaba Azimifar, Babak Nadjar Araabi, Hadi Moradi
{"title":"Forecasting stock market trends using support vector regression and perceptually important points","authors":"Mojtaba Azimifar, Babak Nadjar Araabi, Hadi Moradi","doi":"10.1109/ICCKE50421.2020.9303667","DOIUrl":null,"url":null,"abstract":"Intelligent stock trading systems use soft computing techniques in order to make trading decisions in the stock market. However, the fluctuations of the stock price make it difficult for the trading system to discover the underlying trends. In order to enable the trading system for trend prediction, this paper suggests using perceptually important points as a turning point prediction framework. Perceptually important points are utilized as a high-level representation for the stock price time series to decompose the price into several segments of uptrends and downtrends and define a trading signal which is an indicator of the current trend. A support vector regression model is trained on this high-level data to make trading decisions based on predicted trading signal. The performance of the proposed trading system is compared with three other trading systems on five of the top performing stocks in Tehran Stock Exchange, and obtained results show a significant improvement.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCKE50421.2020.9303667","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Intelligent stock trading systems use soft computing techniques in order to make trading decisions in the stock market. However, the fluctuations of the stock price make it difficult for the trading system to discover the underlying trends. In order to enable the trading system for trend prediction, this paper suggests using perceptually important points as a turning point prediction framework. Perceptually important points are utilized as a high-level representation for the stock price time series to decompose the price into several segments of uptrends and downtrends and define a trading signal which is an indicator of the current trend. A support vector regression model is trained on this high-level data to make trading decisions based on predicted trading signal. The performance of the proposed trading system is compared with three other trading systems on five of the top performing stocks in Tehran Stock Exchange, and obtained results show a significant improvement.