Data mining–based stock price prediction using hybridization of technical and fundamental analysis

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Data Technologies and Applications Pub Date : 2023-03-20 DOI:10.1108/dta-04-2022-0142
Jasleen Kaur, Khushdeep Dharni
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引用次数: 2

Abstract

PurposeThe stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.Design/methodology/approachWe chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.FindingsThe results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.Originality/valueAs stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.
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基于数据挖掘的技术分析和基本面分析相结合的股价预测
股票市场产生了大量的各种金融公司数据库,这些数据库高度波动和复杂。为了预测这些公司的每日股价,投资者经常使用技术分析或基本面分析。数据挖掘技术与基础和技术分析相结合,有可能为股票市场预测提供令人满意的结果。在当前的论文中,通过使用技术和基本面分析变量的组合方法来创建数据挖掘预测模型,努力研究股票市场预测的准确性。设计/方法/方法我们从印度国家证券交易所的CNX 500指数中选择了381家公司,并进行了两阶段的数据分析。第一阶段是确定关键的基本变量,并在此基础上构建投资组合。采用人工神经网络(ANN)、支持向量机(SVM)和决策树J48建立模型。第二阶段需要应用技术分析来预测投资组合中公司的价格走势。使用人工神经网络和支持向量机技术为投资组合中的所有公司创建预测模型。我们还根据模型的输出使用交易决策来估计回报,然后将其与买入并持有的回报和作为基准的NIFTY 50指数的回报进行比较。结果表明,两种投资组合的收益均高于基准买入并持有策略的收益。可以得出结论,无论股票类型如何,数据挖掘技术都能提供更好的结果,并且有能力弥补糟糕的股票。将投资组合的回报与NIFTY的回报作为基准进行比较也表明,与NIFTY产生的回报相比,这两种投资组合产生的回报都更高。由于股票价格同时受到技术指标和基本面指标的影响,本文探讨了技术分析和基本面分析变量对印度股市预测的联合作用。此外,还对个别分析所得的结果进行了比较。研究中提出的方法也可以利用基于趋势的研究来确定是长期持有股票还是短期持有股票。
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来源期刊
Data Technologies and Applications
Data Technologies and Applications Social Sciences-Library and Information Sciences
CiteScore
3.80
自引率
6.20%
发文量
29
期刊介绍: Previously published as: Program Online from: 2018 Subject Area: Information & Knowledge Management, Library Studies
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