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Development of stock market trend prediction system using multiple regression
Computational and Mathematical Organization Theory ( IF 1.8 ) Pub Date : 2019-02-14 , DOI: 10.1007/s10588-019-09292-7
Muhammad Zubair Asghar , Fazal Rahman , Fazal Masud Kundi , Shakeel Ahmad

The Stock market trend prediction is an efficient medium for investors, public companies and government to invest money by taking into account the profit and risk. The existing studies on the development of stock-based prediction systems rely on data acquired from social media sources (sentiment-based) and secondary data sources (financial-sites). However, the data acquired from such sources is usually sparse in nature. Moreover, the selection of predictor variables is also poor, which ultimately degrades the performance of prediction model. The problems associated with existing approaches can be overcome by proposing an effective prediction model with improved quality of input data and enhanced selection/inclusion of predictor variables. This work presents the results of stock prediction by applying a multiple regression model using R software. The results obtained show that the proposed system achieved a prediction accuracy of 95% on KSE 100-index dataset, 89% on Lucky Cement, 97% on Abbot Company dataset. Furthermore, user-friendly interface is provided to assist individuals and companies to invest or not in a specific stock.

中文翻译:

基于多元回归的股票市场趋势预测系统的开发

股市趋势预测是投资者,上市公司和政府通过考虑利润和风险来进行投资的有效手段。基于股票的预测系统开发的现有研究依赖于从社交媒体源(基于情感)和辅助数据源(金融站点)获取的数据。但是,从此类来源获取的数据通常本质上是稀疏的。此外,预测变量的选择也很差,这最终会降低预测模型的性能。可以通过提出一种有效的预测模型来克服与现有方法相关的问题,该模型具有改进的输入数据质量和增强的预测变量选择/包含能力。通过使用R软件应用多元回归模型,这项工作展示了股票预测的结果。获得的结果表明,所提出的系统在KSE 100指数数据集上的预测准确性达到95%,在Lucky Cement上达到89%,在Abbot Company数据集上达到97%。此外,提供了用户友好的界面来帮助个人和公司投资或不投资特定股票。
更新日期:2019-02-14
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