Development of stock market trend prediction system using multiple regression

  • Muhammad Zubair AsgharEmail author
  • 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.


Stock market Prediction Data sparseness Multiple regression Stock predictors 


Author contributions

MZA and FR conceived and designed the experiments; FR and FMK performed the experiments; FR and SA analyzed the data; SA contributed reagents/materials/analysis tools; MZA wrote the paper.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

10588_2019_9292_MOESM1_ESM.rar (353 kb)
Supplementary material 1 (RAR 353 kb)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Computing and Information TechnologyGomal UniversityDera Ismail Khan (KP)Pakistan
  2. 2.Faculty of Computing and Information Technology in Rabigh (FCITR)King Abdul Aziz University (KAU)Jeddah (Rabigh)Saudi Arabia

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