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Development of stock market trend prediction system using multiple regression

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Abstract

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.

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References

  • Ahmadifard M, Sadenejad F, Mohammadi I, Aramesh K (2013) Forecasting stock market return using ANFIS: the case of Tehran Stock Exchange. Int J Adv Stud Hum Soc Sci 1(5):452–459

    Google Scholar 

  • Ariyo AA, Adewumi AO, Ayo CK (2014) Stock price prediction using the ARIMA model. In: Proceedings of the 16th international conference on computer modelling and simulation (UKSim), 2014 UKSim-AMSS, pp 106–112. IEEE

  • Ariyo AA, Adewumi AO, Ayo CK (2014). Stock price prediction using the ARIMA model. In: Proceedings of the 16th international conference on computer modelling and simulation (UKSim), 2014 UKSim-AMSS, pp 106–112. IEEE

  • Devi BU, Sundar D, Alli P (2013) An effective time series analysis for stock trend prediction using ARIMA model for nifty midcap-50. Int J Data Min Knowl Manag Process 3(1):65

    Article  Google Scholar 

  • Enke D, Grauer M, Mehdiyev N (2011) Stock market prediction with multiple regression, fuzzy type-2 clustering and neural networks. Procedia Comput Sci 6:201–206

    Article  Google Scholar 

  • Feinberg A, Genethliou D (2005) Load forecasting. In: Chow, J., Wu, F., Momoh, J. (eds) Applied mathematics for restructured electric power systems. Springer, Berlin, pp 269–285

  • Javaid U (2010) Determinants of equity prices in the stock market. Res J Commerce Econ Soc Sci 4(1):98–114

    Google Scholar 

  • Kabacoff R (2015) R in action: data analysis and graphics with R. Manning Publications Co., Shelter Island

    Google Scholar 

  • Kamley S, Jaloree S, Thaku RS (2013) Multiple regression: a data mining approach for predicting the stock market trends. Int J Comput Sci Eng Inf Technol Res 3(4):173–180

    Google Scholar 

  • Khaidem L, Saha S, Dey SR (2016) Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003

  • Ladan MS, Karim AM, Adekeye KS (2014) Multiple regressions on some selected macroeconomic variables on stock market returns from 1986–2010. Adv Econ Int Financ 1(1):1–11

    Google Scholar 

  • Mondal P, Shit L, Goswami S (2014) Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. Int J Comput Sci Eng Appl 4(2):13

    Google Scholar 

  • Nagar A, Hahsler M (2012) News sentiment analysis using R to predict stock market trends, vol 2. pp 1–20

  • Nasseri A, Tucker A, Cesare S (2015) Quantifying StockTwits semantic terms’ trading behavior in financial markets: an effective application of decision tree algorithms. Expert Syst Appl 42(23):192–210

    Article  Google Scholar 

  • Park NJ, George KM, Park NA (2010) A multiple regression model for trend change prediction. In: Proceedings of international conference on financial theory and engineering (ICFTE), 2009, pp 1–5

  • Qasem M, Thulasiram R, Thulasiram P (2015) Twitter sentiment classification using machine learning techniques for stock markets. In: Proceedings of IEEE international conference on advances in computing, communications and informatics (ICACCI), pp 834–840

  • Qin Y, Song D, Cheng H, Cheng W, Jiang G, Cottrell G (2017) In: Proceedings of the international joint conference on artificial intelligence (IJCAI)

  • Rao T, Srivastava S (2012) Analyzing stock market movements using twitter sentiment analysis. In Proceedings of the IEEE international conference on advances in social networks analysis and mining (ASONAM 2012), pp 1–5

  • Reddy BS (2010) Prediction of stock market indices—using SAS. In: Proceedings of 2nd IEEE international conference on information and financial engineering (ICIFE), pp 112–116

  • Shen S, Jiang H, Zhang T (2012) Stock market forecasting using machine learning algorithms. Stanford University, Stanford, pp 1–5

    Google Scholar 

  • Simon S, Raoot A (2012) Accuracy driven artificial neural networks in stock market prediction. Int J Soft Comput 3(2):35–44

    Article  Google Scholar 

  • Soni S, Shrivastava S (2010) Classification of Indian stock market data using machine learning algorithms. Int J Comput Sci Eng 2(9):2942–2946

    Google Scholar 

  • Yuan J, Luo Y (2014) Test on the validity of futures market’s high frequency volume and price on forecast. In: Proceedings of IEEE international conference on management of e-Commerce and e-Government (ICMeCG), pp 28–32

  • Zhang L, Zhang L, Teng W, Chen Y (2013) Based on information fusion technique with data mining in the application of finance early-warning. Procedia Comput Sci 17:695–703

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Institute of Computing and Information Technology, Gomal University, Dera Ismail Khan (KP), Pakistan

    Muhammad Zubair Asghar, Fazal Rahman & Fazal Masud Kundi

  2. Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdul Aziz University (KAU), Jeddah (Rabigh), Saudi Arabia

    Shakeel Ahmad

Authors
  1. Muhammad Zubair Asghar
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  2. Fazal Rahman
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  3. Fazal Masud Kundi
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  4. Shakeel Ahmad
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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.

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Correspondence to Muhammad Zubair Asghar.

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Asghar, M.Z., Rahman, F., Kundi, F.M. et al. Development of stock market trend prediction system using multiple regression. Comput Math Organ Theory 25, 271–301 (2019). https://doi.org/10.1007/s10588-019-09292-7

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  • DOI: https://doi.org/10.1007/s10588-019-09292-7

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