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Stock Market Forecasting Using LASSO Linear Regression Model

  • Sanjiban Sekhar Roy
  • Dishant Mittal
  • Avik Basu
  • Ajith Abraham
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 334)

Abstract

Predicting stock exchange rates is receiving increasing attention and is a vital financial problem as it contributes to the development of effective strategies for stock exchange transactions. The forecasting of stock price movement in general is considered to be a thought-provoking and essential task for financial time series’ exploration. In this paper, a Least Absolute Shrinkage and Selection Operator (LASSO) method based on a linear regression model is proposed as a novel method to predict financial market behavior. LASSO method is able to produce sparse solutions and performs very well when the numbers of features are less as compared to the number of observations. Experiments were performed with Goldman Sachs Group Inc. stock to determine the efficiency of the model. The results indicate that the proposed model outperforms the ridge linear regression model.

Keywords

Root Mean Square Error Stock Market Stock Price Mean Absolute Percentage Error Stock Market Forecast 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sanjiban Sekhar Roy
    • 1
  • Dishant Mittal
    • 1
  • Avik Basu
    • 1
  • Ajith Abraham
    • 2
    • 3
  1. 1.School of Computing Science and EngineeringVIT UniversityVelloreIndia
  2. 2.IT4InnovationsVSB - Technical University of OstravaOstravaCzech Republic
  3. 3.Machine Intelligence Research Labs (MIR Labs)WashingtonUSA

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