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.
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Roy, S.S., Mittal, D., Basu, A., Abraham, A. (2015). Stock Market Forecasting Using LASSO Linear Regression Model. In: Abraham, A., Krömer, P., Snasel, V. (eds) Afro-European Conference for Industrial Advancement. Advances in Intelligent Systems and Computing, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-319-13572-4_31
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DOI: https://doi.org/10.1007/978-3-319-13572-4_31
Publisher Name: Springer, Cham
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