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Stock Market Forecasting Using LASSO Linear Regression Model
, Mittal D, Basu A, Abraham A.
Published in Springer International Publishing
Volume: 334
Pages: 371 - 381
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. © Springer International Publishing Switzerland 2015.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing Afro-European Conference for Industrial Advancement
PublisherData powered by TypesetSpringer International Publishing
Open Access0