Stock Market Forecasting Using LASSO Linear Regression Model
- 1.6k Downloads
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.
KeywordsRoot Mean Square Error Stock Market Stock Price Mean Absolute Percentage Error Stock Market Forecast
Unable to display preview. Download preview PDF.
- 1.Deng, S., Takashi, M., Kei, S., Tatsuro, S.: Akito Sakurai.: Combining technical analysis with sentiment analysis for stock price prediction. In: IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing (DASC), pp. 800–807. IEEE (2011)Google Scholar
- 2.Yoo, P.D., Kim, M.H., Jan, T.: Machine learning techniques and use of event information for stock market prediction: A survey and evaluation. In: International Conference on Computational Intelligence for Modeling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, vol. 2, pp. 835–841 (2005)Google Scholar
- 3.Schumann, M., Lohrbach, T.: Comparing artificial neural networks with statistical methods within the field of stock market prediction. In: Proceeding of the Twenty-Sixth Hawaii International Conference on in System Sciences, vol. 4, pp. 597–606. IEEE (1993)Google Scholar
- 4.Naeini, M.P., Taremian, H., Hashemi, H.B.: Stock market value prediction using neural networks. In: 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM), pp. 132–136. IEEE (2010)Google Scholar
- 7.Nair, B.B., Minuvarthini, M., Sujithra, B., Mohandas, V.: Stock market prediction using a hybrid neuro-fuzzy system. In: 2010 International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom), pp. 243–247. IEEE (2010)Google Scholar
- 8.Abraham, A., Grosan, C., Han, S.Y., Gelbukh, A.: Evolutionary multiobjective optimization approach for evolving ensemble of intelligent paradigms for stock market modeling. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 673–681. Springer, Heidelberg (2005)Google Scholar
- 10.Pathak, A.: Predictive time series analysis of stock prices using neural network classifier. International Journal of Computer Science and Engineering Technology, 2229–3345 (2014)Google Scholar
- 11.Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-Learn: Machine Learning in Python. JMLR Journal of Machine Learning Research, 2825–2830 (2011)Google Scholar