Stock market forecasting using LASSO linear regression model

Advances in Intelligent Systems and Computing

Volumn 334, Issue , 2015, Pages 371-381

  Roy, Sanjiban Sekhar ^a   Mittal, Dishant ^a   Basu, Avik ^a   Abraham, Ajith ^b,c  

^a VIT UNIVERSITY   (India)

^b VSB TECHNICAL UNIVERSITY OF OSTRAVA   (Czech Republic)

^c SCIENTIFIC NETWORK FOR INNOVATION AND RESEARCH EXCELLENCE   (United States)

Author keywords

LASSO regression; Stock price prediction

Indexed keywords

COMMERCE; FINANCE; FINANCIAL DATA PROCESSING; FORECASTING; INFORMATION DISSEMINATION; MATHEMATICAL MODELS;

FINANCIAL PROBLEMS; FINANCIAL TIME SERIES; LASSO REGRESSIONS; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATORS; LINEAR REGRESSION MODELS; STOCK MARKET FORECASTING; STOCK PRICE MOVEMENTS; STOCK PRICE PREDICTION;

LINEAR REGRESSION;

EID: 84912051370     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-13572-4_31     Document Type: Article

References (11)

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)
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)
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)
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)
5. Aseervatham, S., Antoniadis, A., Gaussier, E., Burlet, M., Denneulin, Y.: A sparse version of the ridge logistic regression for large-scale text categorization. Pattern Recognition Letters 32(2), 101–106 (2011)
6. Ticknor, J.L.: A Bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications 40(14), 5501–5506 (2013)
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)
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)
9. Chen, Y., Yang, B., Abraham, A.: Flexible neural trees ensemble for stock index modeling. Neurocomputing 70(4), 697–703 (2007)
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)
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)