Journal of Management Research

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Prediction of Best Index Suitable for Investor’s by Using DRSMLA Method


Affiliations
1 CMRCET, Hyderabad, India
2 School of Management Studies, Vignan University, Guntur, Andhra Pradesh, India
     

   Subscribe/Renew Journal


Every investor wants to gain high returns and dividends in stock market. This is possible only when there are methods to get knowledge as to invest in stocks that will be risk free. Towards this direction the methods existing in the literature are ID3 Decision trees, neural networks, statistics discriminant analysis and rough set theory. But there are some drawbacks in the above methods. To overcome them we propose a method "Dynamic Rough set using machine learning algorithms" (DRSMLA) to predict best indices for long term, medium term and short term investors. We use Dynamic Rough Set Theory (DRST) for preprocessing the data and divide the data into required time slots. Next the features selected by DRST will be sent to Support Vector Machine (SVM) model to learn and test respectively. The model is tested on Bombay Stock Exchange (BSE) and the results reveal accuracy is increased and time complexity is decreased over the existing methods.

Keywords

Bombay Stock Exchange, Dynamic Rough Set Theory, Investment, Prediction, Support Vector Machine.
User
Notifications

  • Burges, Chris J. C. (1998), A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, 2: 121-167.

  • Cao, L. and Tay, F. (2001), Financial Forecasting Using Support Vector Machines, Neural Computing and Applications, 10: 184-192.

  • Cao, L. and Tay, F. (2003), Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting, IEEE Trans. on Neural Networks, 14: 1506-1518.

  • Desai, V. S. and Bharati, R. (1998), A Comparison of Linear Regression and Neural Network Methods for Predicting Excess Returns on Large Stocks, Annals of Operations Research, 78: 127.

  • Dropsy (1996), Do Macroeconomic Factors Help in Predicting International Equity Risk Premia? Testing the Out-of-sample Accuracy of Linear and Nonlinear Forecasts, Journal of Applied Business Research, 12: 120–132.

  • Evgeniou, T., Pontil, M. and Poggio, T. (2000), Regularization Networks and Support Vector Machines, Advances in Computational Mathematics, 13: 1-50.

  • Golan, R. H. and Ziarko, W. (1995), A Methodology for Stock Market Analysis Utilizing Rough Set Theory, Proceedings of the IEEE/IAFE Computational Intelligence for Financial Engineering, 9-11 April, pp. 32-40.

  • Golan, R., (1995), Stock Market Analysis Utilizing Rough Set Theory, Ph. D. thesis, Department of Computer Science, University of Regina, Canada.

  • Huang, W., Nakamori, Y. and Wang, S. Y. ( 2005), Forecasting Stock Market Movement Direction with Support Vector Machine, Computers & Operations Research, 32: 2513-2522.

  • Kim, K. J. (2003), Financial Time Series Forecasting Using Support Vector Machines, Neurocomputing, 55: 307-319.

  • Kim, K. J. and Han, I. (2000), Genetic Algorithms Approach to Feature Discretization in Artificial Neural Networks for the Prediction of Stock Price Index, Expert Systems with Applications, 19(2): 125–132.

  • Kim, K. J. and Han, I. (2001), The Extraction of Trading Rules from Stock Market Data Using Rough Sets, Expert Systems, 18(4): 194–202.

  • Motiwalla, L. and Wahab, M. (2000), Predictable Variation and Profitable Trading of US Equities: A Trading Simulation Using Neural Networks, Computer & Operations Research, 27: 1111–1129.

  • Poddig, T. and Rehkugler, H. (1996), A World of Integrated Financial Markets Using Artificial Neural Networks, Neurocomputting, 10: 251–273.

  • Qi, M. (1999), Nonlinear Predictability of Stock Returns Using Financial and Economic Variables, Journal of Business & Economic Statistics, 17: 419–429.

  • Qi, M. and Maddala, G. S. (1999), Economic Factors and the Stock Market: A New Perspective, Journal of Forecasting, 18: 151–166.

  • Tay, Francis E. H. and Shen, Lixiang (2002), Economic and Financial Prediction Using Rough Sets Model, European Journal of Operational Research, 141(3): 641-659.

  • Thawornwong, S. and Enke, D. (2001), The Use of Data Mining, Neural Network Models, and Validation Techniques for Predicting Excess Stock Returns, Proceedings of the International ICSC Congress on Computational Intelligence: Methods and Applications, pp. 329-335, Bangor, Wales, UK.

  • Vapnik, V. N. (1995), The Nature of Statistical Learning Theory, Springer, New York.

  • Vapnik, V. N. (1998), Statistical Learning Theory, New York: Wiley.

  • Xu, X., Zhou, C. and Wang, Z. (2009), Credit Scoring Algorithm Based on Link-Analysis Ranking with Support Vector Machine, Expert Systems with Applications, 36: 2625–2632.

  • Ziarko, W., Golan, R. and Edwards, D. (1993), An Application of Datalogic/R Knowledge Discovery Tool to Identify Strong Predictive Rules in Stock Market Data, in Proceedings of AAAI Workshop on Knowledge Discovery in Databases, pp. 89101, Washington, DC.


Abstract Views: 535

PDF Views: 7




  • Prediction of Best Index Suitable for Investor’s by Using DRSMLA Method

Abstract Views: 535  |  PDF Views: 7

Authors

Madhavi Latha
CMRCET, Hyderabad, India
K. Siva Nageswara Rao
School of Management Studies, Vignan University, Guntur, Andhra Pradesh, India

Abstract


Every investor wants to gain high returns and dividends in stock market. This is possible only when there are methods to get knowledge as to invest in stocks that will be risk free. Towards this direction the methods existing in the literature are ID3 Decision trees, neural networks, statistics discriminant analysis and rough set theory. But there are some drawbacks in the above methods. To overcome them we propose a method "Dynamic Rough set using machine learning algorithms" (DRSMLA) to predict best indices for long term, medium term and short term investors. We use Dynamic Rough Set Theory (DRST) for preprocessing the data and divide the data into required time slots. Next the features selected by DRST will be sent to Support Vector Machine (SVM) model to learn and test respectively. The model is tested on Bombay Stock Exchange (BSE) and the results reveal accuracy is increased and time complexity is decreased over the existing methods.

Keywords


Bombay Stock Exchange, Dynamic Rough Set Theory, Investment, Prediction, Support Vector Machine.

References