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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
     

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

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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