TY - GEN

T1 - A rough set theory based predictive model for stock prices

AU - Khoza, Msizi

AU - Marwala, Tshilidzi

PY - 2011

Y1 - 2011

N2 - Attempting to successfully and accurately predict the financial market has long attracted the interests and attention of economists, bankers, mathematicians and scientists alike. The financial markets form the bedrock of any economy. There are a large number of factors and parameters that influence the direction, volume, price and flow of traded stocks. This coupled with the markets' vulnerability to external and non-finance related factors and the resulting intrinsic volatility makes the development of a robust and accurate financial market prediction model an interesting research and engineering problem. In an attempt to solve this engineering problem, the authors of this paper present a rough set theory based predictive model for the financial markets. Rough set theory has, as its base, imperfect data analysis and approximation. The theory is used to extract a set of reducts and a set of trading rules based on trading data of the Johannesburg Stock Exchange (JSE) for the period 1 April 2006 to 1 April 2011. To increase the efficiency of the model four dicretization algorithms are used on the data set, namely, Equal Frequency Binning (EFB), Boolean Reasoning, Entropy and the Naïve Algorithm. The EFB algorithm gives the least number of rules and highest accuracy. Next, the reducts are extracted using the Genetic Algorithm and finally the set of dependency rules are generated from the set of reducts. A rough set confusion matrix is used to assess the accuracy of the model. The model gives a prediction accuracy of 80.4% using the Standard Voting classifier.

AB - Attempting to successfully and accurately predict the financial market has long attracted the interests and attention of economists, bankers, mathematicians and scientists alike. The financial markets form the bedrock of any economy. There are a large number of factors and parameters that influence the direction, volume, price and flow of traded stocks. This coupled with the markets' vulnerability to external and non-finance related factors and the resulting intrinsic volatility makes the development of a robust and accurate financial market prediction model an interesting research and engineering problem. In an attempt to solve this engineering problem, the authors of this paper present a rough set theory based predictive model for the financial markets. Rough set theory has, as its base, imperfect data analysis and approximation. The theory is used to extract a set of reducts and a set of trading rules based on trading data of the Johannesburg Stock Exchange (JSE) for the period 1 April 2006 to 1 April 2011. To increase the efficiency of the model four dicretization algorithms are used on the data set, namely, Equal Frequency Binning (EFB), Boolean Reasoning, Entropy and the Naïve Algorithm. The EFB algorithm gives the least number of rules and highest accuracy. Next, the reducts are extracted using the Genetic Algorithm and finally the set of dependency rules are generated from the set of reducts. A rough set confusion matrix is used to assess the accuracy of the model. The model gives a prediction accuracy of 80.4% using the Standard Voting classifier.

KW - classification

KW - discretization

KW - financial market modelling

KW - neural networks

KW - rough set theory

UR - http://www.scopus.com/inward/record.url?scp=84855969353&partnerID=8YFLogxK

U2 - 10.1109/CINTI.2011.6108571

DO - 10.1109/CINTI.2011.6108571

M3 - Conference contribution

AN - SCOPUS:84855969353

SN - 9781457700453

T3 - 12th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2011 - Proceedings

SP - 57

EP - 62

BT - 12th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2011 - Proceedings

T2 - 12th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2011

Y2 - 21 November 2011 through 22 November 2011

ER -