Rough Set Generating Prediction Rules for Stock Price Movement

Al-Qaheri, Hameed; Zamoon, Shariffah; Hassanien, Aboul Ella; Abraham, Ajith

doi:10.1109/ems.2008.89

Cited by 4 publications

(6 citation statements)

References 26 publications

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“…To quantitatively map this variance and thus distinguish those attributes with higher and lower importance, weights are accorded to the attributes. This of course requires some auxiliary knowledge of the problem domain [2,3,12,14,15]. Since rough set theory makes no such assumption and only does computations on data included in the information table itself, it is therefore prudent to discreetize continuous value attributes.…”

Section: B Data Discreetritizationmentioning

confidence: 99%

“…Any set of indiscernible data elements forms a granule or atom of knowledge about the entire "universe of discourse" (information system framework) [10]. A union of these elementary sets (granules) is said to be a precise or crisp set, other-wise the set is said to be rough [1,2,7,10]. Every rough set will have boundary cases i.e data objects which cannot certainly be classified as belonging to the set or its complement when using the available information [10].…”

Section: Theoretical Foundations Of Rough Setsmentioning

confidence: 99%

“…The decision attribute indicates the future direction of the index. The attribute was calculated using the following [2]:…”

Section: Decision (D) Decision Attributementioning

confidence: 99%

“…However the emergence of computational intelligence techniques as a viable alternative to the "traditional" statistical models that have dominated this area since the 1930's [2,3] has given impetus to the increasing usage of these techniques in fields such as economics and finance [3]. Apart from these, there have been many other successful applications of intelligent systems to decision support and complex automation tasks [4 -6].…”

Section: Introductionmentioning

confidence: 99%

“…For a long time statistical techniques such as Bayesian models, regression and some econometric techniques have dominated research activities in prediction [2]. The primary approach to financial forecasting has been the identification of a stock price trend and continuation of the investment strategy until evidence suggests that the trend has reversed [3].…”

Section: Introductionmentioning

confidence: 99%

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A rough set theory based predictive model for stock prices

Khoza

Marwala

2011

2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI)

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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.

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Section: B Data Discreetritizationmentioning

confidence: 99%

Section: Theoretical Foundations Of Rough Setsmentioning

confidence: 99%

“…The decision attribute indicates the future direction of the index. The attribute was calculated using the following [2]:…”

Section: Decision (D) Decision Attributementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A rough set theory based predictive model for stock prices

Khoza

Marwala

2011

2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI)

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Rough Set Generating Prediction Rules for Stock Price Movement

Al-Qaheri

Zamoon

Hassanien

et al. 2008

2008 Second UKSIM European Symposium on Computer Modeling and Simulation

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Study of Weight Determination Based on Rough Set Theory in Desulfuration Projects Decision

Shang

2008

2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop

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Rough Set Theory could availably analysis and the processing the incomplete information problems. Cited the Rough Set Theory into desulfuration projects decision, through the analysis and extraction of each desulfuration method evaluation index, and according to the importance index of each desulfuration method evaluation index to the order of alternatives is sorted to get thus consistent and completed decision rule. This method based on the actual application background, combined the objective weight and the subjective weight which is decided by the experts' experience knowledge to assured the final weight value. This method makes up for the shortness of the subjective weight decided by the experts' experience knowledge. Through the study of actual application based on rough set theory, confirmed the rationality and feasibility of the rough set theory that used in the decision making of desulfuration projects.

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Rough Set Generating Prediction Rules for Stock Price Movement

Abstract: Abstract

Cited by 4 publications

References 26 publications

A rough set theory based predictive model for stock prices

A rough set theory based predictive model for stock prices

Rough Set Generating Prediction Rules for Stock Price Movement

Study of Weight Determination Based on Rough Set Theory in Desulfuration Projects Decision

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