COMBINATION OF HYBRID FEATURE SELECTION METHOD AND NEAREST NEIGHBOR ALGORITHM FOR PREDICTION OF DAILY DIRECTION OF THE MOST 50 ACTIVE COMPANIES TEHRAN STOCK EXCHANGE MARKET INDEX

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POUYANFAR AHMAD; FALLAHPOUR SAEID; NOROUZIAN LAKVAN EISA; FARHADI AMIR HOSSEIN

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Journal: FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT) Year:2016 | Volume:6 | Issue:25 Page(s): 1-20

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Information Journal Paper

Title

COMBINATION OF HYBRID FEATURE SELECTION METHOD AND NEAREST NEIGHBOR ALGORITHM FOR PREDICTION OF DAILY DIRECTION OF THE MOST 50 ACTIVE COMPANIES TEHRAN STOCK EXCHANGE MARKET INDEX

Author(s)

POUYANFAR AHMAD | FALLAHPOUR SAEID | NOROUZIAN LAKVAN EISA | FARHADI AMIR HOSSEIN | Issue Writer Certificate

Keywords

PRINCIPLE COMPONENT ANALYSISQ2

GENETIC ALGORITHMQ2

FEATURE SELECTIONQ2

DIRECTION PREDICTIONQ2

FILTERINGQ2

NEAREST NEIGHBORQ2

Abstract

Prediction of stock market is always considered by traders and investors due to being profitable. A successful transaction to buy or sell happens close to points that trend of price is changing. Therefore, stock market index prediction and it’s analysis to determine that the closing price of stock will increase or decrease in the next day, are very important. In this research, NEAREST NEIGHBOR classification method based on combined FEATURE SELECTION method to predict the direction of the most 50 active companies Tehran stock exchange market index is used. This hybrid FEATURE SELECTION method that is combination of PRINCIPLE COMPONENT ANALYSIS and GENETIC ALGORITHM has advantages of both types of wrapper and FILTERING FEATURE SELECTION methods, for selection of optimum subset of features from entire space of feature. Performance of this proposed hybrid method is compared with conventional FEATURE SELECTION methods including: information gain, Relif and PRINCIPLE COMPONENT ANALYSIS method that are regarded as FILTERING method and GENETIC ALGORITHM that is one of the wrapper methods, by using the paired comparison test and the results show that, the proposed hybrid method has better performance than other methods in prediction of daily direction of the most 50 active companies Tehran stock exchange market index.

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APA: Copy

POUYANFAR, AHMAD, FALLAHPOUR, SAEID, NOROUZIAN LAKVAN, EISA, & FARHADI, AMIR HOSSEIN. (2016). COMBINATION OF HYBRID FEATURE SELECTION METHOD AND NEAREST NEIGHBOR ALGORITHM FOR PREDICTION OF DAILY DIRECTION OF THE MOST 50 ACTIVE COMPANIES TEHRAN STOCK EXCHANGE MARKET INDEX. FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT), 6(25), 1-20. SID. https://sid.ir/paper/197671/en

Vancouver: Copy

POUYANFAR AHMAD, FALLAHPOUR SAEID, NOROUZIAN LAKVAN EISA, FARHADI AMIR HOSSEIN. COMBINATION OF HYBRID FEATURE SELECTION METHOD AND NEAREST NEIGHBOR ALGORITHM FOR PREDICTION OF DAILY DIRECTION OF THE MOST 50 ACTIVE COMPANIES TEHRAN STOCK EXCHANGE MARKET INDEX. FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT)[Internet]. 2016;6(25):1-20. Available from: https://sid.ir/paper/197671/en

IEEE: Copy

AHMAD POUYANFAR, SAEID FALLAHPOUR, EISA NOROUZIAN LAKVAN, and AMIR HOSSEIN FARHADI, “COMBINATION OF HYBRID FEATURE SELECTION METHOD AND NEAREST NEIGHBOR ALGORITHM FOR PREDICTION OF DAILY DIRECTION OF THE MOST 50 ACTIVE COMPANIES TEHRAN STOCK EXCHANGE MARKET INDEX,” FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT), vol. 6, no. 25, pp. 1–20, 2016, [Online]. Available: https://sid.ir/paper/197671/en

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