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Performance Analysis of Stock Market Prediction Techniques


Roshani Gandhe , Priyadarshini Bhagwati College of Engg., Nagpur; Prof. Manoj S. Chaudhari, Priyadarshini Bhagwati College of Engg., Nagpur,


Stock Price Prediction, Listed Companies, Data Mining, K-Nearest Neighbor, Non-Linear Regression


Stock market values keeps on changing day by day, so it is very difficult to predict the future value of the market. Although there are various techniques implemented for the prediction of stock market values, but the predicted values are not very accurate and error rate is more. Hence an efficient technique is implemented for the prediction of the stock market values using hybrid combinatorial method of clustering and classification. The dataset is taken from shanghai stock exchange market and is first clustered using K-means clustering algorithm and these clustered values are classified using horizontal partition decision tree. Stock prices prediction is interesting and challenging research topic. Developed countries' economies are measured according to their power economy. Currently, stock markets are considered to be an illustrious trading field because in many cases it gives easy profits with low risk rate of return. Stock market with its huge and dynamic information sources is considered as a suitable environment for data mining and business researchers. In this paper, we applied k-nearest neighbor algorithm and non-linear regression approach in order to predict stock prices for a sample of six major companies listed on the Jordanian stock exchange to assist investors, management, decision makers, and users in making correct and informed investments decisions. According to the results, the kNN algorithm is robust with small error ratio; consequently the results were rational and also reasonable. In addition, depending on the actual stock prices data; the prediction results were close and almost parallel to actual stock prices.

Other Details

Paper ID: IJSRDV4I50646
Published in: Volume : 4, Issue : 5
Publication Date: 01/08/2016
Page(s): 1316-1321

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