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Open AccessArticle

Performance Analysis of Statistical and Supervised Learning Techniques in Stock Data Mining

1
Department of Computer Science and Applications, DAV University, Jalandhar 144401, India
2
Department of Computer Science, Guru Nanak Dev University, Amritar 143001, India
*
Author to whom correspondence should be addressed.
Received: 30 September 2018 / Revised: 11 November 2018 / Accepted: 19 November 2018 / Published: 24 November 2018
(This article belongs to the Special Issue Data Analysis for Financial Markets)
Nowadays, overwhelming stock data is available, which areonly of use if it is properly examined and mined. In this paper, the last twelve years of ICICI Bank’s stock data have been extensively examined using statistical and supervised learning techniques. This study may be of great interest for those who wish to mine or study the stock data of banks or any financial organization. Different statistical measures have been computed to explore the nature, range, distribution, and deviation of data. The different descriptive statistical measures assist in finding different valuable metrics such as mean, variance, skewness, kurtosis, p-value, a-squared, and 95% confidence mean interval level of ICICI Bank’s stock data. Moreover, daily percentage changes occurring over the last 12 years have also been recorded and examined. Additionally, the intraday stock status has been mined using ten different classifiers. The performance of different classifiers has been evaluated on the basis of various parameters such as accuracy, misclassification rate, precision, recall, specificity, and sensitivity. Based upon different parameters, the predictive results obtained using logistic regression are more acceptable than the outcomes of other classifiers, whereas naïve Bayes, C4.5, random forest, linear discriminant, and cubic support vector machine (SVM) merely act as a random guessing machine. The outstanding performance of logistic regression has been validated using TOPSIS (technique for order preference by similarity to ideal solution) and WSA (weighted sum approach). View Full-Text
Keywords: stock forecasting; naïve Bayes; C4.5; random forest; logistic regression; support vector machine stock forecasting; naïve Bayes; C4.5; random forest; logistic regression; support vector machine
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Sharma, M.; Sharma, S.; Singh, G. Performance Analysis of Statistical and Supervised Learning Techniques in Stock Data Mining. Data 2018, 3, 54.

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