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

• Main Authors: Manik Sharma, Samriti Sharma, Gurvinder Singh
• Format: Article
• Language: English
• Published: MDPI AG 2018-11-01
• Series: Data
• Subjects: stock forecasting; naïve Bayes; C4.5; random forest; logistic regression; support vector machine
• Online Access: https://www.mdpi.com/2306-5729/3/4/54

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&#8217;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, <i>p</i>-value, a-squared, and 95% confidence mean interval level of ICICI Bank&#8217;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 na&#239;ve 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).