Shashaank D.S, Sruthi.V, Vijayalashimi M.L.S and Shomona Garcia Jacob, SSNCE, India

Predicting the Total turnover of a company in the ever fluctuating Stock market has always proved to be a precarious situation and most certainly a difficult task at hand. Data mining is a well-known sphere of Computer Science that aims at extracting meaningful information from large databases. However, despite the existence of many algorithms for the purpose of predicting future trends, their efficiency is questionable as their predictions suffer from a high error rate. The objective of this paper is to investigate various existing classification algorithms to predict the turnover of different companies based on the Stock price. The authorized dataset for predicting the turnover was taken from www.bsc.com and included the stock market values of various companies over the past 10 years. The algorithms were investigated using the ‘R’ tool. The feature selection algorithm, Boruta, was run on this dataset to extract the important and influential features for classification. With these extracted features, the Total Turnover of the company was predicted using various algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression. This prediction mechanism was implemented to predict the turnover of a company on an everyday basis and hence could help navigate through dubious stock markets trades. An accuracy rate of 95% was achieved by the above prediction process. Moreover, the importance of the stock market attributes was established as well.

Data mining, Feature selection, classification algorithms, Machine learning algorithms