Turnover Prediction of Shares Using Data Mining Techniques : A Case Study

摘要

Predicting the Total turnover of a company in the ever fluctuating Stock market has alwaysproved to be a precarious situation and most certainly a difficult task at hand. Data mining is awell-known sphere of Computer Science that aims at extracting meaningful information fromlarge databases. However, despite the existence of many algorithms for the purpose ofpredicting future trends, their efficiency is questionable as their predictions suffer from a higherror rate. The objective of this paper is to investigate various existing classification algorithmsto predict the turnover of different companies based on the Stock price. The authorized datasetfor predicting the turnover was taken from www.bsc.com and included the stock market valuesof 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 importantand influential features for classification. With these extracted features, the Total Turnover ofthe company was predicted using various algorithms like Random Forest, Decision Tree, SVMand Multinomial Regression. This prediction mechanism was implemented to predict theturnover of a company on an everyday basis and hence could help navigate through dubiousstock 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.