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Predictive mining for stock market based on live news TF-IDF features

Published Online:pp 341-365

The various machine learning algorithms are used for prediction of stock market movement. The data collected for stock market is in the form of breaking news from various finance websites. The TF-IDF features extracted from online news data are used for creation of HMM model along with log likelihood values. The next day's stock price is predicted as either higher or lower than current day's stock price. Results obtained from proposed model is compared with results from other machine learning predictive techniques such as random forest, KNN, multiple regression, bagging and boosting. The proposed model produces approximately 70% of accurate prediction. The captured features are graphically represented with word cloud. The results can be further improved with the use of deep learning ensemble methods.


text mining, stock market, HMM, bagging, boosting, multiple regression, random forest, finance news, TF-IDF, word cloud, autonomic computing