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Machine learning methods are being used by several researchers for successfully predicting prices of financial instruments from the financial time series data of different markets. As the nature of markets in different regions are different, in this paper two machine learning techniques: Back Propagation Technique (BP) and Support Vector Machine Technique (SVM) have been used to predict futures prices traded in Indian stock market. The performances of these techniques are compared and it is observed that SVM provides better performance results as compared to BP technique. The implementation is carried out using MATLAB and SVM Tools (LS-SVM Tool Box).

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Support vector regression (SVR) has been successfully applied in various domains, including predicting the prices of different financial instruments like stocks, futures, options, and indices. Because of the wide variation in financial time-series data, instead of using only a single standard prediction technique like SVR, we propose a hybrid model called USELM-SVR. It is a combination of unsupervised extreme learning machine (US-ELM)-based clustering and SVR forecasting. We assessed the feasibility and effectiveness of this hybrid model using a case study, predicting the one-, two-, and three-day ahead closing values of the energy commodity futures index traded on the Multi Commodity Exchange in India. Our experimental results show that the USELM-SVR is viable and effective, and produces better forecasts than our benchmark models (standard SVR, a hybrid of SVR with self-organizing map (SOM) clustering, and a hybrid of SVR with k-means clustering). Moreover, the proposed USELM-SVR architecture is useful as an alternative model for prediction tasks when we require more accurate predictions.