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Towards a Generic Framework for Short Term Firm-Specific Stock Forecasting

Ahmed, Mansoor and Sriram, Anirudh and Singh, Sanjay (2014) Towards a Generic Framework for Short Term Firm-Specific Stock Forecasting. In: International Conference on Advances in Computing,Communications and Informatics, 2014.

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Abstract

This paper investigates the predictive power of technical analysis, sentiment analysis and stock market analysis coupled with a robust learning engine in predicting stock trends in the short term for specific companies. Using large and varied datasets stretching over a duration of ten years, we set out to train, test and validate our system in order to either contradict or confirm efficient market hypothesis. Our results reveal a significant improvement over the efficient market hypothesis for majority companies and thus strongly challenge it. Technical parameters and algorithms operating upon them are shown to have a significant impact upon the end-predictive power of the system, thus bolstering claims of their efficacy. Moreover, sentiment analysis results also show a strong correlation with future market trends. Lastly, the superiority of supervised nonshallow learning architectures is illustrated via a comparison of results obtained through a myriad of optimization and clustering algorithms.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Stock Forecasting, Sentiment Analysis, Technical Analysis, Machine Learning
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 05 Dec 2014 05:53
Last Modified: 05 Dec 2014 05:53
URI: http://eprints.manipal.edu/id/eprint/141205

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