NDLI: Towards a generic framework for short term firm-specific stock forecasting

Content Provider	IEEE Xplore Digital Library
Author	Ahmed, M. Sriram, A. Singh, S.
Copyright Year	2014
Description	Author affiliation: Dept. of Inf. & Commun. Technol., Manipal Inst. of Technol., Manipal, India (Ahmed, M.; Sriram, A.; Singh, S.)
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 non-shallow learning architectures is illustrated via a comparison of results obtained through a myriad of optimization and clustering algorithms.
Starting Page	2681
Ending Page	2688
File Size	212952
Page Count	8
File Format	PDF
ISBN	9781479930784
e-ISBN	9781479930807
DOI	10.1109/ICACCI.2014.6968411
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-24
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sentiment analysis Neural networks Companies Technical Analysis Market research Stock Forecasting Sentiment Analysis Stock markets Forecasting Machine Learning
Content Type	Text
Resource Type	Article

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