TY - GEN
T1 - Towards a generic framework for short term firm-specific stock forecasting
AU - Ahmed, Mansoor
AU - Sriram, Anirudh
AU - Singh, Sanjay
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84927609749&partnerID=8YFLogxK
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U2 - 10.1109/ICACCI.2014.6968411
DO - 10.1109/ICACCI.2014.6968411
M3 - Conference contribution
AN - SCOPUS:84927609749
T3 - Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014
SP - 2681
EP - 2688
BT - Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014
A2 - Comer, Douglas E.
A2 - Mueller, Peter
A2 - Mallick, Bhawna
A2 - Mukherjea, Sougata
A2 - Thampi, Sabu M.
A2 - Krishnaswamy, Dilip
A2 - Sikora, Axel
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014
Y2 - 24 September 2014 through 27 September 2014
ER -