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.
Original language | English |
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Title of host publication | Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 2681-2688 |
Number of pages | 8 |
ISBN (Electronic) | 9781479930791 |
DOIs | |
Publication status | Published - 01-01-2014 |
Event | 3rd International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014 - Delhi, India Duration: 24-09-2014 → 27-09-2014 |
Conference
Conference | 3rd International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014 |
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Country/Territory | India |
City | Delhi |
Period | 24-09-14 → 27-09-14 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Information Systems