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 |
|---|---|
| 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 |
|---|---|
| Country | India |
| City | Delhi |
| Period | 24-09-14 → 27-09-14 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Information Systems
Cite this
}
Towards a generic framework for short term firm-specific stock forecasting. / Ahmed, Mansoor; Sriram, Anirudh; Singh, Sanjay.
Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 2681-2688 6968411.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Towards a generic framework for short term firm-specific stock forecasting
AU - Ahmed, Mansoor
AU - Sriram, Anirudh
AU - Singh, Sanjay
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
UR - http://www.scopus.com/inward/citedby.url?scp=84927609749&partnerID=8YFLogxK
U2 - 10.1109/ICACCI.2014.6968411
DO - 10.1109/ICACCI.2014.6968411
M3 - Conference contribution
AN - SCOPUS:84927609749
SP - 2681
EP - 2688
BT - Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -