| dc.contributor.advisor | Joshi, M.V. | |
| dc.contributor.author | Sharma, Rohan | |
| dc.date.accessioned | 2020-09-22T20:12:33Z | |
| dc.date.available | 2023-02-16T20:12:33Z | |
| dc.date.issued | 2020 | |
| dc.identifier.citation | Sharma, Rohan (2020). Machine learning in financial data EPS estimates. Dhirubhai Ambani Institute of Information and Communication Technology. iv, 13 p. (Acc.No: T00874) | |
| dc.identifier.uri | http://drsr.daiict.ac.in//handle/123456789/952 | |
| dc.description.abstract | The project “EPS Estimates” is as the name suggests a work on Earnings Per Share figures released by companies annually and quarterly. The whole project is intended to come up with a better consensus methodology for EPS Estimates given by different brokers and give the clients a better idea of what the EPS figures will be like. There are various statistical methods and machine learning models used for the purpose and a comparison is done between them in this report. The details about the intuition behind the models, their shortcomings and some insights behind them are included in this report. | |
| dc.subject | Machine Learning | |
| dc.subject | Earning per Share | |
| dc.classification.ddc | 006.31 SHA | |
| dc.title | Machine learning in financial data EPS estimates | |
| dc.type | Dissertation | |
| dc.degree | M. Tech | |
| dc.student.id | 201811046 | |
| dc.accession.number | T00874 | |
