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DC Field | Value | Language |
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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 | |
Appears in Collections: | M Tech Dissertations |
Files in This Item:
File | Description | Size | Format | |
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201811046.pdf Restricted Access | 615.27 kB | Adobe PDF | View/Open Request a copy |
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