Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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Brac University
2020
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| Dostęp online: | http://hdl.handle.net/10361/14060 |
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10361-140602022-01-26T10:21:46Z Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network Rayhan, Mohammad Sultana, Samiha Majid, Annur Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Financial factors Deep learning XGBoost Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 41-43). This study shows importance hierarchy of financial factors of corporations’ Goodwill and tries to foresee with popular machine learning and deep learning models. Financial engineering is using mathematical model to study financial behavior. Financial engineers are hired by investment banks, commercial banks, hedge funds, insurance companies, corporate treasuries, and regulatory agencies. It is vital for each of them to asses a company’s sustainability before any sort of investment. However, predicting sustainability is not deterministic. Therefore, corporate sustainability has become a mainstream business goal for stakeholders. Whether Quantitative finance impacts goodwill or has implicit insight can be a machine learning problem. Deep learning and machine learning are rapidly changing the financial services industry. Business leaders can now transform vast amounts of financial data into insightful predictions with the help of data science, creating significant savings in the bottom line. This thesis is concerned with investigating financial factors of a company’s Goodwill and also fits popular machine learning and deep learning models and evaluate goodness of fit. To aid the research, a comparison between the proposed models-XGboost and Deep LSTM are conducted. Mohammad Rayhan Samiha Sultana Annur Majid B. Computer Science 2020-10-14T05:03:54Z 2020-10-14T05:03:54Z 2019 2019-12 Thesis ID: 16101117 ID: 16301076 ID: 16101038 http://hdl.handle.net/10361/14060 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 47 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
en_US |
| topic |
Financial factors Deep learning XGBoost Machine learning |
| spellingShingle |
Financial factors Deep learning XGBoost Machine learning Rayhan, Mohammad Sultana, Samiha Majid, Annur Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Rayhan, Mohammad Sultana, Samiha Majid, Annur |
| format |
Thesis |
| author |
Rayhan, Mohammad Sultana, Samiha Majid, Annur |
| author_sort |
Rayhan, Mohammad |
| title |
Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network |
| title_short |
Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network |
| title_full |
Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network |
| title_fullStr |
Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network |
| title_full_unstemmed |
Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network |
| title_sort |
financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network |
| publisher |
Brac University |
| publishDate |
2020 |
| url |
http://hdl.handle.net/10361/14060 |
| work_keys_str_mv |
AT rayhanmohammad financialfactorsanalysisforacquisitionpremiumandanticipationusingextremegradientboostinganddeeprecurrentneuralnetwork AT sultanasamiha financialfactorsanalysisforacquisitionpremiumandanticipationusingextremegradientboostinganddeeprecurrentneuralnetwork AT majidannur financialfactorsanalysisforacquisitionpremiumandanticipationusingextremegradientboostinganddeeprecurrentneuralnetwork |
| _version_ |
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