Stock price forecasting using Bayesian network
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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BRAC University
2018
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| Länkar: | http://hdl.handle.net/10361/10958 |
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10361-109582022-01-26T10:15:56Z Stock price forecasting using Bayesian network Tabassum, Parisa Halder, Mita Majumdar, Mahbub Alam Department of Computer Science and Engineering, BRAC University Stock market Bayesian network Ward method K2 algorithm Bayesian statistical decision theory -- Data processing. Computers -- Enterprise applications -- Business intelligence tools. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 45-47). In a financially volatile market, as the stock market, it is important to have a very precise prediction of a future trend. Because of the financial crisis and scoring profits, it is mandatory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires advanced algorithms of machine learning. The literature contains the stock price prediction algorithm by using Bayesian network. The network is determined from the daily stock price. The prediction error is evaluated from the daily stock price and its prediction. The present algorithm is applied for predicting Google, Procter & Gamble and General Motors stock price. The results of this study show that the algorithm is capable of predicting future stock price more accurately than a lot of another machine learning algorithm available so far. Parisa Tabassum Mita Halder B. Computer Science and Engineering 2018-12-04T06:53:30Z 2018-12-04T06:53:30Z 2018 2018-08 Thesis ID 14301009 ID 14301012 http://hdl.handle.net/10361/10958 en 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 |
English |
| topic |
Stock market Bayesian network Ward method K2 algorithm Bayesian statistical decision theory -- Data processing. Computers -- Enterprise applications -- Business intelligence tools. |
| spellingShingle |
Stock market Bayesian network Ward method K2 algorithm Bayesian statistical decision theory -- Data processing. Computers -- Enterprise applications -- Business intelligence tools. Tabassum, Parisa Halder, Mita Stock price forecasting using Bayesian network |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Majumdar, Mahbub Alam |
| author_facet |
Majumdar, Mahbub Alam Tabassum, Parisa Halder, Mita |
| format |
Thesis |
| author |
Tabassum, Parisa Halder, Mita |
| author_sort |
Tabassum, Parisa |
| title |
Stock price forecasting using Bayesian network |
| title_short |
Stock price forecasting using Bayesian network |
| title_full |
Stock price forecasting using Bayesian network |
| title_fullStr |
Stock price forecasting using Bayesian network |
| title_full_unstemmed |
Stock price forecasting using Bayesian network |
| title_sort |
stock price forecasting using bayesian network |
| publisher |
BRAC University |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/10958 |
| work_keys_str_mv |
AT tabassumparisa stockpriceforecastingusingbayesiannetwork AT haldermita stockpriceforecastingusingbayesiannetwork |
| _version_ |
1814308537979895808 |