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dc.contributor.advisorMajumdar, Mahbub Alam
dc.contributor.authorNasim, Ahmed Zohair
dc.contributor.authorSyed, Shehran
dc.date.accessioned2020-01-21T06:59:39Z
dc.date.available2020-01-21T06:59:39Z
dc.date.copyright2019
dc.date.issued2019
dc.identifier.otherID 18241011
dc.identifier.otherID 16101007
dc.identifier.urihttp://hdl.handle.net/10361/13652
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 39-40).
dc.description.abstractIn this research, modelling of the European option prices of S&P 500 index options was carried out using Multi-layer Perceptron Neural Networks. The goal was to train the neural networks using historical data to accurately determine option prices, given the index price, strike price and time to expiry as inputs. There is no hard and fast formula for pricing options, with the exception of the Black Scholes model, which is only a theoretical model and often under-performs in practical applications. Therefore, developing a model for pricing real options is of great importance, and Neural Networks have the potential to be vital vehicles to that end. That is what motivated this study. Di erent results with respect to accuracy are achieved by partitioning the data according to moneyness of options, with the Neural Network performing exceptionally for in-the-money options, but poorly for out-of-the-money options. This suggest that in a volatile market the neural network outperforms the Black Scholes model for in-the-money options, however the Black Scholes model is still better for at-the-money options.en_US
dc.description.statementofresponsibilityAhmed Zohair Nasim
dc.description.statementofresponsibilityShehran Syed
dc.format.extent40 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac 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.
dc.subjectNeural networken_US
dc.subjectBlack scholes modelen_US
dc.subject.lcshNeural networks (Computer science)
dc.titleModelling option prices using neural networksen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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