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Predicting warrant prices using an artificial neural network model: Experimental comparison with Black Scholes Metron model


 
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1. Title Title of document Predicting warrant prices using an artificial neural network model: Experimental comparison with Black Scholes Metron model
 
2. Creator Author's name, affiliation, country Tam Phan Huy; University of Economics and Law, Ho Chi Minh City, Vietnam and Vietnam National University, Ho Chi Minh City, Vietnam.
 
2. Creator Author's name, affiliation, country Hai Yen Thi Nguyen; University of Economics and Law, Ho Chi Minh City, Vietnam and Vietnam National University, Ho Chi Minh City, Vietnam.
 
2. Creator Author's name, affiliation, country Minh Tú Lê Nguyễn; University of Economics and Law, Ho Chi Minh City, Vietnam and Vietnam National University, Ho Chi Minh City, Vietnam.; Viet Nam
 
2. Creator Author's name, affiliation, country Phú Lộc Lê Nguyễn; University of Economics and Law, Ho Chi Minh City, Vietnam and Vietnam National University, Ho Chi Minh City, Vietnam.; Viet Nam
 
2. Creator Author's name, affiliation, country Lan Phương Thị Đỗ; University of Economics and Law, Ho Chi Minh City, Vietnam and Vietnam National University, Ho Chi Minh City, Vietnam.; Viet Nam
 
2. Creator Author's name, affiliation, country Hồng Thanh Thụy Nguyễn; University of Economics and Law, Ho Chi Minh City, Vietnam and Vietnam National University, Ho Chi Minh City, Vietnam.; Viet Nam
 
2. Creator Author's name, affiliation, country Kim Anh Thi Pham; Faculty of Finance and Accounting - University of Industry and Trade, Ho Chi Minh City, Vietam.; Viet Nam
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) ANN, Black Scholes, deep learning, machine learning, warrant prediction
 
4. Description Abstract The main objective of this study is to build an artificial neural network (ANN) model to predict warrant prices in Vietnam with data collected from 2019 to 2021 from nearly 300 different warrants. The ANN model is applied on a case-by-case basis depending on the status of the ITM or OTM warrants to examine further the model's pricing performance of the proposed model's price relative to the actual warrant's price. In addition, to compare with the ANN model, the Black Scholes Merton (BS) model is also used for warrant pricing. The ANN model is built with structure of 3 hidden layers using ReLU activation and 1 hidden layer using Softplus activation. The research results show that the ANN model has a more significant error performance in the case of more significant data than in the other two cases. BS model, there is no specific conclusion that applying the model, in any case, will be more effective. Regarding performance comparison between the two models, the ANN model outperforms both the BS model.
 
5. Publisher Organizing agency, location Coordinamento SIBA - Università del Salento
 
6. Contributor Sponsor(s) University of Economics and Law, Vietnam National University, Ho Chi Minh City, Vietnam
 
7. Date (YYYY-MM-DD) 2024-03-15
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format pdf
 
10. Identifier Uniform Resource Identifier http://siba-ese.unisalento.it/index.php/ejasa/article/view/27420
 
10. Identifier Digital Object Identifier 10.1285/i20705948v17n1p89
 
11. Source Publication/conference title; vol., no. (year) Electronic Journal of Applied Statistical Analysis; Vol 17, No 1 (2024): Electronic Journal of Applied Statistical Analysis (Special Issue: Big Data in Economics' Applications)
 
12. Language English=en en
 
13. Relation Supp. Files
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
15. Rights Copyright and permissions

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