Part of the book series:Advances in Intelligent Systems and Computing ((AISC,volume 816))
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Abstract
The paper deals with the application of artificial neural network on financial data. We applied different activation functions in hidden layer with regularization to overcome the problem of overfitting. We present a comparative analysis of all combinations of activation functions and regularizations applied on BSE Sensex and Nifty 50 dataset containing the stock indices of last 7 years.
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Authors and Affiliations
International Institute of Information Technology, Naya Raipur, Naya Raipur, India
Rajat Gupta, Shrikant Gupta & Muneendra Ojha
Indian Institute of Information Technology, Allahabad, Allahabad, India
Krishna Pratap Singh
- Rajat Gupta
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- Shrikant Gupta
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- Muneendra Ojha
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- Krishna Pratap Singh
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Correspondence toKrishna Pratap Singh.
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Editors and Affiliations
Department of Mathematics, South Asian University New Delhi , New Delhi, India
Jagdish Chand Bansal
Department of Mathematics, National Institute Of Technology Silchar Department of Mathematics, Silchar, Assam, India
Kedar Nath Das
Department of Mathematics and Computer Science, Faculty of Science, , Liverpool Hope University, Liverpool, UK
Atulya Nagar
Department of Mathematics, Indian Institute of Technology Roor Department of Mathematics, Roorkee, Uttarakhand, India
Kusum Deep
School of Basic Sciences, Indian Institute of Technology Bhubanesw School of Basic Sciences, Bhubaneswar, Odisha, India
Akshay Kumar Ojha
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Gupta, R., Gupta, S., Ojha, M., Singh, K.P. (2019). Regularized Artificial Neural Network for Financial Data. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_59
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Online ISBN:978-981-13-1592-3
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