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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, Q., Chan, M.F.: Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study. Ann. N. Y. Acad. Sci. 1387(1), 84–94 (2017)
Sen, J., Chaudhuri, T.D.: Decomposition of time series data of stock markets and its implications for prediction: an application for the indian auto sector (2016)
Ticknor, J.L.: A Bayesian regularized artificial neural network for stock market forecasting. Expert Syst. Appl. 40(14), 5501–5506 (2013)
Yan, D., Zhou, Q., Wang, J., Zhang, N.: Bayesian regularisation neural network based on artificial intelligence optimisation. Int. J. Prod. Res. 55(8), 2266–2287 (2017)
Wang, J.-Z., Wang, J.-J., Zhang, Z.-G., Guo, S.-P.: Forecasting stock indices with back propagation neural network. Expert Syst. Appl. (2011)
Guresen, E., Kayakutlu, G., Daim, T.U.: Using artificial neural network models in stock market index prediction. Expert Syst. Appl. 38(8), 10389–10397 (2011)
Chen, H., Xiao, K., Sun, J., Wu, S.: A double-layer neural network framework for high-frequency forecasting. ACM Trans. Manage. Inf. Syst. 7(4), 1–17 (2017)
Ng, A.Y.: Feature selection, L1 versus L2 regularization, and rotational invariance. In: Twenty-First International Conference on Machine learning—ICML’04, p. 78 (2004)
Yahoo Finance World Indices. https://in.finance.yahoo.com/world-indices/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-13-1592-3_59
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1591-6
Online ISBN: 978-981-13-1592-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)