Abstract
As the stock market is highly volatile and chaotic in nature, prediction about this is a highly challenging task. To achieve better prediction accuracy, this article presents a model that uses artificial neural network (ANN) optimized by gray wolf optimization (GWO) technique. The model is applied on Bombay stock exchange (BSE) data. The range of data selection was from 25 August 2004 to 24 October 2018. To evaluate the performance of the model, many evaluation metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and median average error (MedAE) are used. The end result shows that the proposed model outperforms ANN model.
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Sahoo, S., Mohanty, M.N. (2020). Stock Market Price Prediction Employing Artificial Neural Network Optimized by Gray Wolf Optimization. In: Patnaik, S., Ip, A., Tavana, M., Jain, V. (eds) New Paradigm in Decision Science and Management. Advances in Intelligent Systems and Computing, vol 1005. Springer, Singapore. https://doi.org/10.1007/978-981-13-9330-3_8
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DOI: https://doi.org/10.1007/978-981-13-9330-3_8
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