Abstract
Prediction of future trends in financial time-series data are very important for decision making in the share market. Usually financial time-series data are non-linear, volatile and subject to many other factors like local or global issues, causes a difficult task to predict them consistently and efficiently. This paper present an improved Dynamic Recurrent FLANN (DRFLANN) based adaptive model for forecasting the stock Indices of Indian stock market. The proposed DRFLANN based model employs the least mean square (LMS) algorithm to train the weights of the networks. The Mean Absolute Percentage Error (MAPE), the Average Mean Absolute Percentage Error (AMAPE), the variance of forecast errors (VFE) is used for determining the accuracy of the model. To improve further the forecasting results, we have introduced three technical indicators named Relative Strength Indicator (RSI), Price Volume Change Indicator (PVCI), and Moving Average Volume Indicator (MAVI). The reason of choosing these three indicators is that they are focused on important attributes of price, volume, and combination of both price and volume of stock data. The results show the potential of the model as a tool for making stock price prediction.
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References
Brownstone, D.: Using the percentage accuracy to measure neural network predictions in stock market movements. Neurocomputing 10, 237–250 (1996)
Chen, A.S., Leung, M.T., Daouk, H.: Application of Neural Networks to an emerging financial market: Forecasting and trading the Taiwan Stock Index. Comput. Operations Res. 30, 901–923 (2003)
Chang, P.-C., Liu, C.-H., Lin, J.-L., Fan, C.-Y., Ng, C.S.P.: A neural network with a case based dynamic window for stock trading prediction. Expert Systems with Applications 36(3, Pt 2), 6889–6898 (2009)
Zhang, Y., Wu, L.: Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Systems with Applications 36(5), 8849–8854 (2009)
Gorriz, J.M., Puntonet, C.G., Salmeron, M., de la Rosa, J.J.G.: A new model for time-series forecasting using radial basis functions and exogenous data. Neural Comput. & Applic., pp. 101–111 (2004), doi:10.1007/s00521-004-0412-5
Feng, H.-M., Chou, H.-C.: Evolutional RBFANs prediction systems generation in the applications of financial time series data. Expert Systems with Applications 38, 8285–8292 (2011)
Giles, C.L., Lawrence, S., Tsoi, A.C.: Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference. Machine Learning 44, 161–183 (2001)
Chen, Y., Dong, X., Zhao, Y.: Stock Index Modeling using EDA based Local Linear Wavelet Neural Network. In: International Conference on Neural Networks and Brain, pp. 1646–1650 (2005), doi:10.1109/ICNNB.2005.1614946
Hao, H.-N.: Short-term Forecasting of Stock Price Based on Genetic-Neural Network. In: Sixth International Conference on Natural Computation (ICNC 2010). IEEE Conference Publications (2010)
Wu, L.: A Hybrid Model for Day-Ahead Price forecasting. IEEE Transactions on Power Systems 25(3), 1519–1530 (2010)
Pao, Y.H., Takefji, Y.: Functional-Link Net Computing. IEEE Computer Journal, 76–79 (1992)
Majhi, R., Panda, G., Sahoo, G.: Development and performance evaluation of FLANN based model for forecasting of stock markets. Experts Systems with Applications An International Journal 36(3, Pt 2), 6800–6808 (2008)
Bebarta, D.K., Biswal, B., Dash, P.K.: Comparative study of stock market forecasting using different functional link artificial neural networks. Int. J. Data Analysis Techniques and Strategies 4(4), 398–427 (2012)
Bebarta, D.K., Biswal, B., Rout, A.K., Dash, P.K.: Forecasting and classification of Indian stocks using different polynomial functional link artificial neural networks. In: IEEE Conference, INDCON, pp. 178–182 (2012), doi:10.1109/INDCON.2012.6420611
Patra, J.C.: Chebysheb Neural Network-Based Model for Dual-Junction Sollar Cells. IEEE Transactions on Energy Conversion 26, 132–139 (2011)
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Bebarta, D.K., Rout, A.K., Biswal, B., Dash, P.K. (2015). Dynamic Recurrent FLANN Based Adaptive Model for Forecasting of Stock Indices. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2. Advances in Intelligent Systems and Computing, vol 338. Springer, Cham. https://doi.org/10.1007/978-3-319-13731-5_55
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DOI: https://doi.org/10.1007/978-3-319-13731-5_55
Publisher Name: Springer, Cham
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