Autoregressive Neural Network EURO STOXX 50 Forecasting Model Based on Principal Component Stock Selection
Keywords:ARIMA, Neural Networks, Time series
Purpose: The given study looks into forecast accuracy of a traditional ARIMA model while comparing it to Autoregressive Neural Network (AR-NN) model for 984 trading days on EURO STOXX 50 Index.
Methodology: A hybrid model is constructed by combining ARIMA model and feed-forward neural network model aiming to attain linear and non-linear price fluctuations. The study also incorporates the investigation of component stock prices of the index, that can be selected to improve the predictability of the hybrid model.
Findings:The reached ARIMA (1,1,3) model showed higher scores than AR-NN model however integrating selected exogenous stock prices from the index components gave much notable accuracy results. The selected exogenous stocks were extracted after conducting PCA and model scores were compared via MAPE and RMSE.
Unique contribution to theory, practice and policy: The major contribution of this work is to provide the researcher and fnancial analyst a systematic approach for development of intelligent methodology to forecast stock market. This paper also presents the outlines of proposed work with the aim to enhance the performance of existing techniques. Therefore, Empirical analysis is employed along with a hybrid model based on a feed-forward Neural Network. Lesser error is attained on the test set of Index stock price by comparing the performance of ARIMA and AR-NN while forecasting. Hence, The components of extracted Index stock price like exogenous features are added to make an influence from the AR-NN model.
Adebiyi, A. A., Ayo, C. K., Adebiyi, M., & Otokiti, S. O. (2012). Stock price prediction using neural network with hybridized market indicators. Journal of Emerging Trends in Computing and Information Sciences, 3(1)
Areekul, P., Senjyu, T., Toyama, H., & Yona, A. (2009). Notice of violation of ieee publication principles: A hybrid arima and neural network model for short-term price forecasting in deregulated market. IEEE Transactions on Power Systems, 25(1), 524-530
Bisoi, R., & Dash, P. K. (2015). Prediction of financial time series and its volatility using a hybrid dynamic neural network trained by sliding mode algorithm and differential evolution. International Journal of Information and Decision Sciences, 7(2), 166-191.
Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. Journal of Financial economics, 122(2), 221-247.
Faruk, D. Ö. (2010). A hybrid neural network and ARIMA model for water quality time series prediction. Engineering applications of artificial intelligence, 23(4), 586-594.
Haleh, H., Moghaddam, B. A., & Ebrahimijam, S. (2011). A new approach to forecasting stock price with EKF data fusion. International Journal of Trade, Economics and Finance, 2(2), 109.
Kumar, D. A., & Murugan, S. (2013, February). Performance analysis of Indian stock market index using neural network time series model. In 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (pp. 72-78). IEEE.
Lam, M. (2004). Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decision support systems, 37(4), 567-581.
Lin, C. S., Chiu, S. H., & Lin, T. Y. (2012). Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting. Economic Modelling, 29(6), 2583-2590.
Mehtab, S., & Sen, J. (2020). A time series analysis-based stock price prediction using machine learning and deep learning models. International Journal of Business Forecasting and Marketing Intelligence, 6(4), 272-335.
Musa, Y., & Joshua, S. (2020). Analysis of ARIMA-artificial neural network hybrid model in forecasting of stock market returns. Asian Journal of Probability and Statistics, 42-53.
Si, Y. W., & Yin, J. (2013). OBST-based segmentation approach to financial time series. Engineering Applications of Artificial Intelligence, 26(10), 2581-2596.
Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018, December). A comparison of ARIMA and LSTM in forecasting time series. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1394-1401). IEEE.
Tay, F. E., & Cao, L. (2001). Application of support vector machines in financial time series forecasting. omega, 29(4), 309-317.
Valentini, V. (2020). Efficient market hypothesis: diving into the cryptocurrency world.
Wang, J. Z., Wang, J. J., Zhang, Z. G., & Guo, S. P. (2011). Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38(11), 14346-14355.
Wang, X., Dai, W., & Yan, Y. (2021). Numerical analysis of a new conservative scheme for the 2D generalized Rosenau-RLW equation. Applicable Analysis, 100(12), 2564-2580.
Wen, Y., Lin, P., & Nie, X. (2020, March). Research of stock price prediction based on PCA-LSTM model. In IOP Conference Series: Materials Science and Engineering (Vol. 790, No. 1, p. 012109). IOP Publishing.
White, H. (1988, July). Economic prediction using neural networks: The case of IBM daily stock returns. In ICNN (Vol. 2, pp. 451-458).
Wood, D., & Dasgupta, B. (1996). Classifying trend movements in the MSCI USA capital market index—A comparison of regression, arima and neural network methods. Computers & Operations Research, 23(6), 611-622.
Zhong, X., & Enke, D. (2017). A comprehensive cluster and classification mining procedure for daily stock market return forecasting. Neurocomputing, 267, 152-168.