EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices
AbstractThis paper proposes a new computational intelligence model for predicting univariate time series, called EPAK, and a complex prediction model for stock market index synthesizing all the sector index predictions using EPAK as a kernel. The EPAK model uses a complex nonlinear feature extraction procedure integrating a forward rolling Empirical Mode Decomposition (EMD) for financial time series signal analysis and Principal Component Analysis (PCA) for dimension reduction to generate information-rich features as input to a new two-layer K-Nearest Neighbor (KNN) with Affinity Propagation (AP) clustering for prediction via regression. The EPAK model is then used as a kernel for predicting each of all the sector indices of the stock market. The sector indices predictions are then synthesized via weighted average to generate the prediction of the stock market index, yielding a complex prediction model for the stock market index. The EPAK model and the complex prediction model for stock index are tested on real historical financial time series in Chinese stock index including CSI 300 and ten sector indices, with results confirming the effectiveness of the proposed models.
 China Securities Index Co., LTD (China). CSI 300 index compilation scheme. Shanghai: China Securities Index Co., LTD (China); 2016.
 Davidson, J.; Li, X. Y. (2016); Strict stationarity, persistence and volatility forecasting in ARCH process, Journal of Empirical Finance, 38, 534-547, 2016.
 Dudoit, S.; Fridlyand, J. (2002); A prediction-based resampling method for estimating the number of clusters in a dataset, Genome Biology, 3(7),1-21, 2002.
 Frey, B. J.; Dueck, D. (2007); Clustering by passing messages between data points, Science, 315, 972-976, 2007.
 Huang, N. E.; Shen, Z.; Long, S. R. ; et al. (1998); The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society A:Mathematical, Physical and Engineering Sciences, 454, 903-995, 1998.
 Hu, C.; Liu, X.; Pan, B.; et al. (2017); Asymmetric Impact of Oil Price Shock on Stock Market in China: A Combination Analysis Based on SVAR Model and NARDL Model, Emerging Markets Finance and Trade, 2017 (just-accepted).
 Iabal, M.; Naveed, A. (2016); Forecasting inflation: Autoregressive integrated moving average model,European Scientific Journal, 12(1), 83-92, 2016.
 Jaramillo, J.; Velasquez, J. D.; Franco, C. J. (2017); Research in financial time series forecasting with SVM: Contributions from literature, IEEE Latin America Transactions, 15(1),145-153, 2017.
 Jena, P. R.; Majhi, R.; Majhi, B. (2015); Development and performance evaluation of a novel knowledge guided artificial neural network (KGANN) model for exchange rate prediction, Journal of King Saud University-Computer and Information Sciences, 27(4), 450-457, 2015.
 Karl Pearson, F. R. S. (1901); On lines and planes of closest fit to systems of points in space, Philosophical Magazine, 2, 559-572, 1901.
 Martinez, F.; Frias, M.; Perez, M. (2017); A methodology for applying k-nearest neighbor to time series forecasting, Artificial Intelligence Review, 1-19, 2017.
 Mokoma, T. J.; Moroke, N. D.(2015); Is the South African exchange rate volatile? Application of the ARCH framework, Risk Governance and Control: Financial Market & Institutions, 5(1), 110-122, 2015.
 Pan, H. P.; Haidar, I.; Kulkarni, S. (2009); Daily prediction of short-term trends of crude oil prices using neural networks exploiting multimarket dynamics, Frontiers of Computer Science in China, 3(2),177-191, 2009.
 Pan, H. P.; Ma, Y.; Zhang, C. Z. (2017); FEPA-An integrated computational intelligence model for predicting financial time series, 2017 IEEE/SICE International Symposium on System Integration (SII 2017), 2017 Dec 11-14, Taiwan, China (Accepted).
 Pedro, C. S.; Pedro, H. M. (2017); Volatility forecasting via SVR-GARCH with mixture of Gaussian kernels, Computational Management Science, 14(2), 179-196, 2017.
 Ravi, V. ; Pradeepkumar, D. Deb, ; K. (2017); Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms, Swarm and Evolutionary Computation, 36, 136-149, 2017.
 Rotshtein, A.; Pustylnik, L.; Giat, Y.(2016); Fuzzy logic and chaos theory in time series forecasting, International Journal of Intelligent Systems, 31(11), 1056-1071, 2016.
 Sermpinis, G.; Stasinakis, C.; Theofilatos, K.; Karathanasopoulos, A. (2015); Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms- Support vector regression forecast combinations, Harvard Business Review, 247(3), 831-846, 2015.
 Tealab, A.; Hefny, H.; Badr, A. (2017); Forecasting of nonlinear time series using ANN, Future Computing and Informatics Journal, 2(1), 39-457, 2017.
 Wang, J.; Wang, J. (2017); Forecasting stochastic neural network based on financial empirical mode decomposition, Neural Networks, 90, 8-20, 2017.
 Wang, K.; Zhang, J.; Li, A.; et al. (2007); Adaptive affinity propagation clustering, Acta Automatica Sinica, 33(12), 1242-1246, 2007.
 Wen, F.; Gong, X.; Cai, S. (2016); Forecasting the volatility of crude oil futures using HAR-type models with structural breaks,Energy Economics, 59, 400-413, 2016.
 Wen, F.; Xiao, J.; Huang, C.; et al. (2018); Interaction between oil and US dollar exchange rate: nonlinear causality, time-varying influence and structural breaks in volatility, Applied Economics, 50(3), 319-334, 2018.
 Yang, H. L.; Lin, H. C. (2017); Applying the hybrid model of EMD, PSR, and ELM to exchange rates forecasting, Computational Economics, 49(1), 99-116, 2017.
 Zhang, C. Z.; Pan, H. P. (2015); A forecasting model based on forward rolling EMD techniques, Technical Economics (a Chinese Journal), 34(5), 70-76, 2015.
 Zhang, G. S.; Zhang, X. D.; Feng, H. Y. (2016); Forecasting financial time series using a methodology based on autoregressive integrated moving average and Taylor expansion, Expert Systems, 33 (5), 501-516, 2016.
 Zhang, N. N.; Lin, A. J.; Shang, P. J. (2017); Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting, Physica A: Statistical Mechanics and Its Applications, 477, 161-173, 2017.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.