Intelligent ensemble systems for modeling NASDAQ microstructure: A comparative study

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8774, Issue , 2014, Pages 240-251

  Lahmiri, Salim ^a  

^a ESCA School of Management   (Morocco)

Author keywords

Ensemble; Forecasting; Neural networks; Stock market microstructure

Indexed keywords

BACKPROPAGATION; ELECTRONIC TRADING; FORECASTING; MICROSTRUCTURE; NEURAL NETWORKS; PATTERN RECOGNITION; TIME SERIES ANALYSIS;

COMPARATIVE STUDIES; ENSEMBLE; FEED-FORWARD BACK-PROPAGATION NEURAL NETWORKS; FORECASTING ERROR; MARKET MICROSTRUCTURES; NEURAL NETWORK (NN); RADIAL BASIS NEURAL NETWORKS; TIME DELAY NEURAL NETWORKS;

FEEDFORWARD NEURAL NETWORKS;

EID: 84910069898     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-11656-3_22     Document Type: Conference Paper

References (24)

1. Francis, E.H., Chao, L.J.: Modified Support Vector Machine in Financial Time Series Forecasting. Neurocomputing 48, 847-861 (2002)
2. Zhang, G.P., Berardi, V.L.: Time Series Forecasting with Neural Network Ensembles: An Application for Exchange Rate Prediction. Journal of the Operational Research Society 52, 652-664 (2001)
3. Atsalakis, G.S., Valavanis, K.P.: Surveying Stock Market Forecasting Techniques Part II: Soft Computing Methods. Expert Systems with Applications 36, 5932-5941 (2009)
4. Bahrammirzaee, A.: A Comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems. Neural Computing & Applications 19, 1165-1195 (2010)
5. Xiao, Y., Xiao, J., Lu, F., Wang, S.: Ensemble ANNs-PSO-GA Approach for Day-Ahead Stock Exchange Prices Forecasting. International Journal of Computational Intelligence Systems 6, 96-114 (2013)
6. Kim, M.J., Min, S.H., Han, I.: An Evolutionary Approach to The Combination of Multiple Classifiers to Predict a Stock Price Index. Expert Systems with Applications 31, 241-247 (2006)
7. Tsai, C.-F., Lin, Y.-C., Yen, D.C., Chen, Y.-M.: Predicting Stock Returns by Classifier Ensembles. Applied Soft Computing 11, 2452-2459 (2011)
8. Wang, L., Wu, J.: Neural Network Ensemble Model using PPR and LS-SVR for Stock Market Forecasting. In: Huang, D.-S., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2011. LNCS, vol. 6838, pp. 1-8. Springer, Heidelberg (2011)
9. Kim, E., Kim, W., Lee, Y.: Combination of Multiple Classifiers for The Customer’s Purchase Behavior Prediction. Decision Support Systems 34, 167-175 (2003)
10. Goh, W.Y., Lim, C.P., Peh, K.K.: Predicting Drug Dissolution Profiles with An Ensemble of Boosted Neural Networks: A Time Series Approach. IEEE Transactions on Neural Networks 14, 459-463 (2003)
11. Langella, G., Basile, A., Bonfante, A., Terribile, F.: High-Resolution Space-Time Rainfall Analysis using Integrated ANN Inference Systems. Journal of Hydrology 387, 328-342 (2010)
12. Fildes, R., Kourentzes, N.: Validation and Forecasting Accuracy in Models of Climate Change. International Journal of Forecasting 27, 968-995 (2011)
13. Zheng, J.: Predicting Software Reliability with Neural Network Ensembles. Expert Systems with Applications 36, 2116-2122 (2009)
14. Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with Artificial Neural Networks: The State of The Art. International Journal of Forecasting 14, 35-62 (1998)
15. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Representations by BackPropagating Errors. Nature 323, 533-536 (1986)
16. Cybenko, G.: Approximation by Superpositions of Sigmoidal Function. Math. Contr. Signals Syst. 2, 303-314 (1989)
17. Funahashi, K.-I.: On The Approximate Realization of Continuous Mappings by Neural Networks. Neural Networks 2, 183-192 (1989)
18. Hornik, K.: Approximation Capabilities of Multilayer Feedforward Networks. Neural Networks 4, 251-257 (1991)
19. Kim, M.-J., Kang, D.-K.: Ensemble with Neural Networks for Bankruptcy Prediction. Expert Systems with Applications 37, 3373-3379 (2010)
20. Kourentzes, N., Barrow, D.K., Crone, S.F.: Neural Network Ensemble Operators for Time Series Forecasting. Expert Systems with Applications 41, 4235-4244 (2014)
21. Hansen, L.K., Salamon, P.: Neural Network Ensembles. IEEE Trans. Pattern Analysis and Machine Intelligence 12, 993-1001 (1990)
22. Kim, S.S.: Time-Delay Recurrent Neural Network for Temporal Correlations and Prediction. Neurocomputing 20, 253-263 (1998)
23. Leontaritis, I.J., Billings, S.A.: Input-Output Parametric Models for Non-Linear Systems. Part I: Deterministic Non-Linear Systems. International Journal of Control 41, 303-328 (1985)
24. Hao, Y., TianTian, X., Paszczynski, S., Wilamowski, B.M.: Advantages of Radial Basis Function Networks for Dynamic System Design. IEEE Transactions on Industrial Electronics 58, 5438-5450 (2011)