A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Applied Soft Computing

Volumn 29, Issue , 2015, Pages 196-210

  Hafezi, Reza ^a,b   Shahrabi, Jamal ^a   Hadavandi, Esmaeil ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

^b Member of Futures Studies Research   (Iran)

Author keywords

Artificial neural network; Bat algorithm; DAX stock price; Fundamental analysis; Multi agent system; Stock price prediction

Indexed keywords

COMMERCE; FINANCIAL MARKETS; FORECASTING; GENETIC ALGORITHMS; INTELLIGENT AGENTS; INTELLIGENT SYSTEMS; INVESTMENTS; MULTI AGENT SYSTEMS; NEURAL NETWORKS;

BAT ALGORITHMS; FUNDAMENTAL ANALYSIS; GENERALIZED REGRESSION NEURAL NETWORK(GRNN); GLOBAL FINANCIAL CRISIS; INTELLIGENT MODELING; MULTIAGENT FRAMEWORK; STOCK PRICE; STOCK PRICE PREDICTION;

ELECTRONIC TRADING;

EID: 84922730958     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2014.12.028     Document Type: Article

References (53)

1. A. Ayodele, A. Charles, A. Marion, and O. Sunday Stock price prediction using neural network with hybridized market indicators J. Emerg. Trends Comput. Inf. Sci. 3 2012 1 9
2. A. Khadjeh Nassirtoussi, T.Y. Wah, and D.N.C. Ling A novel FOREX prediction methodology based on fundamental data Afr. J. Bus. Manage. 5 2011 8322 8330
3. H. Haleh, B. Akbari Moghaddam, and S. Ebrahimijam A new approach to forecasting stock price with EKF data fusion Int. J. Trade Econ. Finance 2 2011 109 114
4. M.H. Fazel Zarandi, E. Hadavandi, and I.B. Turksen A hybrid fuzzy intelligent agent-based system for stock price prediction Int. J. Intell. Syst. 27 2012 1 23
5. J. Zhang, L. Cheng, and H. Wang A multi-agent-based decision support system for bankruptcy contagion effects Expert Syst. Appl. 39 2012 5920 5934
6. K. Khan, and A. Sahai A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context Intell. Syst. Appl. 7 2012 23 29
7. B.J. Vanstone, G. Finnie, and C. Tan Applying fundamental analysis and neural networks in the Australian stockmarket Information Technology Papers 2004
8. C.F. Tsai, and S.P. Wang Stock price forecasting by hybrid machine learning techniques Proceedings of the International Multiconference of Engineers and Computer Scientists Hong Kong 2009
9. Y.Q. Zhang, S. Akkaladevi, G. Vachtsevanos, and T.Y. Lin Granular neural web agents for stock prediction Soft Comput. 6 2002 406 413
10. K. Ali Albashiri, F. Coenen, and P. Leng Emads: an extendible multi-agent data miner Knowl. Based Syst. 22 2009 523 528
11. I. Czarnowski, and P. Jedrzejowicz An agent-based framework for distributed learning Eng. Appl. Artif. Intell. 24 2011 93 102
12. P. Yang, L. Tao, L. Xu, and Z. ZiliZhang Multi agent framework for bio-data mining P. Wen, Lecture Notes in Computer Sciences 2009 Springer-Verlag 200 207
13. M. Wardeh, F. Coenen, and T.B. Capon PISA: a framework for multiagent classification using argumentation Data Knowl. Eng. 75 2012 34 57
14. C.G. Ralha, and C.V.S. Silva A multi-agent data mining system for cartel detection in Brazilian government procurement Expert Syst. Appl. 39 2012 11642 11656
15. C. Giannella, R. Bhargava, and H. Kargupta Multi-agent systems and distributed data mining Cooperative Information Agents VIII 2004 Springer Berlin/Heidelberg 1 15
16. M. Klusch, S. Lodi, and G. Moro Agent-based distributed data mining: the KDEC scheme Intelligent Information Agents 2003 Springer Berlin/Heidelberg 104 122
17. J. Gao, J. Denzinger, and R.C. James A cooperative multi-agent data mining model and its application to medical data on diabetes Autonomous Intelligent Systems: Agents and Data Mining 2005 Springer Berlin/Heidelberg 93 107
18. S.K. Shukla, M.K. Tiwari, and Y.J. Son Bidding-based multi-agent system for integrated process planning and scheduling: a data-mining and hybrid tabu-SA algorithm-oriented approach Int. J. Adv. Manuf. Technol. 38 2008 163 175
19. J.C.d. Silva, C. Giannella, R. Bhargava, H. Karguptab, and M. Klusch Distributed data mining and agents Eng. Appl. Artif. Intell. 18 2005 791 807
20. H.B. Zghal, S. Faiz, and H.B. Ghezala A Framework for Data Mining Based Multi-Agent: An Application to Spatial Data vol. 5 2007 World Academy of Science, Engineering and Technology
21. J.A. Cendón, A.G. Marcos, M.C. Limas, and J.O. Meré A multi-agent data mining system for defect forecasting in a decentralized manufacturing environment Comput. Intell. Secur. Inf. Syst. 85 2010 43 50
22. R.S.T. Lee, and J.N.K. Liu iJADE Weather MAN: a weather forecasting system using intelligent multiagent-based fuzzy neuro network IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 34 2004 369 377
23. J. Jumadinova, and P. Dasgupta A multi-agent system for analyzing the effect of information on prediction markets Int. J. Intell. Syst. 26 2011 383 409
24. P.C. Chang, and C.H. Liu A TSK type fuzzy rule based system for stock price prediction Expert Syst. Appl. 34 2008 135 144
25. M.H.F. Zarandi, B. Rezaee, I.B. Turksen, and E. Neshat A type-2 fuzzy rule-based expert system model for stock price analysis Expert Syst. Appl. 36 2009 139 154
26. C.J. Lu Integrating independent component analysis-based denoising scheme with neural network for stock price prediction Expert Syst. Appl. 37 2010 7056 7064
27. M.A. Boyacioglu, and D. Avci An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange Expert Syst. Appl. 37 2010 7908 7912
28. Y. Kara, M.A. Boyacioglu, and Ö.K. Baykan Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul stock exchange Expert Syst. Appl. 38 2011 5311 5319
29. H.M. Feng, and H.C. Chou Evolutional RBFNs prediction systems generation in the applications of financial time series data Expert Syst. Appl. 38 2011 8285 8292
30. C.M. Hsu A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming Expert Syst. Appl. 38 2011 14026 14036
31. T.S. Chang A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction Expert Syst. Appl. 38 2011 14846 14851
32. P.C. Chang, D.d. Wang, and C.l. Zhou A novel model by evolving partially connected neural network for stock price trend forecasting Expert Syst. Appl. 39 2012 611 620
33. W. Kettera, J. Collinsb, M. Ginib, A. Guptac, and P. Schraterb Detecting and forecasting economic regimes in multi-agent automated exchanges Decis. Support Syst. 47 2009 307 318
34. Š. Raudys, and I. Zliobaite The multi-agent system for prediction of financial time series Artificial Intelligence and Soft Computing - ICAISC 2006 2006 Springer Berlin/Heidelberg 653 662
35. J.R. Wei, J.P. Huang, and P.M. Hui An agent-based model of stock markets incorporating momentum investors Physica A: Stat. Mech. Appl. 392 2013 2728 2735
36. B. Cui, H. Wang, K. Ye, and J. Yan Intelligent agent-assisted adaptive order simulation system in the artificial stock market Expert Syst. Appl. 39 2012 8890 8898
37. Y. Li, W. Zhang, Y. Zhang, X. Zhang, and X. Xiong Calibration of the agent-based continuous double auction stock market by scaling analysis Inf. Sci. 256 2012 46 56
38. B.D. Kluger, and M.E. McBride Intraday trading patterns in an intelligent autonomous agent-based stock market J. Econ. Behav. Organ. 79 2011 226 245
39. L. Ponta, S. Pastore, and S. Cincotti Information-based multi-assets artificial stock market with heterogeneous agents Nonlinear Anal.: Real World Appl. 12 2011 1235 1242
40. C.X. Yang, R. Wang, and S. Hu Modeling and analysis of an agent-based model for Chinese stock market Phys. Lett. A 377 2013 2041 2046
41. L. Padgham, and M. Winikoff Developing Intelligent Agent Systems: A Practical Guide 2004 John Wiley & Sons Ltd. England
42. M.V. Sokolova, and A. Fernández-Caballero Evaluation of environmental impact upon human health with DeciMaS framework Expert Syst. Appl. 39 2012 3469 3483
43. E. Parras-Gutierrez, V.M. Rivas, M. Garcia-Arenas, and M.J.d. Jesus Short, medium and long term forecasting of time series using the L-Co-R algorithm Neurocomputing 128 2014 433 446
44. P. Winker Optimized multivariate lag structure selection Comput. Econ. 16 2000 87 103
45. J. Shahrabi, E. Hadavandi, and S. Asadi Developing a hybrid intelligent model for forecasting problems: case study of tourism demand time series Knowl.-Based Syst. 43 2013 112 122
46. K.P. Burnham, and D.R. Anderson Multimodel inference: understanding AIC and BIC in model selection Sociol. Methods Res. 33 2004 261 304
47. J.P. Teixeira, and P.O. Fernandesa Tourism time series forecast - different ANN architectures with time index input Procedia Technol. 5 2012 445 454
48. J. Park, and I.W. Sandberg Universal approximation using radial-basis-function networks Neural Comput. 3 1991 246 257
49. G. Atsalakis, and K. Valavanis Surveying stock market forecasting techniques - Part II: Soft computing methods Expert Syst. Appl. 36 2009 5932 5941
50. X.S. Yang A new metaheuristic bat-inspired algorithm Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) 2010 Springer Berlin/Heidelberg 65 74
51. R.D. Arnott, J. Hsu, and P. Moore Fundamental indexation Financ. Anal. J. 61 2005 83 99
52. W. Gryc Neural Network predictions of Stock Price Fluctuations, Technical Report 2010 http://i2r.org/nnstocks.pdf
53. R. Hassan A combination of hidden Markov model and fuzzy model for stock market forecasting Neurocomputing 72 2009 3439 3446