Abstract
Artificial neural networks (ANNs) have been popularly applied for stock market prediction, since they offer superlative learning ability. However, they often result in inconsistent and unpredictable performance in the prediction of noisy financial data due to the problems of determining factors involved in design. Prior studies have suggested genetic algorithm (GA) to mitigate the problems, but most of them are designed to optimize only one or two architectural factors of ANN. With this background, the paper presents a global optimization approach of ANN to predict the stock price index. In this study, GA optimizes multiple architectural factors and feature transformations of ANN to relieve the limitations of the conventional backpropagation algorithm synergistically. Experiments show our proposed model outperforms conventional approaches in the prediction of the stock price index.
References
Achelis SB (1995) Technical analysis from A to Z. Probus Publishing, Chicago
Adeli H, Hung S (1995) Machine learning: neural networks, genetic algorithms, and fuzzy systems. Wiley, New York
Bala J, Huang J, Vafaie H, DeJong K, Wechsler H (1995) Hybrid learning using genetic algorithms and decision trees for pattern classification. In: Proc of the int jnt conf on artificial intelligence, pp 19–25
Bauer RJ (1994) Genetic algorithms and investment strategies. Wiley, New York
Chang J, Jung Y, Yeon K, Jun J, Shin D, Kim H (1996) Technical indicators and analysis methods. Jinritamgu Publishing, Seoul
Chen AS, Leung MT, Daouk H (2003) Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Comput Oper Res 30(6):901–923
Choi J (1995) Technical indicators. Jinritamgu Publishing, Seoul
Coakley JR, Brown CE (2000) Artificial neural networks in accounting and finance: modeling issues. Int J Intell Syst Account Finance Manag 9(2):119–144
Cooper DR, Emory CW (1995) Business research methods. Irwin, Chicago
Dash M, Liu H (1997) Feature selection methods for classifications. Intell Data Anal 1(3):131–156
Davis L (1994) Handbook of genetic algorithms. Van Nostrand Reinhold, New York
Dorsey R, Sexton R (1998) The use of parsimonious neural networks for forecasting financial time series. J Comput Intell Finance 6(1):24–31
Dougherty J, Kohavi R, Sahami M (1995) Supervised and unsupervised discretization of continuous features. In: Proc of the 12th int conf on machine learning, San Francisco, pp 194–202
Durand N, Alliot J, Medioni F (2000) Neural nets trained by genetic algorithms for collision avoidance. Appl Intell 13:205–213
Fayyad UM, Irani KB (1993) Multi-interval discretization of continuous-valued attributes for classification learning. In: Proc of the 13th int jnt conf on artificial intelligence, pp 1022–1027
Gifford E (1995) Investor’s guide to technical analysis: predicting price action in the markets. Pitman, London
Gupta JND, Sexton RS (1999) Comparing backpropagation with a genetic algorithm for neural network training. Omega-Int J Manage Sci 27(6):679–684
Hansen JV (1998) Comparative performance of backpropagation networks designed by genetic algorithms and heuristics. Int J Intell Syst Account Finance Manag 7(2):69–79
Hansen JV, Nelson RD (2003) Forecasting and recombining time-series components by using neural networks. J Oper Res Soc 54(3):307–317
Henderson CE, Potter WD, McClendon RW, Hoogenboom G (2000) Predicting aflatoxin contamination in peanuts: a genetic algorithm/neural network approach. Appl Intell 12:183–192
Ignizio JP, Soltys R (1996) Simultaneous design and training of ontogenic neural network classifiers. Comput Oper Res 23(6):535–546
Kaikhah K, Garlick R (2000) Variable hidden layer sizing in Elman recurrent neuro-evolution. Appl Intell 12:193–205
Kim K, Han I (2000) Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Syst Appl 19(2):125–132
Lacerda E, Carvalho ACPLF, Braga AP, Ludermir TB (2005) Evolutionary radial basis functions for credit assessment. Appl Intell 22:167–181
Leung FHF, Lam HK, Ling SH, Tam PKS (2003) Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans Neural Netw 14(1):79–88
Liu H, Motoda H (1998) Feature transformation and subset selection. IEEE Intell Syst Their Appl 13(2):26–28
Liu H, Setiono R (1996) Dimensionality reduction via discretization. Knowl-Based Syst 9(1):67–72
Maniezzo V (1994) Genetic evolution of the topology and weight distribution of neural networks. IEEE Trans Neural Netw 5(1):39–53
Martens J, Wets G, Vanthienen J, Mues C (1998) An initial comparison of a fuzzy neural classifier and a decision tree based classifier. Expert Syst Appl 15(3-4):375–381
Masters T (1993) Practical neural network recipes in C++. Academic Press, Boston
McNelis PD (2005) Neural networks in finance: gaining predictive edge in the market. Elsevier Academic Press, Amsterdam
Montana D, Davis L (1989) Training feedforward neural networks using genetic algorithms. In: Proc of the 11th int jnt conf on artificial intelligence, Detroit, pp 762–767
Murphy JJ (1986) Technical analysis of the futures markets: a comprehensive guide to trading methods and applications. Prentice-Hall, New York
Ornes C, Sklanski J (1997) A neural network that explains as well as predicts financial market behavior. In: Proc of the IEEE/IAFE, pp 43–49
Pujol JCF, Poli R (1998) Evolving the topology and the weights of neural networks using a dual representation. Appl Intell 8:73–84
Rumelhart DE, McClelland JL (1986) Parallel distributed processing: explorations in the microstructure of cognition, vol 1. MIT Press, Cambridge
Salcedo-Sanz S, Bousono-Calzon C (2005) A hybrid neural-genetic algorithm for the frequency assignment problem in satellite communications. Appl Intell 22:207–217
Scott PD, Williams KM, Ho KM (1997) Forming categories in exploratory data analysis and data mining. In: Liu X, Cohen P Berthold M (eds) Advances in intelligent data analysis. Springer, Berlin, pp 235–246
Sexton RS (1998) Identifying irrelevant input variables in chaotic time series problems: using genetic algorithm for training neural networks. J Comput Intell Finance 6(5):34–41
Sexton RS, Alidaee B, RE Dorsey, Johnson JD (1998) Global optimization for artificial neural networks: a tabu search application. Eur J Oper Res 106(2–3):570–584
Sexton RS, RE Dorsey, Johnson JD (1998) Toward global optimization of neural networks: a comparison of the genetic algorithm and backpropagation. Decis Support Syst 22(2):171–185
Sexton RS, Dorsey RE, Johnson JD (1999) Optimization of neural networks: a comparative analysis of the genetic algorithm and simulated annealing. Eur J Oper Res 114(3):589–601
Sexton RS, McMurtrey S, Michalopoulos JO, AM Smith (2005) Employee turnover: a neural network solution. Comput Oper Res 32(10):2635–2651
Sexton RS, Sriram RS, Etheridge H (2003) Improving decision effectiveness of artificial neural networks: a modified genetic algorithm approach. Decis Sci 34(3):421–442
Shang Y, Wah BW (1996) Global optimization for neural network training. Computer 29(3):45–54
Shin TS, Han I (2000) Optimal signal multi-resolution by genetic algorithms to support artificial neural networks for exchange-rate forecasting. Expert Syst Appl 18(4):257–269
Shin KS, Lee YJ (2002) A genetic algorithm application in bankruptcy prediction modeling. Expert Syst Appl 23(3):321–328
Shin K, Shin, T, Han I (1998) Neuro-genetic approach for bankruptcy prediction: a comparison to back-propagation algorithms. In: Proc of the int conf of the Korea society of management information systems 1998, Seoul, South Korea, pp 585–597
Slowinski R, Zopounidis C (1995) Application of the rough set approach to evaluation of bankruptcy risk. Int J Intell Syst Account Finance Manag 4:27–41
Susmaga R (1997) Analyzing discretizations of continuous attributes given a monotonic discrimination function. Intell Data Anal 1(3):157–179
Ting KA (1997) Discretization in lazy learning algorithms. Artif Intell Rev 11(1–5):157–174
Vafaie H, DeJong K (1998) Feature space transformation using genetic algorithms. IEEE Intell Syst Their Appl 13(2):57–65
Valova I, Milano G, Bowen K, Gueorguieva N (2010) Bridging the fuzzy, neural and evolutionary paradigms for automatic target recognition. Appl Intell Online First
Vellido A, Lisboa PJG, Vaughan J (1999) Neural networks in business: a survey of applications. Expert Syst Appl 17(1):51–70
Wallet BC, Marchette DJ, Solka JL, Wegman EJ (1996) A genetic algorithm for best subset selection in linear regression. In: Proc of the 28th symp on the interface of computing science and statistics, pp 545–550
Wang T, Qin Z, Jin Z, Zhang S (2010) Handling over-fitting in test cost-sensitive decision tree learning by feature selection, smoothing and pruning. J Syst Softw 83(7):1137–1147
Williamson AG (1995) Refining a neural network credit application vetting system with a genetic algorithm. J Microcomput Appl 18(3):261–277
Wong F, Tan C (1994) Hybrid neural, genetic and fuzzy systems. In: Deboeck GJ (ed) Trading on the edge. Wiley, New York, pp 245–247
Yang J, Honavar V (1998) Feature subset selection using a genetic algorithm. IEEE Intell Syst Their Appl 13(2):44–49
Yao X, Liu Y (1997) A new evolutionary system for evolving artificial neural networks. IEEE Trans Neural Netw 8(3):694–713
Zhang S (2010) Shell-neighbor method and its application in missing data imputation. Appl Intell. doi:10.1007/s10489-009-0207-6
Zhao Q, Higuchi T (1996) Efficient learning of NN-MLP based on individual evolutionary algorithm. Neurocomputing 13(2–4):201–215
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Kim, Kj., Ahn, H. Simultaneous optimization of artificial neural networks for financial forecasting. Appl Intell 36, 887–898 (2012). https://doi.org/10.1007/s10489-011-0303-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-011-0303-2