Abstract
In the past two decades, there has been much interest in applying neural networks to financial time series forecasting. Yet, there has been relatively little attention paid to selecting the input features for training these networks. This paper presents a novel CARTMAP neural network based on Adaptive Resonance Theory that incorporates automatic, intuitive, transparent, and parsimonious feature selection with fast learning. On average, over three separate 4-year simulations spanning 2004–2009 of Dow Jones Industrial Average stocks, CARTMAP outperformed related and classical alternatives. The alternatives were an industry standard random walk, a regression model, a general purpose ARTMAP, and ARTMAP with stepwise feature selection. This paper also discusses why the novel feature selection scheme outperforms the alternatives and how it can represent a step toward more transparency in financial modeling.
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Amis G, Carpenter G (2007) Default ARTMAP 2. In: Proceedings of the international joint conference on neural networks (IJCNN, 07), Orlando, Florida, pp 777–782
Aragones E, Gilboa I, Postlewaite A, Schmeidler D (2005) Fact-free learning. Am Econ Rev 95:1355–1368
Bishop C (2006) Pattern recognition and machine learning. Springer, New York
Blalock HM Jr (1963) Correlated independent variables: the problem of multicollinearity. Soc Forces, University of North Carolina Press
Bodyanskiy Y, Popov S (2006) Neural network approach to forecasting quasiperiodic financial time series. Eur J Oper Res 175:1357–1366
Brockett P, Golden L, Jang J, Yang C (2006) A comparison of neural network, statistical methods, and variable choice for life insurers’ financial distress prediction. J Risk Insur 73(3):397–419
Carpenter G, Grossberg S (1987) A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput Vis Graph Image Process 37(54):115
Carpenter G, Grossberg S, Reynolds J (1991) ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Netw 4:565–588
Carpenter G, Grossberg S, Rosen D (1991) Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Netw 4:759–771
Chao L, Knight R (1998) Contribution of human prefrontal cortex to delay performance. J Cogn Neurosci 10:167–177
Chartered Financial Analyst Institute (2010) CFA program curriculum. CFA Institute, Pearson
Chen W, Shih J (2006) Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets. Int J Electron Finance 1(1):49–67
Chen W, Shih J, Wu S (2006) Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets. Int J Electron Finance 1(1):49–67
Conway A, Cowan N, Bunting M (2001) The cocktail party phenomenon revisited: the importance of working memory capacity. Psychon Bull Rev 8:331–335
Duda R, Hart P, Stork D (2001) Pattern classification. Wiley-Interscience, New York
Fama E (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25(2):383–417
Hamilton J (1994) Time series analysis. Princeton University Press, Princeton
Higgins J (2004) Introduction to modern nonparametric statistics. Brooks/Cole-Thomson Learning, Pacific Grove
Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial time series. Neurocomputing 10:215–236
Kane M, Engle R (2002) The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: an individual-differences perspective. Psychon Bull Rev 9(4):637–671
Kim K (2006) Artificial neural networks with evolutionary instance selection for financial forecasting. Expert Syst Appl 30:519–526
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
Kirkos E, Spathis C, Manolopoulos Y (2007) Data mining techniques for the detection of fraudulent financial statements. Expert Syst Appl 32:995–1003
Kirkpatrick C, Dahlquist J (2006) Technical analysis: the complete resource for financial market technicians. FT Press, Upper Saddle River
Kumar K, Bhattacharya S (2006) Artificial neural network vs linear discriminant analysis in credit ratings forecast. Rev Account Finance 5(3):216–227
Kwon Y, Moon B (2007) A hybrid neurogenetic approach for stock forecasting. IEEE Trans Neural Netw 18(3):851–864
Leung C, Tsoi A (2005) Combined learning and pruning for recurrent radial basis function networks based on recursive least square algorithms. Neural Comput Appl 15:62–78
Lina C, Huang J, Gena M, Tzeng G (2006) Recurrent neural network for dynamic portfolio selection. Appl Math Comput 175(2):1139–1146
Lintner J (1965) The valuation of risk assets and selection of risky investments in stock portfolios and capital budgets. Rev Econ Stat 47:13–37
Liu B, Cui Q, Jiang T, Ma S (2004) A combinational feature selection and ensemble neural network method for classification of gene expression data. BMC Bioinform 5:136. doi:10.1186/1471-2105-5-136
Lo A (2001) Bubble, rubble, finance in trouble? J Psychol Financial Mark 3:76–86
Lo A (2007) The efficient markets hypothesis. In: Blume L, Durlauf S (eds) The new Palgrave dictionary of economics. Palgrave Macmillan, New York
Lo A, Repin D (2002) The psychophysiology of real-time financial risk processing. J Cogn Neurosci 14(3):323–339
Loeb G (1996) The battle for investment survival. Wiley, Malden
Malkiel B (1973) A random walk down Wall Street. W. W. Norton & Company, New York
Markowitz H (1952) The utility of wealth. J Political Econ 2:151–158
McQueen G, Thorley S (1999) Mining fool’s gold. Financial Analysts J 55(2):61–72
Miller P, Seier W (1994) Strategy utilization deficiencies in children: when, where, and why. In: Reese H (ed) Advances in child development and behavior, vol 25. Academic Press, Salt Lake City
Murphy J (1999) Technical analysis of the financial markets: a comprehensive guide to trading methods and applications. New York Institute of Finance, New York
Parsons O, Carpenter G (2003) ARTMAP neural networks for information fusion and data mining: map production and target recognition methodologies. Neural Netw 16:1075–1089
Posner M, Rothbart M, Thomas-Thrapp L, Gerardi G (1998) Development of orienting to locations and objects. In: Wright R (ed) Visual attention. Oxford University Press, New York
Schwager J (1995) The new market wizards: conversations with America’s top traders. Wiley, Hoboken
Sharpe W (1965) Capital asset prices: a theory of market equilibrium under conditions of risk. J Finance 19(3):425–442
Sharpe W (1994) The sharpe ratio. J Portfolio Manag 21(1):49–58
Siegler R, Alibali M (2005) Children’s thinking. Prentice Hall, Upper Saddle River
Thawornwong S, Enke D (2004) The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing 56:205–232
Versace M, Bhatt R, Hinds O, Schiffer M (2004) Predicting the exchange traded fund DIA with a combination of genetic algorithms and neural networks. Expert Syst Appl 27(3):417–425
Wang K (2006) Neural network approach to vibration feature selection and multiple fault detection for mechanical systems. Innov Comput Inf Control 3(30):431–434
West D (2000) Neural network credit scoring models. Comput Oper Res 27:1131–1152
Williams J (1938) The theory of investment value. Harvard University Press, Cambridge
Witten I, Frank E (2005) Data mining. Elsevier, San Francisco
Wong C, Versace M (2011a) Rethinking neural networks in financial decision-making studies: seven cardinal confounds. In: Global conference on business and finance proceedings, Las Vegas, Nevada
Wong C, Versace M (2011) Echo ARTMAP: context sensitivity with neural networks in financial decision-making. Glob J Bus Res 5(5):27–43
Woods D, Knight R (1986) Electrophysiological evidence of increased distractibility after dorsolateral prefrontal lesions. Neurology 36:212–216
Yu L, Wang S, Lai K (2008) Neural network-based mean–variance–skewness model for portfolio selection. Comput Oper Res 35:34–46
Zhu X, Wang H, Xu L, Li H (2008) Predicting stock index increments by neural networks: the role of trading volume under different horizons. Expert Syst Appl 34:3043–3054
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This research was supported by CELEST, an NSF Science of Learning Center (SBE-0354378).
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Wong, C., Versace, M. CARTMAP: a neural network method for automated feature selection in financial time series forecasting. Neural Comput & Applic 21, 969–977 (2012). https://doi.org/10.1007/s00521-012-0830-8
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DOI: https://doi.org/10.1007/s00521-012-0830-8