Abstract
The high computational complexity of many problems in financial decision-making has prevented the development of time-efficient deterministic solution algorithms so far. At least for some of these problems, e.g., constrained portfolio selection or non-linear time series prediction problems, the results from complexity theory indicate that there is no way to avoid this problem. Due to the practical importance of these problems, we require algorithms for finding optimal or near-optimal solutions within reasonable computing time. Hence, heuristic approaches are an interesting alternative to classical approximation algorithms for such problems. Over the last years many interesting ideas for heuristic approaches were developed and tested for financial decision-making. We present an overview of the relevant methodology, and, some applications that show interesting results for selected problems in finance.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
M. Garey, D. Johnson, Computers and, Intractability, New York, W. H. Freeman& Company, 1979.
C. Papadimitriou, Computational Complexity, Reading, Addison-Wesley, 1994.
D. Seese, F. Schlottmann, The building blocks of complexity: a unified criterion and selected applications in economics and, finance, presented at Sydney Financial Mathematics Workshop 2002, http://www.qgroup.org.au/SFMW
G. Ausiello, P. Crescenzi, G. Gambosi, V. Kann, A. Marchetti-Spaccamela, M. Protasi, Complexity and, Approximation, Springer, Heidelberg, 1999.
C. Reeves (ed.), Modern Heuristic Techniques for Combinatorial Problems, Oxford, Blackwell Scientific Publishers, 1993.
I. Osman, J. Kelly (eds.), Meta-heuristics: Theory and, Applications, Dordrecht, Kluwer, 1996.
E. Aarts and, J. Lenstra (eds.), Local Search in Combinatorial Optimization, Chichester, John Wiley&Sons, 1997.
D. Fogel, Z. Michalewicz, How to Solve it Modern Heuristics, Springer, Heidelberg, 2000.
D. Pham, D. Karaboga, Intelligent Optimization Techniques, Springer, London, 2000.
O. Nelles, Nonlinear System Identification, Springer, Heidelberg, 2001.
S. Chen (ed.), Evolutionary Computation in Economics and, Finance, Springer, Heidelberg, 2002.
S. Kirkpatrick, C. Gelatt and, M. Vecchi, Optimization by simulated annealing, Science 220 (1983), 671–680.
V. Cerny, Thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm, Journal of Optimization Theory and, Applications 45 (1985), 41–51.
W. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and, E. Teller, Equation of the state calculations by fast computing machines, Journal of Chemical Physics 21 (1953), 1087–1092.
E. Aarts and, J. Korst, Simulated annealing and, Boltzmann machines: a stochastic approach to combinatorial optimization and, neural computing, Chichester, John Wiley& Sons, 1989.
E. Aarts, J. Korst and, P. van Laarhoven, Simulated annealing, in: E. Aarts and, J. Lenstra, Local search in combinatorial optimization, Chichester, John Wiley&Sons, 1997, 91–120.
T. Chang, N. Meade, J. Beasley, Y. Sharaiha, Heuristics for cardinality constrained portfolio optimization, Computers&Operations Research 27 (2000), 1271–1302.
H. Markowitz, Portfolio Selection: Efficient Diversification of Investments, John Wiley & Sons, New York, 1959.
G. Dueck and, T. Scheurer, Threshold accepting: A general purpose algorithm appearing superior to simulated annealing, Journal of Computational Physics 90 (1990), 161–175.
G. Dueck and, P. Winker, New concepts and, algorithms for portfolio choice, Applied Stochastic Models and, Data Analysis 8 (1992), 159–178.
M. Gilli and, E. Kellezi, Portfolio optimization with VaR and, expected Shortfall, in: E. Kontoghoirghes, B. Rustem and, S. Siokos (eds.), Computational Methods in Decisionmaking, Economics and, Finance, Kluwer, Dordrecht, 2002.
M. Gilli and, E. Kellezi, Threshold accepting for index tracking, Research paper, University of Geneva, http://www.unige.ch/ses/metri/gilli/portfolio/Yale-2001-IT.pdf.
P. Winker, Optimization Heuristics in Econometrics, John Wiley&Sons, Chichester, 2001.
F. Glover, Future paths for integer programming and, links to artificial intelligence, Computers and, Operations Research 13 (1986), 533–549.
P. Hansen, The steepest ascent mildest descent heuristic for combinatorial programming, presented at Congress on Numerical Methods in Combinatorial Optimization, Capri, 1986.
F. Glover, M. Laguna, Tabu Search, Kluwer, Dordrecht, 1997.
A. Hertz, E. Taillard, and, D. de Werra, Tabu search, in: E. Aarts and, J. Lenstra, Local Search in Combinatorial Optimization, John Wiley&Sons, Chichester, 1997, 121–136.
F. Glover, J. Mulvey, and, K. Hoyland, Solving dynamic stochastic control problems in finance using tabu search with variable scaling, in: H. Osman and, J. Kelly (eds.), Meta-heuristics: Theory and, Applications, Kluwer, Dordrecht, 1996, 429–448.
K. DeJong, D. Fogel, and, H. Schwefel, A history of evolutionary computation, in: T. Baeck, D. Fogel and, Z. Michalewicz (eds.), Evolutionary Computation 1, Bristol, IOP Publishing, 2000, 40–58.
L. Fogel, A. Owens, and, M. Walsh, Artificial Intelligence through Simulated Evolution, John Wiley&Sons, New York, 1966.
J. Koza, Genetic Programming, MIT Press, Cambridge, MA, 1992.
J. Koza, Genetic Programming II, MIT Press, Cambridge, MA, 1994.
J. Koza, F. Bennett, D. Andre, and, M. Keane, Genetic Programming III, Morgan Kaufmann, San Francisco, 1999.
J. Holland, Adaptation in Natural and, Artificial Systems, Michigan University Press, Ann Arbor, 1975.
I. Rechenberg, Cybernetic solution path of an experimental problem, Royal Aircraft Establishment Library Translation 1122, 1965.
H. Schwefel, Evolution and, Optimum Seeking, John Wiley&Sons, Chichester, 1995.
T. Baeck, D. Fogel, Z. Michalewicz (eds.), Evolutionary Computation 1, Bristol, IOP Publishing, 2000.
T. Baeck, D. Fogel, Z. Michalewicz (eds.), Evolutionary Computation 2, Bristol, IOP Publishing, 2000.
W. Banzhaf, J. Daida, A. Eiben, M. Garzon, V. Honavar, M. Jakiela, R. Smith (eds.), Proc. of the Genetic and, Evolutionary Computation Conference, Morgan Kaufmann, San Francisco, 1999.
G. Rudolph, Finite Markov chain results in evolutionary computation: A tour d’horizon, Fundamentae Informaticae, 1998, 1–22.
H. Muehlenbein, Genetic Algorithms, in: E. Aarts and, J. Lenstra (eds.), Local Search in Combinatorial Optimization, John Wiley&Sons, Chichester, 1997, 137–172.
S. Droste, T. Janses and, I. Wegener, Perhaps not a free lunch but at least a free appetiser, in: W. Banzaf et al. (eds.), Proceedings of First Genetic and, Evolutionary Computation Conference, San Francisco, Morgan Kaufmann, 1999, 833–839.
M. Vose, The Simple Genetic Algorithm, MIT Press, Cambridge, MA, 1999.
I. Wegener, On the expected runtime and, the success probability of Evolutionary Algorithms, Lecture Notes in Computer Science 1928, Springer, Heidelberg, 2000.
T. Riechmann, Learning in Economics, Physica, Heidelberg, 2001.
C. Rieck, Evoluationary simulation of asset trading strategies, in: E. Hillebrand, J. Stender (eds.): Many-agent Simulation and, Artificial Life, IOS Press, 1994, 112–136.
P. Tayler, Modelling artificial stock markets using genetic algorithms, in: S. Goonatilake, P. Treleaven (eds.), Intelligent Systems for Finance and, Business, John Wiley& Sons, New York, 1995, 271–287.
B. LeBaron, W. Arthur, R. Palmer, Time series properties of an artificial stock market, Journal of Economic Dynamics&Control 23 (1999), 1487–1516.
J. Coche, An evolutionary approach to the examination of capital market efficiency, Evolutionary Economics 8, 357–382.
J. Farmer, A. Lo, Frontiers of finance: Evolution and, efficient markets, Santa Fe Institute, 1999, http://www.santafe.edu/~jdf.
R. Walker, E. Haasdijk, M. Gerrets, Credit evaluation using a genetic algorithm; in: S. Goonatilake, P. Treleaven (eds.), Intelligent Systems for Finance and, Business, John Wiley&Sons, New York, 1995, 39–59.
S. Mott, Insider dealing detection at the Toronto Stock Exchange Modelling artificial stock markets using genetic algorithms, in: S. Goonatilake, P. Treleaven (eds.), Intelligent systems for finance and, business, John Wiley&Sons, New York, 1995, 135–144.
A. Frick, R. Herrmann, M. Kreidler, A. Narr, D. Seese, A genetic based approach for the derivation of trading strategies on the German stock market, in: Proceedings ICONIP ′96, Springer, Heidelberg, 1996, 766–770.
R. Bauer, Genetic Algorithms and, Investment Strategies, John Wiley&Sons, New York, 1994.
J. Kingdon, Intelligent Systems and, Financial Forecasting, Springer, Heidelberg, 1997.
R. Tsang, P. Lajbcygier, Optimization of technical trading strategy using split search Genetic Algorithms, in: Y. Abu-Mostafa, B. LeBaron, A. Lo, A. Weigend (eds.), Computational Finance 1999, MIT Press, Cambridge, MA, 2000, 690–703.
W. Langdon, R. Poli, Foundations of Genetic Programming, Springer, Heidelberg, 2002.
C. Keber, Option valuation with the Genetic Programming approach, in: Y. Abu-Mostafa, B. LeBaron, A. Lo, A. Weigend, Computational finance 1999, MIT Press, Cambridge, MA, 2000, 370–386.
M. Brennan, E. Schwarz, The valuation of American put options, Journal of Finance 32 (1977), 449–462.
J. Cox, S. Ross, M. Rubinstein, Option pricing: a simplified approach, Journal of Financial Economics 7 (1979), 229–263.
J. Li and, E. Tsang, Reducing failures in investment recommendations using Genetic Programming, presented at 6th Conference on Computing in Economics and, Finance, Barcelona, 2000.
S. Baglioni, C. da Costa Pereira, D. Sorbello and, A. Tettamanzi, An evolutionary approach to multiperiod asset allocation, in: R. Poli, W. Banzhaf, W. Langdon, J. Miller, P. Nordin and, T. Fogarty (eds.), Genetic Programming, Proceedings of EuroGP 2000, Springer, Heidelberg, 2000, 225–236.
S. Chen, T. Kuo, Towards an agent-based foundation of financial econometrics: An approach based on Genetic-Programming financial markets, in: W. Banzhaf et al. (eds.), Proc. of the Genetic and, Evolutionary Computation Conference, Morgan Kaufmann, San Francisco, 1999, 966–973.
W. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics 5 (1943), 115–133
R. Schalkoff, Artificial Neural Networks, New York, McGraw-Hill, 1997.
M. Arbib, The Handbook of Brain Theory and, Neural Networks, MIT Press, Cambridge, MA, 1995.
T. Kohonen, Self-organising Maps, Springer, Heidelberg, 1995.
G. Deboeck and, T. Kohonen, Visual Explorations in Finance, Springer, Heidelberg, 1998.
U. Seiffert, L. Jain (eds.), Self-organising Neural Networks, Springer, Heidelberg, 2002.
D. Rumelhart, J. McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations, MIT Press, Cambridge, MA, 1986.
R. Hecht-Nielsen, Neurocomputing, Addison-Wesley, Reading, MA, 1990.
K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural Networks 4 (1991), 251–257.
M. Anthony, P. Bartlett, Learning in Neural Networks, University Press, Cambridge, UK, 1999.
A. Zapranis, P. Refenes, Priciples of Neural Model Identification, Springer, London, 1999.
D. Witkowska, Neural Networks application to analysis of daily stock returns at the largest stock markets, in: P. Szczepaniak (ed.), Computational Intelligence and, Applications, Heidelberg, Physica, 1999, 351–364.
M. Azoff, Neural Network Time Series Forecasting of Financial Markets, John Wiley &Sons, New York, 1994.
R. Bharati, V. Desai, M. Gupta, Predicting real estate returns using Neural Networks, Journal of Computational Intelligence in Finance 7 (1999) 1, 5–15.
J. Baetge, A. Jerschensky, Measurement of the probability of insolvency with Mixture-of-Expert Networks, in: W. Gaul, H. Locarek-Junge (eds.), Classification in the Information Age, Springer, Heidelberg, 1999, 421–429.
A. Refenes, Neural Networks in the Capital Markets, John Wiley&Sons, Chichester, 1995.
R. Trippi, E. Turban, Neural Networks in Finance and, Investing, Probus Publishing, Chicago, 1993.
M. Odom, R. Sharda, A Neural Network model for bankruptcy prediction, Proceedings of the IEEE International Joint Conference an Neural Networks, Vol. 2, 1990, 163–167.
K. Coleman, T. Graettinger and, W. Lawrence, Neural Networks for bankruptcy prediction: The power to solve financial problems, in: AI review (1991) 4, 48–50.
R. McLeod, D. Malhotra and, R. Malhotra, Predicting credit risk, A Neural Network Approach, Journal of Retail Banking (1993) 3, 37–44.
J. Baetge and, C. Krause, The classification of companies by means of Neural Networks, Journal of Information Science and, Technology 3 (1993) 1, 96–112.
R. Wilson, R. Sharda, Bankruptcy prediction using Neural Networks, Decision Support Systems 11 (1994), 545–557.
E. Altaian, Financial ratios, discriminant analysis and, the prediction of corporate bankruptcy, Journal of Finance 23 (1968), 189–209.
E. Altaian, G. Marco and, F. Varetto, Corporate distress diagnosis: Comparisions using linear discriminant analysis and, Neural Networks, Journal of Banking and, Finance 18 (1994) 3, 505–529.
A. Beltratti, S. Margarita and, P. Terna, Neural Networks for Economic and, Financial Modelling, International Thomson Computer Press, London, 1994.
M. Malliaris and, L. Salchenberger, Beating the best: A Neural Network challenges the Black-Scholes formula, Applied Intelligence 3 (1993) 3, 193–206.
J. Hutchinson, A. Lo, and, T. Poggio, A nonparametric approach to pricing and, hedging derivative securities, Journal of Finance 49 (1994) 3, 851–889.
P. Lajbcygier, A. Flitman, A. Swan, and, R. Hyndman, The pricing and, trading of options using a hybrid Neural Network model with historical volatility, NeurvVest Journal 5 (1997) 1, 27–41.
M. Hanke, Neural Network approximation of analytically intractable option pricing models, Journal of Computational Intelligence in Finance 5 (1997) 5, 20–27.
R. Herrmann, A. Narr, Risk neutrality, Risk (1997) 8.
P. Lajbcygier, Literature review: The non-parametric models, Journal of Computational Intelligence in Finance 7 (1999) 6, 6–18.
F. Black, M. Scholes, The valuation of option contracts and, a test of market efficiency, Journal of Finance 27 (1972), 399–417.
F. Black, M. Scholes, The pricing of options and, corporate liabilities, Journal of Political Economy 81 (1973), 637–654.
H. Locarek-Junge and, R. Prinzler, Estimating Value-at-Risk using Artificial Neural Networks, in: C. Weinhardt, H. Meyer zu Seihausen and, M. Morlock (eds.), Informationssysteme in der Finanzwirtschaft, Springer, Heidelberg, 1998, 385–399.
C. Bishop, Neural Networks for Pattern Recognition, Clarendon Press, Oxford, 1995.
P. Jorion, Value-at-Risk: The new benchmark for controlling market risk, Irwin, Chicago, 1997.
J. P. Morgan and, Reuters, RiskMetrics™ Technical Document, New York, 1996, http://www.rmg.com.
P. Nairn, P. Herve, and, H. Zimmermann, Advanced adaptive architectures for asset allocation, in: C. Dunis (ed.), Advances in Quantitative Asset Management, Kluwer Academic Publishers, Norwell, MA, 2000, 89–112.
M. Bonilla, P. Marco, I. Olmeda, Forecasting exchange rate volatilities using Artificial Neural Networks, in: M. Bonilla, T. Casasus and, R. Sala, Financial Modelling, Physica, Heidelberg, 2000, 57–68.
C. Alexander, Volatility and, correlation: Measurement, models and, applications, in: C. Alexander (ed.), Risk Management and, Analysis, Vol. 1: Measuring and, Modelling Financial Risk, John Wiley&Sons, New York, 1998, 125–171.
S. Judd, Time complexity of learning, in: M. Arbib (ed.), Handbook of Brain Theory and, Neural Networks, MIT Press, Cambridge, MA, 1995, 984–990.
L. Zadeh, Fuzzy sets, Information and, Control 8, (1965) 338–352.
L. Zadeh, Outline of a new approach to the analysis of complex systems and, decision processes, IEEE Transactions on Systems, Man and, Cybernetics, SMC-3 (1973) 1, 28–44.
G. Klir, B. Yuan, Fuzzy Sets and, Fuzzy Logic: Theory and, Applications, Prentice-Hall, Upper Saddle River, NJ, 1995.
G. Klir, B. Yuan (eds.), Fuzzy Sets, Fuzzy Logic and, Fuzzy Systems, Singapore, World Scientific, 1995.
L. Wang, Fuzzy systems are universal approximators, in: Proceedings of the First IEEE International Conference on Fuzzy Systems, San Diego, 1992, 1163–1169.
B. Kosko, Fuzzy systems as universal approximators, in: IEEE Transactions on Computers, 43 (1994) 9, 1329–1333.
C. von Altrock, Fuzzy Logic and, NeuroFuzzy Applications in Business and, Finance, Prentice-Hall, Upper Saddle River, NJ, 1997.
H. Rommelfanger, Fuzzy logic based systems for checking credit solvency of small business firms, in: R. Ribeiro, H.-J. Zimmermann, R. Yager and, J. Kacprzyk (eds.), Soft Computing in Financial Engineering, Physica, Heidelberg, 1999, 371–387.
R. Weber, Applications of Fuzzy logic for credit worthiness evaluation, in: R. Ribeiro, H.-J. Zimmermann, R. Yager and, J. Kacprzyk (eds.), Soft Computing in Financial Engineering, Physica, Heidelberg, 1999, 388–401.
D. Ruan, J. Kacprzyk, M. Fedrizzi, Soft Computing for Risk Evaluation and, Management, Physica, Heidelberg, 2001, 375–409.
K. Korolev, K. Leifert, and, H. Rommelfanger, Fuzzy logic based risk management in financial intermediation, in: D. Ruan, J. Kacprzyk, M. Fedrizzi, Soft Computing for Risk Evaluation and, Management, Physica, Heidelberg, 2001, 447–471.
R. Merton, An analytic derivation of the cost of deposit insurance and, loan guarantees, Journal of Banking in Finance (1977) 1, 3–11.
S. Goonatilake, S. Khebbal (eds.), Intelligent Hybrid Systems, John Wiley&Sons, Chichester, 1995.
A. Abraham, M. Koeppen (eds.), Hybrid Information Systems, Springer, Heidelberg, 2002.
D. Rutkowska, Neuro-fuzzy Architectures and, Hybrid Learning, Springer, Heidelberg, 2002.
Y. Jin, Advanced Fuzzy Systems Design and, Applications, Springer, Heidelberg, 2002.
J. Balicki, Evolutionary Neural Networks for solving multiobjective optimization problems, in: P. Szczepaniak, Computational Intelligence and, Applications, Springer, Heidelberg, 1999, 108–199.
M. Gupta, Fuzzy neural computing, in: P. Szczepaniak, Computational Intelligence and Applications, Springer, Heidelberg, 1999, 34–41.
R. Herrmann, M. Kreidler, D. Seese and, K. Zabel, A fuzzy-hybrid approach to stock trading, in: S. Usui, T. Omori (eds.), Proceedings ICONIP ′98, Amsterdam, IOS Press, 1998, 1028–1032.
S. Siekmann, R. Neuneier, H.-J. Zimmermann and, R. Kruse, Neuro-Fuzzy methods applied to the German stock index DAX, in: R. Ribeiro, H.-J. Zimmermann, R. Yager and, J. Kacprzyk (eds.), Soft Computing in Financial Engineering, Physica, Heidelberg, 1999, 186–203.
Z. Harland, Using nonlinear Neurogenetic models with profit related objective functions to trade the US T-Bond future, in: Y. Abu-Mostafa, B. LeBaron, A. Lo, A. Weigend (eds.), Computational Finance 1999, MIT Press, Cambridge, MA, 2000, 327–343.
F. Schlottmann, D. Seese, A hybrid genetic-quantitative method for risk-return optimization of credit portfolios, Proc. QMF′2001 (abstracts), Sydney, 2001, http://www.business.uts.edu.au/resources/qmf2001/F_Schlottmann.pdf.
F. Schlottmann, D. Seese, Finding Constrained Downside Risk-Return Efficient Credit Portfolio Structures Using Hybrid Multi-Objective Evolutionary Computation, in: G. Bol, G. Nakhaeizadeh, S. Rachev, T. Ridder, K.-H. Vollmer (eds.), Credit Risk, Heidelberg, Springer, 2003, 231–265.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer Science+Business Media New York
About this chapter
Cite this chapter
Schlottmann, F., Seese, D. (2004). Modern Heuristics for Finance Problems: A Survey of Selected Methods and Applications. In: Rachev, S.T. (eds) Handbook of Computational and Numerical Methods in Finance. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-0-8176-8180-7_9
Download citation
DOI: https://doi.org/10.1007/978-0-8176-8180-7_9
Publisher Name: Birkhäuser, Boston, MA
Print ISBN: 978-1-4612-6476-7
Online ISBN: 978-0-8176-8180-7
eBook Packages: Springer Book Archive