Assessing the Volume of Changes to Banking Assets and Liabilities Using Genetic Algorithms in Additional Funds Needed Model
This paper investigates Small-Medium Banks’ (SMBs) business plans in accordance with the structure of Additional Funds Needed (AFN) model. The Key Profitability Variables (KPVs) are the size and structure of deposits, loans, and their interest rates. This study employs a Genetic Algorithm (GA) problem with hard constraints, to point out the limits to changes in the structure of deposits and loans and the effects of those changes on the P&L of a banking institution. After examining 10,000 iterations with Evolver, an innovative optimization software that uses GA, OptQuest, and linear programming, the alternations, have been narrowed down to 3700 which satisfy both, a) constraints and b) maximization of profits. Having also the distributions, this paper concludes that it is a useful methodology that must be further exploited by applying risk weights, mainly for credit risk, to the structural components of the Balance Sheet, and to other competitive institutions.
Keywords: banking institutions, genetic algorithms, additional funds needed, operational research
 Smirlock M. Evidence on the (non) relationship between concentration and profitability in banking. Journal of Money, Credit and Banking. 1985;17(1):69.
 Shao M, Smonou D, Kampouridis M, Tsang E. Guided Fast Local Search for speeding up a financial forecasting algorithm. 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE; 2014.
 Short BK. The relation between commercial bank profit rates and banking concentration in Canada, Western Europe, and Japan. Journal of Banking and Finance. 1979;3(3):209–219.
 Bourke P. Concentration and other determinants of bank profitability in Europe, North America and Australia. Journal of Banking and Finance. 1989;13(1):65–79.
 Meyer TP, Packard NH. Local forecasting of high-dimensional chaotic dynamics. Santa Fe Institute Studies in the Sciences of Complexity-Proceedings. Addison- Wesley Publishing Co; 1992.
 Akhavein JD, Berger AN, Humphrey DB. The effects of megamergers on efficiency and prices: Evidence from a bank profit function. SSRN Electronic Journal. 1997.
 Bikker JA, Hu H. Cyclical patterns in profits, provisioning and lending of banks and procyclicality of the new basel capital requirements. PSL Quarterly Review. 2002;221.
 Eichengreen B, Gibson HD. Greek banking at the dawn of the new millennium. 2001.
 Berger AN, DeYoung R, Genay H, Udell GF. Globalization of financial institutions: Evidence from cross-border banking performance. SSRN Electronic Journal. 2000;
 Bashir AHM. Determinants of profitability in Islamic banks: Some evidence from the Middle East. 2003.
 Davydenko A. Determinants of bank profitability in Ukraine. Undergraduate Economic Review. 2010;7(1).
 Javaid S, Anwar J, Zaman K, Gafoor A. Determinants of bank profitability in Pakistan: Internal factor analysis. Mediterranean Journal of Social Sciences. 2011;2(1).
 Tapia MGC, Coello CAC. Applications of multi-objective evolutionary algorithms in economics and finance: A survey. IEEE Congress on Evolutionary Computation. 2007;7:532–539.
 Căpraru B, Ihnatov I. Determinants of bank’s profitability in EU15. Analele științifice ale Universității ”Al. I. Cuza” din Iași. Secțiunea IIIc, Științe economice. 2015;62(1):93–101.
 Hofstrand D. Understanding profitability. Ag Decisions Makers. 2009;2:C3–C24.
 Martinez Peria MS, Majnoni G, Jones MT, Blaschke W. Stress testing of financial systems: An overview of issues, methodologies, and FSAP experiences. IMF Working Paper. 2001;01(88):1.
 Trigkas S, Liapis K, Thalassinos E. Contributions to Management Science. Cham: Springer International Publishing; 2021. Administrative accounting information to control profitability under certainty and uncertainty of a universal bank. p. 53–78.
 Jiménez G, Mencía J. Modelling the distribution of credit losses with observable and latent factors. Journal of Empirical Finance. 2009;16(2):235–253.
 Castren O, Fitzpatrick T, Sydow M. Assessing portfolio credit risk changes in a sample of EU large and complex banking groups in reaction to macroeconomic shocks. 2008.
 Kapadia S, Drehmann M, Elliott J, Sterne G. Liquidity risk, cash-flow constraints and systemic feedbacks. SSRN Electronic Journal. 2012.
 Tregenna F. The fat years: The structure and profitability of the US banking sector in the pre-crisis period. Cambridge Journal of Economics. 2009;33(4):609–632.
 Aguilar-Rivera R, Valenzuela-Rendón M, Rodríguez-Ortiz JJ. Genetic algorithms and Darwinian approaches in financial applications: A survey. Expert Systems with Applications. 2015;42(21):7684–7697.
 Van Den End JW, Hoeberichts M, Tabbae M. Modelling scenario analysis and macro stress-testing. 2006.
 Gaspar-Cunha A, Recio G, Costa L, Estébanez C. Self-adaptive MOEA feature selection for classification of bankruptcy prediction data. Scientific World Journal. 2014;2014:1–20.
 Da Costa Moraes MB, Nagano MS. Evolutionary models in cash management policies with multiple assets. Economic Modelling. 2014;39:1–7.
 Spanos PM, Galanos CL, Liapis KJ. Economic and Financial Challenges for Eastern Europe. Cham: Springer International Publishing; 2019. Corporate financial modeling using quantitative methods. p. 161–183.
 Polanski A, School of Management and Economics Queen’s University of Belfast. Genetic algorithm search for predictive patterns in multidimensional time series. Complex Systems. 2010;19(3):195–210.
 Back B, Laitinen T, Sere K. Neural networks and genetic algorithms for bankruptcy predictions. Expert Systems with Applications. 1996;11(4):407–413.
 Ponsich A, Jaimes AL, Coello CAC. A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. IEEE Transactions on Evolutionary Computation. 2013;17(3):321–344.
 Hochreiter R, Wozabal D. Natural Computing in Computational Finance. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010. Evolutionary estimation of a coupled Markov chain credit risk model. p. 31–44.
 Nikolaos L, Iordanis E. Default prediction and bankruptcy hazard analysis into recurrent neuro-genetic-hybrid networks to adaboost M1 regression and logistic regression models in finance. 7th WSEAS International Conference on Engineering Education. 2010:22–24.
 Lin F, Liang D, Yeh C-C, Huang J-C. Novel feature selection methods to financial distress prediction. Expert Systems with Applications. 2014;41(5):2472–2483.
 Jiang Y, Xu L, Wang H, Wang H. Influencing factors for predicting financial performance based on genetic algorithms. Systems Research and Behavioral Science. 2009;26(6):661–673.
 Huang C-F, Chang C-H, Kuo L-M, Lin B-H, Hsieh T-N, Chang B-R. A genetic-search model for first-day returns using IPO fundamentals. 2012 International Conference on Machine Learning and Cybernetics. IEEE; 2012.
 Packard NH. A genetic learning algorithm for the analysis of complex data. Complex Systems. 1990;4:543–572.
 Kingdon J, Taylor J, Mannion C. Intelligent systems and financial forecasting. New York: Springer-Verlag, Inc.; 1997.
 Chen S-H, Lee W-C. Option pricing with genetic algorithms: A second report. International Conference on Neural Networks. Vol. 1. IEEE; 1997.
 Kim K-J, Han I. Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications. 2000;19(2):125–132.
 Ma I, Wong T, Sankar T, Sin R. Forecasting the volatility of a financial index by wavelet transform and evolutionary algorithm. 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat No04CH37583). IEEE; 2005.
 Rimcharoen S, Sutivong D, Chongstitvatana P. Prediction of the Stock Exchange of Thailand using adaptive evolution strategies. 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’05). IEEE; 2005.
 Goonatilake S, Campbell JA, Ahmad N. Advances in fuzzy logic, neural networks and genetic algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg; 1995. Geneticfuzzy systems for financial decision making. p. 202–223.
 Kanungo RP. Genetic algorithms: Genesis of stock evaluation. SSRN Electronic Journal. 2004.
 De Araujo R, Madeiro F, De Sousa RP, Pessoa LF, Ferreira T. An evolutionary morphological approach for financial time series forecasting. IEEE Congress on Evolutionary Computation. IEEE; 2006. p. 2467–2474.
 Parracho P, Neves R, Horta N. Trading with optimized uptrend and downtrend pattern templates using a genetic algorithm kernel. 2011 IEEE Congress of Evolutionary Computation (CEC). IEEE; 2011.
 Araújo R de A, Ferreira TAE. A Morphological-Rank-Linear evolutionary method for stock market prediction. Information Sciences. 2013;237:3–17.
 Bernardo D, Hagras H, Tsang E. A genetic type-2 fuzzy logic-based system for financial applications modelling and prediction. IEEE International Conference on Fuzzy Systems (FUZZ). IEEE; 2013. p. 1–8.
 Ghosh P, Chinthalapati V. Financial time series forecasting using agent-based models in equity and FX markets. 6th Computer Science and Electronic Engineering Conference (CEEC). IEEE; 2014. p. 97–102.
 Garcia-Almanza AL, Tsang EP. Forecasting stock prices using genetic programming and chance discovery. 12th International Conference on Computing in Economics and Finance. 2006.
 Wagner N, Michalewicz Z, Khouja M, McGregor RR. Time series forecasting for dynamic environments: The DyFor genetic program model. IEEE Transactions on Evolutionary Computation. 2007;11(4):433–452.
 Hamida SB, Abdelmalek W, Abid F. Applying dynamic training-subset selection methods using genetic programming for forecasting implied volatility [Internet]. arXiv [q-fin.GN]. 2020. Available from: http://arxiv.org/abs/2007.07207
 Karatahansopoulos A, Sermpinis G, Laws J, Dunis C. Modelling and trading the Greek stock market with gene expression and genetic programing algorithms: Gene expression and genetic programing algorithms. Journal of Forecasting. 2014;33(8):596–610.
 Mahfoud S, Mani G. Artificial Intelligence applications on wall street. Routledge; 2017. Financial forecasting using genetic algorithms. p. 543–563.
 del Arco-Calderón CL, Viñuela PI, Hernández Castro JC. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2004. Forecasting time series by means of evolutionary algorithms. p. 1061–1070.
 Donate JP, Cortez P. Evolutionary optimization of sparsely connected and timelagged neural networks for time series forecasting. Applied Soft Computing. 2014;23:432–443.
 Gupta P, Mehlawat MK, Mittal G. Asset portfolio optimization using support vector machines and real-coded genetic algorithm. Journal of Global Optimization. 2012;53(2):297–315.
 Wang W, Hu J, Dong N. A convex-risk-measure based model and genetic algorithm for portfolio selection. Mathematical Problems in Engineering. 2015;2015:1–8.
 Hochreiter R. Applications of Evolutionary Computation. Cham: Springer International Publishing; 2015. An evolutionary optimization approach to risk parity portfolio selection. p. 279–288.
 Wagman L. Stock portfolio evaluation: An application of genetic programmingbased technical analysis. Genetic Algorithms and Genetic Programming at Stanford. 2003;213–220.
 Krink T, Paterlini S. Multiobjective optimization using differential evolution for realworld portfolio optimization. Computational Management Science. 2011;8(1–2):157– 179.
 Lwin K, Qu R, Kendall G. A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization. Applied Soft Computing. 2014;24:757–772.
 García S, Quintana D, Galván IM, Isasi P. Multiobjective algorithms with resampling for portfolio optimization. Computing and Informatics. 2014;32:777–796.
 Adebiyi A, Ayo C. Portfolio selection problem using generalized differential evolution 3. Applied Mathematical Sciences. 2015;9:2069–2082.