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Evolutionary Machine Learning in Finance

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Handbook of Evolutionary Machine Learning

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

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Abstract

One way to measure the impact of our field of research on finance is to analyse the adoption of evolutionary machine learning in the finance literature. In this study, we focus on articles appearing in the top-ranked journals in finance. A number of interesting observations are made including that there appears to be a trend in the adoption of evolutionary machine learning across a growing and diverse set of topics.

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Notes

  1. 1.

    https://www.ft.com/content/3405a512-5cbb-11e1-8f1f-00144feabdc0.

  2. 2.

    https://charteredabs.org/academic-journal-guide-2021/.

  3. 3.

    https://scholar.google.com.

References

  1. Telikani, A., Tahmassebi, A., Banzhaf, W., Gandomi, A.H.: Evolutionary machine learning: a survey. ACM Comput. Surv. 54(8), Article 161, 35 (2021)

    Google Scholar 

  2. EvoWorkshops, G., et al. (ed.): Proceedings of Applications of evolutionary computing : EvoWorkshops 2007, EvoCOMNET, EvoFIN, EvoIASP, EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTRANSLOG, Valencia, Spain, April 11–13, 2007. Lecture Notes in Computer Science, vol. 4448. Springer (2007)

    Google Scholar 

  3. Chen, S.-H. (ed.): Evolutionary Computation in Economics and Finance. Physica-Verlag (2002)

    Google Scholar 

  4. Chen, S.-H. (ed.): Genetic Algorithms and Genetic Programming in Computational Finance. Springer (2002)

    Google Scholar 

  5. Chen, S.-H., Wang, P.P. (eds.): Computational Intelligence in Economics and Finance. Springer (2003)

    Google Scholar 

  6. Rennard, J.-P. (ed.): Handbook of Research on Nature-Inspired Computing for Economics and Management. IGI Global (2006)

    Google Scholar 

  7. Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modelling. Springer

    Google Scholar 

  8. Chen, S.-H., Wang, P.P., Kuo, T.-W. (eds.): Computational Intelligence in Economics and Finance, vol. 2. Springer (2007)

    Google Scholar 

  9. Brabazon, A., O’Neill, M. (eds.): Natural Computing in Computational Finance. Springer (2008)

    Google Scholar 

  10. Brabazon, A., O’Neill, M. (eds.): Natural Computing in Computational Finance, (Volume 2). Springer (2009)

    Google Scholar 

  11. Brabazon, A., O’Neill, M., Maringer, D. (eds.): Natural Computing in Computational Finance (Volume 3). Springer (2010)

    Google Scholar 

  12. Brabazon, A., O’Neill, M., Maringer, D. (eds.): Natural Computing in Computational Finance (Volume 4). Springer (2011)

    Google Scholar 

  13. Chen, S.-H., Kaboudan, M., Du, Y.-R. (eds.): The Oxford Handbook of Computational Economics and Finance. Oxford University Press (2018)

    Google Scholar 

  14. Brabazon, A., Kampouridis, M., O’Neill, M.: Applications of genetic programming to finance and economics: past, present, future. Genet. Program. Evolvable Mach. 21(1–2), 33–53 (2020)

    Article  Google Scholar 

  15. Iba, H., Aranha, C.C.: Practical Applications of Evolutionary Computation to Financial Engineering: Robust Techniques for Forecasting, Trading and Hedging. Springer-Verlag, New York Inc (2012). ISBN-10: 3642276474

    Google Scholar 

  16. Brown, S.J., Goetzmann, W.N., Kumar, A.: The Dow theory: William Peter Hamilton’s track record reconsidered. J. Financ. 53, 1311–1333 (1998)

    Article  Google Scholar 

  17. Noe, T.H., Rebello, M.J., Wang, J.: The evolution of security designs. J. Financ. 61, 2103–2135 (2006)

    Article  Google Scholar 

  18. Noe, T.H., Rebello, M.J., Wang, J.: Corporate financing: an artificial agent-based analysis. J. Financ. 58, 943–973 (2003)

    Article  Google Scholar 

  19. Garmaise, M.J.: Borrower misreporting and loan performance. J. Financ. 70, 449–484 (2015)

    Article  Google Scholar 

  20. Cieslak, A., Povala, P.: Information in the term structure of yield curve volatility. J. Financ. 71, 1393–1436 (2016)

    Article  Google Scholar 

  21. Schneider, P., Trojani, F.: (Almost) model-free recovery. J. Financ. 74, 323–370 (2019)

    Article  Google Scholar 

  22. Schwert, M.: Bank capital and lending relationships. J. Financ. 73, 787–830 (2018)

    Article  Google Scholar 

  23. Brogaard, J., Zareei, A.: Machine learning and the stock market. J. Financ. Quant. Anal. 1–66 (2022). https://doi.org/10.1017/S0022109022001120

  24. Neely, C., Weller, P., Dittmar, R.: Is technical analysis in the foreign exchange market profitable? a genetic programming approach. J. Financ. Quant. Anal. 32(4), 405–426 (1997). https://doi.org/10.2307/2331231

    Article  Google Scholar 

  25. Allen, F., Karjalainen, R.: Using genetic algorithms to find technical trading rules. J. Financ. Econ. 51(2), 245–271 (1999)

    Google Scholar 

  26. Cummins, J.D., Lalonde, D., Phillips, R.D.: The basis risk of catastrophic-loss index securities. J. Financ. Econ. 71(1), 77–111 (2004)

    Google Scholar 

  27. Ewens, M., Gorbenko, A., Korteweg, A.: Venture capital contracts. J. Financ. Econ. 143(1), 131–158 (2022)

    Google Scholar 

  28. Bajgrowicz, P., Scaillet, O.: Technical trading revisited: false discoveries, persistence tests, and transaction costs. J. Financ. Econ. 106(3), 473–491 (2012)

    Google Scholar 

  29. Craig, B., Ma, Y.: Intermediation in the interbank lending market. J. Financ. Econ. 145(2), Part A, 179–207 (2022)

    Google Scholar 

  30. Joenväärä, J., Kosowski, R.: The effect of regulatory constraints on fund performance: new evidence from UCITS hedge funds. Rev. Financ. 25(1), 189–233 (2021)

    Google Scholar 

  31. Lensberg, T., Schenk-Hoppé, K.R.: On the evolution of investment strategies and the Kelly rule-A Darwinian approach. Rev. Financ. 11(1), 25–50 (2007)

    Google Scholar 

  32. O’Doherty, M.S., Savin, N.E., Tiwari, A.: Hedge fund replication: a model combination approach. Rev. Financ. 21(4), 1767–1804 (2017)

    Google Scholar 

  33. Michaels, R., Beau Page, T., Whited, T.M.: Labor and capital dynamics under financing frictions. Rev. Financ. 23(2), 279–323 (2019)

    Google Scholar 

  34. Routledge, B.R.: Adaptive learning in financial markets. Rev. Financ. Stud. 12(5), 1165–1202 (1999)

    Google Scholar 

  35. Patton, A.J., Weller, B.M.: Risk price variation: the missing half of empirical asset pricing. Rev. Financ. Stud. (2022). hhac012

    Google Scholar 

  36. Payzan-LeNestour, E., Bossaerts, P.: Learning about unstable, publicly unobservable payoffs. Rev. Financ. Stud. 28(7), 1874–1913 (2015)

    Google Scholar 

  37. Chernov, M., Dunn, B.R., Longstaff, F.A.: Macroeconomic-driven prepayment risk and the valuation of mortgage-backed securities. Rev. Financ. Stud. 31(3), 1132–1183 (2018)

    Google Scholar 

  38. Adam, K.: Learning and equilibrium selection in a monetary overlapping generations model with sticky prices. Rev. Econ. Stud. 70(4), 887–907 (2003)

    Google Scholar 

  39. Qu, Z., Tkachenko, D.: Global identification in DSGE models allowing for indeterminacy. Rev. Econ. Stud. 84(3), 1306–1345 (2017)

    Google Scholar 

  40. Dew-Becker, I.: Bond pricing with a time-varying price of risk in an estimated medium-scale Bayesian DSGE model. J. Money Credit. Bank. 46, 837–888 (2014)

    Article  Google Scholar 

  41. Drenovak, M., Ranković, V., Urošević, B., Jelic, R.: Mean-maximum drawdown optimization of buy-and-hold portfolios using a multi-objective evolutionary algorithm. Financ. Res. Lett. 46, Part A, 102328 (2021)

    Google Scholar 

  42. Baek, S., Mohanty, S.K., Glambosky, M.: COVID-19 and stock market volatility: an industry level analysis. Financ. Res. Lett. 37(2020), 101748 (2020)

    Article  Google Scholar 

  43. Gong, X.-L., Xiong, X.: Multi-objective portfolio optimization under tempered stable Lévy distribution with Copula dependence. Financ. Res. Lett. 38, 101506 (2021)

    Google Scholar 

  44. Yu, L.,. Zhang, X.: Can small sample dataset be used for efficient internet loan credit risk assessment? Evidence from online peer to peer lending. Financ. Res. Lett. 38, 101521 (2020)

    Google Scholar 

  45. Kamali, R., Mahmoodi, S., Jahandideh, M.-T.: Optimization of multi-period portfolio model after fitting best distribution. Financ. Res. Lett. 30, 44–50 (2019)

    Google Scholar 

  46. Geuder, J., Kinateder, H., Wagner, N.F.: Cryptocurrencies as financial bubbles: The case of Bitcoin. Financ. Res. Lett. 31, 2019 (2018)

    Google Scholar 

  47. Ma, C., Xiao, R., Mi, X.: Measuring the dynamic lead-lag relationship between the cash market and stock index futures market. Financ. Res. Lett. 47, Part B, 102940 (2022)

    Google Scholar 

  48. Boudt, K., Lu, W., Peeters, B.: Higher order comoments of multifactor models and asset allocation. Financ. Res. Lett. 13, 225–233 (2015)

    Google Scholar 

  49. Shi, Y.: A closed-form estimator for the Markov switching in mean model. Financ. Res. Lett. 44(2022), 102107 (2021)

    Google Scholar 

  50. Lensberg, T., Schenk-Hoppé, K.R., Ladley, D.: Costs and benefits of financial regulation: short-selling bans and transaction taxes. J. Bank. & Financ. 51, 103–118 (2015)

    Google Scholar 

  51. Varetto, F.: Genetic algorithms applications in the analysis of insolvency risk. J. Bank. & Financ. 22(10–11), 1421–1439 (1998)

    Google Scholar 

  52. Chavez-Demoulin, V., McGill, J.A.: High-frequency financial data modeling using Hawkes processes. J. Bank. & Financ. 36(12), 3415–3426 (2012)

    Google Scholar 

  53. Marshall, B.R., Young, M.R., Rose, L.C.: Candlestick technical trading strategies: can they create value for investors?. J. Bank. & Financ. 30(8), 2303–2323 (2005)

    Google Scholar 

  54. Langfield, S., Liu, Z., Ota, T.: Mapping the UK interbank system. J. Bank. & Financ. 45, 288–303 (2014)

    Google Scholar 

  55. Krink, T., Paterlini, S., Resti, A.: The optimal structure of PD buckets. J. Bank. & Financ. 32(10), 2275–2286 (2008)

    Google Scholar 

  56. Leippold, M., Schärer, S.: Discrete-time option pricing with stochastic liquidity. J. Bank. & Financ. 75, 1–16 (2017)

    Google Scholar 

  57. Jones, S., Johnstone, D., Wilson, R.: An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes. J. Bank. & Financ. 56, 72–85 (2015)

    Google Scholar 

  58. Chen, J., Jiang, G.J., Yuan, C., Zhu, D.: Breaking VIX at open: evidence of uncertainty creation and resolution. J. Bank. & Financ. 124, 106060 (2021)

    Google Scholar 

  59. Sarantis, N.: On the short-term predictability of exchange rates: a BVAR time-varying parameters approach. J. Bank. & Financ. 30(8), 2257–2279 (2005)

    Google Scholar 

  60. Khandani, A.E., Kim, A.J., Lo, A.W.: Consumer credit-risk models via machine-learning algorithms. J. Bank. & Financ. 34(11), 2767–2787 (2010)

    Google Scholar 

  61. Fricke, D., Roukny, T.: Generalists and specialists in the credit market. J. Bank. & Financ. 112, 105335 (2020)

    Google Scholar 

  62. Chiarella, C., Ladley, D.: Chasing trends at the micro-level: the effect of technical trading on order book dynamics. J. Bank. & Financ. 72(Supplement), S119–S131 (2016)

    Google Scholar 

  63. Baule, R., Shkel, D.: Model risk and model choice in the case of barrier options and bonus certificates. J. Bank. & Financ. 133, 106307 (2021)

    Google Scholar 

  64. Packham, N., Woebbeking, C.F.: A factor-model approach for correlation scenarios and correlation stress testing. J. Bank. & Financ. 101, 92–103 (2019)

    Google Scholar 

  65. Chen, X.H., Maringer, D.: Detecting time-variation in corporate bond index returns: a smooth transition regression model. J. Bank. & Financ. 35(1), 95–103 (2011)

    Google Scholar 

  66. Branger, N., Herold, M., Muck, M.: International stochastic discount factors and covariance risk. J. Bank. & Financ. 123, 106018 (2021)

    Google Scholar 

  67. Dbouk, W., Jamali, I.: Predicting daily oil prices: linear and non-linear models. Res. Int. Bus. Financ. 46, 149–165 (2018)

    Google Scholar 

  68. Aslam, F., Hunjra, A.I., Ftiti, Z., Louhichi, W., Shams, T.: Insurance fraud detection: evidence from artificial intelligence and machine learning. Res. Int. Bus. Financ. 62, 101744 (2022)

    Google Scholar 

  69. Kyriazis, N., Papadamou, S., Corbet, S.: A systematic review of the bubble dynamics of cryptocurrency prices. Res. Int. Bus. Financ. 54, 101254 (2020)

    Google Scholar 

  70. Chu, J., Chan, S., Zhang, Y.: High frequency momentum trading with cryptocurrencies. Res. Int. Bus. Financ. 52, 101176 (2020)

    Google Scholar 

  71. Li, W., Mei, F.: Asset returns in deep learning methods: an empirical analysis on SSE 50 and CSI 300. Res. Int. Bus. Financ. 54, 101291 (2020)

    Google Scholar 

  72. Chen, S.-H., Chang, C.-L., Tseng, Y.-H.: Social networks, social interaction and macroeconomic dynamics: how much could Ernst Ising help DSGE?. Res. Int. Bus. Financ. 30(2014), 312–335 (2012)

    Google Scholar 

  73. Manahov, V., Urquhart, A.: The efficiency of Bitcoin: a strongly typed genetic programming approach to smart electronic Bitcoin markets. Int. Rev. Financ. Anal. 73, 101629 (2021)

    Google Scholar 

  74. Zhang, Y.-J., Lin, J.-J.: Can the VAR model outperform MRS model for asset allocation in commodity market under different risk preferences of investors?. Int. Rev. Financ. Anal. 66, 101395 (2019)

    Google Scholar 

  75. El Shazly, M.R., El Shazly, H.E.: Forecasting currency prices using a genetically evolved neural network architecture. Int. Rev. Financ. Anal. 8(1), 67–82 (1999)

    Google Scholar 

  76. Kampouridis, M., Chen, S.-H., Tsang, E.: Market fraction hypothesis: a proposed test. Int. Rev. Financ. Anal. 23, 41–54 (2012)

    Google Scholar 

  77. He, X.-Z., Li, Y., Zheng, M.: Heterogeneous agent models in financial markets: a nonlinear dynamics approach. Int. Rev. Financ. Anal. 62, 135–149 (2019)

    Google Scholar 

  78. Zhang, X., Zhang, Q., Chen, D., Gu, J.: Financial integration, investor protection and imbalanced optimistically biased information timeliness in emerging markets. Int. Rev. Financ. Anal. 64, 38–56 (2019)

    Google Scholar 

  79. Manahov, V.: A note on the relationship between high-frequency trading and latency arbitrage. Int. Rev. Financ. Anal. 47, 281–296 (2016)

    Google Scholar 

  80. Diaz, E.M., Perez-Quiros, G.: GEA tracker: a daily indicator of global economic activity. J. Int. Money Financ. 115, 102400 (2021)

    Google Scholar 

  81. Lim, G.C., McNelis, P.D.: Central bank learning, terms of trade shocks and currency risk: should only inflation matter for monetary policy?. J. Int. Money Financ. 26(6), 865–886 (2007)

    Google Scholar 

  82. Marey, P.S.: Exchange rate expectations: controlled experiments with artificial traders. J. Int. Money Financ. 23(2), 283–304 (2004)

    Google Scholar 

  83. Neely, C.J., Weller, P.A.: Intraday technical trading in the foreign exchange market. J. Int. Money Financ. 22(2), 223–237 (2003)

    Google Scholar 

  84. Lai, J.T., McNelis, P.D., Yan, I.K.M.: Regional capital mobility in China: economic reform with limited financial integration. J. Int. Money Financ. 37, 493–503 (2013)

    Google Scholar 

  85. Neely, C.J., Weller, P.A.: Technical trading rules in the European monetary system. J. Int. Money Financ. 18(3), 429–458 (1999)

    Google Scholar 

  86. De Grauwe, P., Markiewicz, A.: Learning to forecast the exchange rate: two competing approaches. J. Int. Money Financ. 32, 42–76 (2013)

    Google Scholar 

  87. Neely, C.J., Weller, P.A.: Technical analysis and central bank intervention. J. Int. Money Financ. 20(7), 949–970 (2001)

    Google Scholar 

  88. Sant’Anna, L.R., Righi, M.B., Müller, F.M., Guedes, P.C.: Risk measure index tracking model. Int. Rev. Econ. & Financ. 80, 361–383 (2022)

    Google Scholar 

  89. Fernandez-Perez, A., Fernández-Rodríguez, F., Sosvilla-Rivero, S.: The term structure of interest rates as predictor of stock returns: evidence for the IBEX 35 during a bear market. Int. Rev. Econ. & Financ. 31, 21–33 (2013)

    Google Scholar 

  90. Neely, C.J.: Risk-adjusted, ex ante, optimal technical trading rules in equity markets. Int. Rev. Econ. & Financ. 12(1), 69–87 (2003)

    Google Scholar 

  91. Wanke, P., Azad, M.A.K., Emrouznejad, A., Antunes, J.: A dynamic network DEA model for accounting and financial indicators: a case of efficiency in MENA banking. Int. Rev. Econ. & Financ. 61, 52–68 (2019)

    Google Scholar 

  92. Heath, A., Kelly, G., Manning, M., Markose, S., Shaghaghi, A.R.: CCPs and network stability in OTC derivatives markets. J. Financ. Stab. 27, 217–233 (2016)

    Google Scholar 

  93. Li, Chien-Kuo., Liang, Deron, Lin, Fengyi, Chen, Kwo-Liang.: The application of corporate governance indicators with XBRL technology to financial crisis prediction. Emerg. Mark. Financ. Trade. 51(sup1), S58–S72 (2015)

    Google Scholar 

  94. Xu, X., Wang, C., Li, J., Shi, C.: Green transportation and information uncertainty in gasoline distribution: evidence from China. Emerg. Mark. Financ. Trade. 57(11), 3101–3119 (2021)

    Google Scholar 

  95. Wang, T., Zhao, S., Wang, W., Yang, H.: How does exogenous shock change the structure of interbank network?: evidence from China under COVID-19. Emerg. Mark. Financ. Trade (2022)

    Google Scholar 

  96. Zhao, W., Lu, Y., Zhao, M., Zhang, P.: Fluctuations in the open economy of China: evidence from the ABNK model. Emerg. Mark. Financ. Trade. 56(9), 2073–2092 (2020)

    Google Scholar 

  97. Tirapat, S., Visaltanachoti, N.: Opportunistic insider trading. Pac. Basin Financ. J. 21(1), 1046–1061 (2012). ISSN 0927-538X

    Google Scholar 

  98. Wang, L., Su, Z.-Q., Fung, H.-G., Jin, H.-M., Xiao, Z.: Do CEOs with academic experience add value to firms? Evidence on bank loans from Chinese firms. Pac. Basin Financ. J. 67, 101534 (2021)

    Google Scholar 

  99. Abraham, R., Samad, M.E., Bakhach, A.M., El-Chaarani, H., Sardouk, A., Nemar, S.E., Jaber, D.: Forecasting a stock trend using genetic algorithm and random forest. J. Risk Financ. Manag. 15(5), 188 (2022)

    Google Scholar 

  100. Małecka-Ziembińska, E., Ziembiński, R.: Application of genetic algorithm to optimal income taxation. J. Risk Financ. Manag. 13(11), 251 (2020)

    Google Scholar 

  101. Pisula, T.: An ensemble classifier-based scoring model for predicting bankruptcy of polish companies in the Podkarpackie Voivodeship. J. Risk Financ. Manag. 13(2), 37 (2020)

    Google Scholar 

  102. Mba, J.C., Mai, M.M.: A particle swarm optimization copula-based approach with application to cryptocurrency portfolio optimisation. J. Risk Financ. Manag. 15(7), 285 (2022)

    Google Scholar 

  103. Nazário, R.T.F., e Silva, L.J., Sobreiro, V.A., Kimura, H.: A literature review of technical analysis on stock markets. Q. Rev. Econ. Financ. 66, 115–126 (2017)

    Google Scholar 

  104. Mansourfar, G., Mohamad, S., Hassan, T.: The behavior of MENA oil and non-oil producing countries in international portfolio optimization. Q. Rev. Econ. Financ. 50(4), 415–423 (2010)

    Google Scholar 

  105. Jiang, M., Liu, J., Zhang, L.: An extended regularized Kalman filter based on Genetic Algorithm: Application to dynamic asset pricing models. Q. Rev. Econ. Financ. 79, 28–44 (2021)

    Google Scholar 

  106. Zhuo, J., Li, X., Yu, C.: Parameter behavioral finance model of investor groups based on statistical approaches. Q. Rev. Econ. Financ. 80, 74–79 (2021)

    Google Scholar 

  107. Manahov, V., Hudson, R., Gebka, B.: Does high frequency trading affect technical analysis and market efficiency? And if so, how?. J. Int. Financ. Mark. Inst. Money. 28(2014), 131–157 (2013)

    Google Scholar 

  108. Sermpinis, G., Stasinakis, C., Dunis, C.: Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects. J. Int. Financ. Mark. Inst. Money. 30, 21–54 (2014)

    Google Scholar 

  109. Manahov, V., Hudson, R., Hoque, H.: Return predictability and the ‘wisdom of crowds’: Genetic Programming trading algorithms, the Marginal Trader Hypothesis and the Hayek Hypothesis. J. Int. Financ. Mark. Inst. Money. 37, 85–98 (2015)

    Google Scholar 

  110. Manahov, V., Hudson, R., Linsley, P.: New evidence about the profitability of small and large stocks and the role of volume obtained using Strongly Typed Genetic Programming. J. Int. Financ. Mark. Inst. Money. 33, 299–316 (2014)

    Google Scholar 

  111. Neely, C.J.: Forecasting foreign exchange volatility: Why is implied volatility biased and inefficient? And does it matter?. J. Int. Financ. Mark. Inst. Money 19(1), 188–205 (2009)

    Google Scholar 

  112. Polyzos, S., Samitas, A., Kampouris, I.: Economic stimulus through bank regulation: Government responses to the COVID-19 crisis. J. Int. Financ. Mark. Inst. Money. 75, 101444 (2021)

    Google Scholar 

  113. Aziz, S., Dowling, M., Hammami, H., Piepenbrink, A.: Machine learning in finance: a topic modeling approach. Eur. Financ. Manag. 28, 744–770 (2022)

    Article  Google Scholar 

  114. Bedendo, M., Cathcart, L., El-Jahel, L.: Market and model credit default swap spreads: mind the gap!. Eur. Financ. Manag. 17, 655–678 (2011)

    Google Scholar 

  115. Franco, M., Vivo, J.-M.: Genetic algorithms for parameter estimation in modelling of index returns. Eur. J. Financ. 24(13), 1088–1099 (2018)

    Google Scholar 

  116. Dunis, C.L., Laws, J., Karathanasopoulos, A.: GP algorithm versus hybrid and mixed neural networks. Eur. J. Financ. 19(3), 180–205 (2013)

    Google Scholar 

  117. Drenovak, M., Ranković, V., Urošević, B., Jelic, R.: Bond portfolio management under Solvency II regulation. Eur. J. Financ. 27(9), 857–879 (2021)

    Google Scholar 

  118. Geraskin, P., Fantazzini, D.: Everything you always wanted to know about log-periodic power laws for bubble modeling but were afraid to ask. Eur. J. Financ. 19(5), 366–391 (2013)

    Google Scholar 

  119. Zhang, J., Wen, J., Chen, J.: Modeling market fluctuations under investor sentiment with a Hawkes-Contact process. Eur. J. Financ. (2021)

    Google Scholar 

  120. Gao, X., Ladley, D.: Noise trading and market stability. Eur. J. Financ. (2021)

    Google Scholar 

  121. Dunis, C.L., Laws, J., Middleton, P.W., Karathanasopoulos, A.: Trading and hedging the corn/ethanol crush spread using time-varying leverage and nonlinear models. Eur. J. Financ. 21(4), 352–375 (2015)

    Article  Google Scholar 

  122. Sermpinis, G., Laws, J., Dunis, C.L.: Modelling commodity value at risk with Psi Sigma neural networks using open-high-low-close data. Eur. J. Financ. 21(4), 316–336 (2015)

    Article  Google Scholar 

  123. Luo, J., Chen, L.: Volatility dependences of stock markets with structural breaks. Eur. J. Financ. 24(17), 1727–1753 (2018)

    Google Scholar 

  124. Carr, P., Torricelli, L.: Additive logistic processes in option pricing. Financ. Stoch. 25, 689–724 (2021)

    Google Scholar 

  125. Manahov, V.: Front-running scalping strategies and market manipulation: why does high-frequency trading need stricter regulation?. Financ. Rev. 51, 363–402 (2016)

    Google Scholar 

  126. Shapiro, A.F.: A Hitchhiker’s guide to the techniques of adaptive nonlinear models. Insur. Math. Econ. 26(2–3), 119–132 (2000)

    Google Scholar 

  127. Gupta, P., Mittal, G., Mehlawat, M.K.: Expected value multiobjective portfolio rebalancing model with fuzzy parameters. Insur. Math. Econ. 52(2), 190–203 (2013)

    Google Scholar 

  128. Shapiro, A,F.: Fuzzy logic in insurance. Insur. Math. Econ. 35(2), 399–424 (2004)

    Google Scholar 

  129. Liu, Y.-J., Zhang, W.-G.: Fuzzy portfolio optimization model under real constraints. Insur. Math. Econ. 53(3), 704–711 (2013)

    Google Scholar 

  130. Shapiro, A.F., Gorman, R.P.: Implementing adaptive nonlinear models. Insur. Math. Econ. 26(2–3), 289–307 (2000)

    Google Scholar 

  131. Huang, X., Zhao, T.: Mean-chance model for portfolio selection based on uncertain measure. Insur. Math. Econ. 59, 243–250 (2014)

    Google Scholar 

  132. Graf, S., Kling, A., Ruß, J.: Risk analysis and valuation of life insurance contracts: Combining actuarial and financial approaches. Insur. Math. Econ. 49(1), 115–125 (2011)

    Google Scholar 

  133. Shapiro, A.F.: The merging of neural networks, fuzzy logic, and genetic algorithms. Insur. Math. Econ. 31(1), 115–131 (2002)

    Google Scholar 

  134. Jevtić, P., Luciano, E., Vigna, E.: Mortality surface by means of continuous time cohort models. Insur. Math. Econ. 53(1), 122–133 (2013)

    Google Scholar 

  135. Zemp, A.: Risk comparison of different bonus distribution approaches in participating life insurance. Insur. Math. Econ. 49(2), 249–264 (2011)

    Google Scholar 

  136. Blostein, M., Miljkovic, T.: On modeling left-truncated loss data using mixtures of distributions. Insur. Math. Econ. 85, 35–46 (2019)

    Google Scholar 

  137. Jevtić, P., Regis, L.: A continuous-time stochastic model for the mortality surface of multiple populations. Insur. Math. Econ. 88, 181–195 (2019)

    Google Scholar 

  138. Zhou, H., Zhou, K.Q., Li, X.: Stochastic mortality dynamics driven by mixed fractional Brownian motion. Insur. Math. Econ. 106, 218–238 (2022)

    Google Scholar 

  139. Beer, S., Braun, A., Marugg, A.: Pricing industry loss warranties in a Lévy-Frailty framework. Insur. Math. Econ. 89, 171–181 (2019)

    Google Scholar 

  140. Manahov, V., Hudson, R., Urquhart, A.: High-frequency trading from an evolutionary perspective: financial markets as adaptive systems. Int. J. Financ. Econ. 2019(24), 943–962 (2019)

    Article  Google Scholar 

  141. Loukeris, N., Eleftheriadis, I.: Further Higher Moments in Portfolio Selection and A Priori Detection of Bankruptcy, Under Multi-layer Perceptron Neural Networks, Hybrid Neuro-genetic MLPs, and the Voted Perceptron. Int. J. Financ. Econ. 20, 341–361 (2015). https://doi.org/10.1002/ijfe.1521

  142. Moradi, M., Jabbari Nooghabi, M., Rounaghi, M.M.: Investigation of fractal market hypothesis and forecasting time series stock returns for Tehran Stock Exchange and London Stock Exchange. Int. J. Financ. Econ. 26, 662–678 (2021)

    Google Scholar 

  143. Mahmoudi, A., Hashemi, L., Jasemi, M., Pope, J.: A comparison on particle swarm optimization and genetic algorithm performances in deriving the efficient frontier of stocks portfolios based on a mean-lower partial moment model. Int. J. Financ. Econ. 26, 5659–5665 (2021)

    Google Scholar 

  144. Yang, X., Zhang, C., Yang, Y., Wang, W., Wagan, Z.A.: A new risk measurement method for China’s carbon market. Int. J. Financ. Econ. 27, 1280–1290 (2022)

    Google Scholar 

  145. Xu, D., Zhang, X., Feng, H.: Generalized fuzzy soft sets theory-based novel hybrid ensemble credit scoring model. Int. J. Financ. Econ. 24, 903–921 (2019)

    Google Scholar 

  146. Arreola Hernandez, J., Kang, S.H., Yoon, S.-M.: Interdependence and portfolio optimisation of bank equity returns from developed and emerging Europe. Int. J. Financ. Econ. 27, 678–696 (2022)

    Google Scholar 

  147. Benink, H.A., Gordillo, J.L., Pardo, J.P., Stephens, C.R.: Market efficiency and learning in an artificial stock market: a perspective from Neo-Austrian economics. J. Empir. Financ. 17(4), 668–688 (2010)

    Google Scholar 

  148. Ji, J., Wang, D., Xu, D., Xu, C.: Combining a self-exciting point process with the truncated generalized Pareto distribution: an extreme risk analysis under price limits. J. Empir. Financ. 57, 52–70 (2020)

    Google Scholar 

  149. Visaltanachoti, N., Charoenwong, C., Ding, D.K.: Information asymmetry in warrants and their underlying stocks on the stock exchange of Thailand. J. Empir. Financ. 18(3), 474–487 (2011)

    Google Scholar 

  150. Ballocchi, G., Dacorogna, M.M., Hopman, C.M., Müller, U.A., Olsen, R.B.: The intraday multivariate structure of the Eurofutures markets. J. Empir. Financ. 6(5), 479–513 (1999)

    Google Scholar 

  151. Bauwens, L., De Backer, B., Dufays, A.: A Bayesian method of change-point estimation with recurrent regimes: application to GARCH models. J. Empir. Financ. 29, 207–229 (2014)

    Google Scholar 

  152. Díaz-Hernández, A., Constantinou, N.: A multiple regime extension to the Heston-Nandi GARCH (1, 1) model. J. Empir. Financ. 53, 162–180 (2019)

    Google Scholar 

  153. Kuester, K., Mittnik, S., Paolella, M.S.: Value-at-risk prediction: a comparison of alternative strategies, J. Financ. Econ. 4(1), Winter, 53–89 (2006)

    Google Scholar 

  154. Gerlach, R., Wang, C.: Bayesian semi-parametric realized conditional autoregressive expectile models for tail risk forecasting. J. Financ. Econ. 20(1), Winter, 105–138 (2022)

    Google Scholar 

  155. Kozhan, R., Salmon, M.: The information content of a limit order book: the case of an FX market. J. Financ. Mark. 15(1), 1–28 (2012)

    Google Scholar 

  156. Arnold, T., Hilliard, J.E., Schwartz, A.: Short-maturity options and jump memory. J. Financ. Res. 30, 437–454 (2007)

    Article  Google Scholar 

  157. Herron, R.: How Much Does Your Banker’s Target-Specific Experience Matter? Evidence from Target IPO Underwriters that Advise Acquirers. J. Financ. Serv. Res. 61, 217–258 (2022)

    Article  Google Scholar 

  158. Liu, Q., Luo, Q., Tse, Y., Xie, Y.: The market quality of commodity futures markets. J. Futur. Mark. 40, 1751–1766 (2020)

    Google Scholar 

  159. Posselt, A.M.: Dynamics in the VIX complex. J. Futur. Mark. 42, 1665–1687 (2022)

    Article  Google Scholar 

  160. Roberts, M.C.: Technical analysis and genetic programming: constructing and testing a commodity portfolio. J. Futur. Mark. 25, 643–660 (2005)

    Article  Google Scholar 

  161. Wang, J.: Trading and hedging in S &P 500 spot and futures markets using genetic programming. J. Futur. Mark. 20, 911–942 (2000)

    Article  Google Scholar 

  162. Mamre, M.O., Sommervoll, D.E.: Coming of Age: Renovation Premiums in Housing Markets. J. R. Estate Financ. Econ. (2022)

    Google Scholar 

  163. Porth, L., Pai, J., Boyd, M.: A Portfolio Optimization Approach Using Combinatorics With a Genetic Algorithm for Developing a Reinsurance Model. J. Risk Insur. 82, 687–713 (2015)

    Article  Google Scholar 

  164. Owadally, I., Haberman, S., Gómez Hernández, D.: A Savings Plan With Targeted Contributions. J. Risk Insur. 80, 975–1000 (2013)

    Article  Google Scholar 

  165. Gatzert, N., Kellner, R.: The Effectiveness of Gap Insurance With Respect to Basis Risk in a Shareholder Value Maximization Setting. J. Risk Insur. 81, 831–860 (2014)

    Article  Google Scholar 

  166. Cont, R.: Model uncertainty and its impact on the pricing of derivative instruments. Math. Financ. 16, 519–547 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  167. Karathanasopoulos, A., Dunis, C., Khalil, S.: Modelling, forecasting and trading with a new sliding window approach: the crack spread example. Quant. Financ. 16(12), 1875–1886 (2016)

    Google Scholar 

  168. Brabazon, A.: The new ‘brew’ on the Liffey: How fmc2 is adding the yeast. Quant. Financ. 10(3), 241–245 (2010)

    Article  Google Scholar 

  169. LeBaron, B.: A builder’s guide to agent-based financial markets. Quant. Financ. 1(2), 254–261 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  170. Horvath, B., Muguruza, A., Tomas, M.: Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models. Quant. Financ. 21(1), 11–27 (2021)

    Google Scholar 

  171. Payne, B.C., Tresl, J.: Hedge fund replication with a genetic algorithm: breeding a usable mousetrap. Quant. Financ. 15(10), 1705–1726 (2015)

    Google Scholar 

  172. Wang, C., Chen, Q., Gerlach, R.: Bayesian realized-GARCH models for financial tail risk forecasting incorporating the two-sided Weibull distribution. Quant. Financ. 19(6), 1017–1042 (2019)

    Google Scholar 

  173. Cheng, C.-H., Wang, S.-H.: A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress. Quant. Financ. 15(12), 1979–1994 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  174. Oesch, C., Maringer, D.: Low-latency liquidity inefficiency strategies. Quant. Financ. 17(5), 717–727 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  175. Tsao, C.-Y.: Portfolio selection based on the mean-VaR efficient frontier. Quant. Financ. 10(8), 931–945 (2010)

    Google Scholar 

  176. Hendricks, D., Gebbie, T., Wilcox, D.: Detecting intraday financial market states using temporal clustering. Quant. Financ. 16(11), 1657–1678 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  177. Lien, D., Tse,Y.K., Zhang, X.: Structural change and lead-lag relationship between the Nikkei spot index and futures price: a genetic programming approach. Quant. Financ. 3(2), 136–144 (2003)

    Google Scholar 

  178. Tafin Djoko, D., Tillé, Y.: Selection of balanced portfolios to track the main properties of a large market. Quantitative Finance 15(2), 359–370 (2015)

    Google Scholar 

  179. Wang, D., Tu, J., Chang, X., Li, S.: The lead-lag relationship between the spot and futures markets in China. Quant. Financ. 17(9), 1447–1456 (2017)

    Google Scholar 

  180. Sariev, E., Germano, G.: Bayesian regularized artificial neural networks for the estimation of the probability of default. Quant. Financ. 20(2), 311–328 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  181. Acosta-González, E., Armas-Herrera, R., Fernández-Rodríguez, F.: On the index tracking and the statistical arbitrage choosing the stocks by means of cointegration: the role of stock picking. Quant. Financ. 15(6), 1075–1091 (2015)

    Google Scholar 

  182. Ma, F., Liang, C., Zeng, Q., Li, H.: Jumps and oil futures volatility forecasting: a new insight. Quant. Financ. 21(5), 853–863 (2021)

    Google Scholar 

  183. Creamer, G., Freund, Y.: Automated trading with boosting and expert weighting. Quant. Financ. 10(4), 401–420 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  184. Creamer, G.: Model calibration and automated trading agent for euro futures. Quant. Financ. 12(4), 531–545 (2012)

    Article  MathSciNet  Google Scholar 

  185. Zumbach, G.: Volatility conditional on price trends. Quant. Financ. 10(4), 431–442 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  186. Funahashi, H.: Artificial neural network for option pricing with and without asymptotic correction. Quant. Financ. 21(4), 575–592 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  187. Kim, H., Jun, S., Moon, K.-S.: Stock market prediction based on adaptive training algorithm in machine learning. Quant. Financ. 22(6), 1133–1152 (2022)

    Google Scholar 

  188. How, J., Ling, M., Verhoeven, P.: Does size matter? a genetic programming approach to technical trading. Quant. Financ. 10(2), 131–140 (2010)

    Google Scholar 

  189. Fang, J., Lin, J., Xia, S., Xia, Z., Hu, S., Liu, X., Yong, J.: Neural network-based automatic factor construction. Quant. Financ. 20(12), 2101–2114 (2020)

    Google Scholar 

  190. Ji, J.R., Wang, D., Tu, J.Q.: Modifying a simple agent-based model to disentangle the microstructure of Chinese and us stock markets. Quant. Financ. 18(12), 2067–2083 (2018)

    Google Scholar 

  191. Yaros, J.R., Imieliński, T.: Data-driven methods for equity similarity prediction. Quant. Financ. 15(10), 1657–1681 (2015)

    Google Scholar 

  192. Shu, L., Shi, F., Tian, G.: High-dimensional index tracking based on the adaptive elastic net. Quant. Financ. 20(9), 1513–1530 (2020)

    Google Scholar 

  193. Pfeuffer, M., Möstel, L., Fischer, M.: An extended likelihood framework for modelling discretely observed credit rating transitions. Quant. Financ. 19(1), 93–104 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  194. Dempster, M.A.H., Jones, C.M.: A real-time adaptive trading system using genetic programming. Quant. Financ. 1(4), 397–413 (2001)

    Article  MATH  Google Scholar 

  195. Austin, M.P., Bates, G., Dempster, M.A.H., Leemans, V., Williams, S.N.: Adaptive systems for foreign exchange trading. Quant. Financ. 4(4), 37–45 (2004)

    Google Scholar 

  196. Azzone, M., Baviera, R.: Additive normal tempered stable processes for equity derivatives and power-law scaling. Quant. Financ. 22(3), 501–518 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  197. Siikanen, M., Nögel, U., Kanniainen, J.: Trading too expensively in the FX market?. Quant. Financ. 19(12), 1933–1944 (2019)

    Google Scholar 

  198. Chen, M.-Y.: Using a hybrid evolution approach to forecast financial failures for Taiwan-listed companies. Quant. Financ. 14(6), 1047–1058 (2014)

    Google Scholar 

  199. Grishina, N., Lucas, C.A., Date, P.: Prospect theory-based portfolio optimization: an empirical study and analysis using intelligent algorithms. Quant. Financ. 17(3), 353–367 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  200. Murphy, N.J., Gebbie, T.J.: Learning the dynamics of technical trading strategies. Quant. Financ. 21(8), 1325–1349 (2021)

    Article  MathSciNet  Google Scholar 

  201. Meade, N., Beasley, J.E.: Detection of momentum effects using an index out-performance strategy. Quant. Financ. 11(2), 313–326 (2011)

    Article  MathSciNet  Google Scholar 

  202. Jobst, N.J., Horniman, M.D., Lucas, C.A., Mitra, G.: Computational aspects of alternative portfolio selection models in the presence of discrete asset choice constraints. Quant. Financ. 1(5), 489–501 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  203. Gibson Brandon, R., Gyger, S.: Optimal hedge fund portfolios under liquidation risk. Quant. Financ. 11(1), 53–67 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  204. Oeuvray, R., Junod, P.: A practical approach to semideviation and its time scaling in a jump-diffusion process. Quant. Financ. 15(5), 809–827 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  205. Baule, R., Entrop, O., Wessels, S.: Performance measurement for option portfolios in a stochastic volatility framework. Quant. Financ. 22(3), 519–539 (2022)

    Google Scholar 

  206. Rebonato, R.: Probably approximately correct. Quant. Financ. 16(3), 349–353 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  207. Luss, R., D’Aspremont, A.: Predicting abnormal returns from news using text classification. Quant. Financ. 15(6), 999–1012 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  208. Yamamoto, R.: Trading profitability from learning and adaptation on the Tokyo Stock Exchange. Quant. Financ. 16(6), 969–996 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  209. Zhang, S.M., Feng, Y.: American option pricing under the double Heston model based on asymptotic expansion. Quant. Financ. 19(2), 211–226 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  210. Kim, S., Kim, S.: Index tracking through deep latent representation learning. Quant. Financ. 20(4), 639–652 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  211. Satpathy, T., Shah, R.: Sparse index tracking using sequential Monte Carlo. Quant. Financ. 22(9), 1579–1592 (2022)

    Article  MATH  Google Scholar 

  212. Li, W., Paraschiv, F., Sermpinis, G.: A data-driven explainable case-based reasoning approach for financial risk detection. Quant. Financ. (2022)

    Google Scholar 

  213. Lu, X., Abergel, F.: High-dimensional Hawkes processes for limit order books: modelling, empirical analysis and numerical calibration. Quant. Financ. 18(2), 249–264 (2018)

    Google Scholar 

  214. Zhao, Z., Xu, F., Du, D., Meihua, W.: Robust portfolio rebalancing with cardinality and diversification constraints. Quant. Financ. 21(10), 1707–1721 (2021)

    Google Scholar 

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  1. Natural Computing Research & Applications Group, School of Business, University College Dublin, Dublin, Ireland

    Michael O’Neill & Anthony Brabazon

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    Wolfgang Banzhaf

  2. Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  3. School of Engineering and Computer Science and Centre for Data Science and Artificial Intelligence, Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

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O’Neill, M., Brabazon, A. (2024). Evolutionary Machine Learning in Finance. In: Banzhaf, W., Machado, P., Zhang, M. (eds) Handbook of Evolutionary Machine Learning. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-3814-8_24

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