4,094
Views
32
CrossRef citations to date
0
Altmetric
Invited Review

Computational approaches and data analytics in financial services: A literature review

ORCID Icon, , &
Pages 1581-1599 | Received 21 Nov 2018, Accepted 10 Mar 2019, Published online: 28 May 2019

References

  • Abbasi, A., Albrecht, C., Vance, A., & Hansen, J. (2012). MetaFraud: A meta-learning framework for detecting financial fraud. MIS Quarterly, 36(4), 1293–1327.
  • Abbaszadeh, S., Nguyen, T.-D., & Wu, Y. (2018). Optimal trading under non-negativity constraints using approximate dynamic programming. Journal of the Operational Research Society, 69(9), 1406–1422. doi: 10.1080/01605682.2017.1398201
  • Abellán, J., & Castellano, J. G. (2017). A comparative study on base classifiers in ensemble methods for credit scoring. Expert Systems with Applications, 73, 1–10. doi: 10.1016/j.eswa.2016.12.020
  • Afful-Dadzie, E., & Afful-Dadzie, A. (2016). A decision-making model for selecting start-up businesses in a government venture capital scheme. Management Decision, 54(3), 714–734. doi: 10.1108/MD-06-2015-0226
  • Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94, 164–184. doi: 10.1016/j.eswa.2017.10.040
  • Allevi, E., Basso, A., Bonenti, F., Oggioni, G., & Riccardi, R. (in press). Measuring the environmental performance of green SRI funds: A DEA approach. Energy Economics. doi: 10.1016/j.eneco.2018.07.023
  • Almahdi, S., & Yang, S. Y. (2017). An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown. Expert Systems with Applications, 87, 267–279. doi: 10.1016/j.eswa.2017.06.023
  • Andriosopoulos, K., Doumpos, M., Papapostolou, N. C., & Pouliasis, P. K. (2013). Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms. Transportation Research Part E: Logistics and Transportation Review, 52, 16–34. doi: 10.1016/j.tre.2012.11.006
  • Andriosopoulos, K., & Nomikos, N. (2014). Performance replication of the spot energy index with optimal equity portfolio selection: Evidence from the UK, US and Brazilian markets. European Journal of Operational Research, 234(2), 571–582.
  • Ang, K., & Quek, C. (2006). Stock trading using RSPOP: A novel rough set-based neuro-fuzzy approach. IEEE Transactions on Neural Networks, 17(5), 1301–1315. doi: 10.1109/TNN.2006.875996
  • Angelelli, E., Mansini, R., & Speranza, M. G. (2008). A comparison of MAD and CVaR models with real features. Journal of Banking & Finance, 32(7), 1188–1197. doi: 10.1016/j.jbankfin.2006.07.015
  • Angilella, S., & Mazzù, S. (in press). A credit risk model with an automatic override for innovative small and medium-sized enterprises, journal of the operational research society. Journal of the Operational Research Society. doi: 10.1080/01605682.2017.1411313
  • Aouni, B., Colapinto, C., & Torre, D. L. (2014). A fuzzy goal programming model for venture capital investment decision-making. INFOR: Information Systems and Operational Research, 52(3), 138–146. doi: 10.3138/infor.52.3.138
  • Aouni, B., Doumpos, M., Pérez-Gladish, B., & Steuer, R. E. (2018). On the increasing importance of multiple criteria decision aid methods for portfolio selection. Journal of the Operational Research Society, 69(10), 1525–1542. doi: 10.1080/01605682.2018.1475118
  • Aquaro, V., Bardoscia, M., Bellotti, R., Consiglio, A., Carlo, F. D., & Ferri, G. (2010). A Bayesian networks approach to operational risk. Physica A: Statistical Mechanics and Its Applications, 389(8), 1721–1728. doi: 10.1016/j.physa.2009.12.043
  • Asimit, A. V., Badescu, A. M., Siu, T. K., & Zinchenko, Y. (2015). Capital requirements and optimal investment with solvency probability constraints. IMA Journal of Management Mathematics, 26(4), 345–375. doi: 10.1093/imaman/dpt029
  • Azar, A., & Dolatabad, K. M. (2019). A method for modelling operational risk with fuzzy cognitive maps and bayesian belief networks. Expert Systems with Applications, 115, 607–617. doi: 10.1016/j.eswa.2018.08.043
  • Babalos, V., Philippas, N., Doumpos, M., & Zopounidis, C. (2012). Mutual funds performance appraisal using stochastic multicriteria acceptability analysis. Applied Mathematics and Computation, 218(9), 5693–5703. doi: 10.1016/j.amc.2011.11.066
  • Babat, O., Vera, J. C., & Zuluaga, L. F. (2018). Computing near-optimal value-at-risk portfolios using integer programming techniques. European Journal of Operational Research, 266(1), 304–315. doi: 10.1016/j.ejor.2017.09.009
  • Baena-Mirabete, S., & Puig, P. (2017). Parsimonious higher order Markov models for rating transitions. Journal of the Royal Statistical Society: Series A (Statistics in Society), 181(1), 107–131. doi: 10.1111/rssa.12267
  • Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003). Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science, 49(3), 312–329. doi: 10.1287/mnsc.49.3.312.12739
  • Baesens, B., & Van Gestel, T. (2009). Credit risk management: Basic concepts: Financial risk components, rating analysis, models, economic and regulatory capital. Oxford: Oxford University Press.
  • Balibek, E., & Köksalan, M. (2010). A multi-objective multi-period stochastic programming model for public debt management. European Journal of Operational Research, 205(1), 205–217. doi: 10.1016/j.ejor.2009.12.001
  • Balla, V., Gaganis, C., Pasiouras, F., & Zopounidis, C. (2014). Multicriteria decision aid models for the prediction of securities class actions: evidence from the banking sector. OR Spectrum, 36(1), 57–72. doi: 10.1007/s00291-013-0333-8
  • Ballestero, E., Bravo, M., Pérez-Gladish, B., Arenas-Parra, M., & Plà-Santamaria, D. (2012). Socially responsible investment: A multicriteria approach to portfolio selection combining ethical and financial objectives. European Journal of Operational Research, 216(2), 487–494. doi: 10.1016/j.ejor.2011.07.011
  • Ban, G.-Y., Karoui, N. E., & Lim, A. E. B. (2018). Machine learning and portfolio optimization. Management Science, 64(3), 1136–1154. doi: 10.1287/mnsc.2016.2644
  • Bandi, C., & Bertsimas, D. (2014). Robust option pricing. European Journal of Operational Research, 239(3), 842–853. doi: 10.1016/j.ejor.2014.06.002
  • Başoğlu, İ., Hörmann, W., & Sak, H. (2018). Efficient simulations for a Bernoulli mixture model of portfolio credit risk. Annals of Operations Research, 260(1-2), 113–128. doi: 10.1007/s10479-016-2241-1
  • Bast I, E., Kuzey, C., & Delen, D. (2015). Analyzing initial public offerings: Short-term performance using decision trees and SVMs. Decision Support Systems, 73, 15–27. doi: 10.1016/j.dss.2015.02.011
  • Bastos, J. A. (2014). Ensemble predictions of recovery rates. Journal of Financial Services Research, 46(2), 177–193. doi: 10.1007/s10693-013-0165-3
  • Bellotti, T., & Crook, J. (2009). Credit scoring with macroeconomic variables using survival analysis. Journal of the Operational Research Society, 60(12), 1699–1707. doi: 10.1057/jors.2008.130
  • Bellotti, T., & Crook, J. (2009). Support vector machines for credit scoring and discovery of significant features. Expert Systems with Applications, 36(2), 3302–3308. doi: 10.1016/j.eswa.2008.01.005
  • Bellotti, T., & Crook, J. (2014). Retail credit stress testing using a discrete hazard model with macroeconomic factors. Journal of the Operational Research Society, 65(3), 340–350. doi: 10.1057/jors.2013.91
  • Bequé, A., & Lessmann, S. (2017). Extreme learning machines for credit scoring: An empirical evaluation. Expert Systems with Applications, 86, 42–53. doi: 10.1016/j.eswa.2017.05.050
  • Bertsimas, D., & Shioda, R. (2009). Algorithm for cardinality-constrained quadratic optimization. Computational Optimization and Applications, 43(1), 1–22. doi: 10.1007/s10589-007-9126-9
  • Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. doi: 10.1287/opre.1030.0065
  • Berutich, J. M., López, F., Luna, F., & Quintana, D. (2016). Robust technical trading strategies using GP for algorithmic portfolio selection. Expert Systems with Applications, 46, 307–315. doi: 10.1016/j.eswa.2015.10.040
  • Bezerra, P. C. S., & Albuquerque, P. H. M. (2017). Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels. Computational Management Science, 14(2), 179–196. doi: 10.1007/s10287-016-0267-0
  • Bijak, K., & Thomas, L. C. (2015). Modelling LGD for unsecured retail loans using Bayesian methods. Journal of the Operational Research Society, 66(2), 342–352. doi: 10.1057/jors.2014.9
  • Bjrk, T., Murgoci, A., & Zhou, X. Y. (2012). Mean-variance portfolio optimization with state-dependent risk aversion. Mathematical Finance, 24(1), 1–24. doi: 10.1111/j.1467-9965.2011.00515.x
  • Blank, B., Lunceford, E., Morik, J., He, S., Rana, M., Rajendran, P., … Kompella, P. L. (2017). BNY Mellon optimization reduces intraday credit risk by $1.4 trillion. Interfaces, 47(1), 38–51. doi: 10.1287/inte.2017.0887
  • Bloembergen, D., Tuyls, K., Hennes, D., & Kaisers, M. (2015). Evolutionary dynamics of multi-agent learning: A survey. Journal of Artificial Intelligence Research, 53, 659–697. doi: 10.1613/jair.4818
  • Bo, L., & Capponi, A. (2016). Optimal investment in credit derivatives portfolio under contagion risk. Mathematical Finance, 26(4), 785–834. doi: 10.1111/mafi.12074
  • Bo, L., & Capponi, A. (2017). Robust optimization of credit portfolios. Mathematics of Operations Research, 42(1), 30–56. doi: 10.1287/moor.2016.0790
  • Boginski, V., Butenko, S., & Pardalos, P. M. (2005). Statistical analysis of financial networks. Computational Statistics & Data Analysis, 48(2), 431–443. doi: 10.1016/j.csda.2004.02.004
  • Bonini, S., & Caivano, G. (2016). Estimating loss-given default through advanced credibility theory. The European Journal of Finance, 22(13), 1351–1362. doi: 10.1080/1351847X.2013.870918
  • Booth, A., Gerding, E., & McGroarty, F. (2014). Automated trading with performance weighted random forests and seasonality. Expert Systems with Applications, 41(8), 3651–3661. doi: 10.1016/j.eswa.2013.12.009
  • Bordino, I., Battiston, S., Caldarelli, G., Cristelli, M., Ukkonen, A., & Weber, I. (2012). Web search queries can predict stock market volumes. PLoS One, 7(7), e40014. doi: 10.1371/journal.pone.0040014
  • Brown, D. B., & Smith, J. E. (2011). Dynamic portfolio optimization with transaction costs: Heuristics and dual bounds. Management Science, 57(10), 1752–1770. doi: 10.1287/mnsc.1110.1377
  • Calabrese, R. (2014). Downturn loss given default: Mixture distribution estimation. European Journal of Operational Research, 237(1), 271–277. doi: 10.1016/j.ejor.2014.01.043
  • Calafiore, G. C. (2008). Multi-period portfolio optimization with linear control policies. Automatica, 44(10), 2463–2473. doi: 10.1016/j.automatica.2008.02.007
  • Capotorti, A., & Barbanera, E. (2012). Credit scoring analysis using a fuzzy probabilistic rough set model. Computational Statistics & Data Analysis, 56(4), 981–994. doi: 10.1016/j.csda.2011.06.036
  • Capponi, A., & Figueroa-López, J. E. (2014). Dynamic portfolio optimization with a defaultable security and regime-switching. Mathematical Finance, 24(2), 207–249. doi: 10.1111/j.1467-9965.2012.00522.x
  • Carapuço, J., Neves, R., & Horta, N. (2018). Reinforcement learning applied to forex trading. Applied Soft Computing, 73, 783–794. doi: 10.1016/j.asoc.2018.09.017
  • Castelo Gouveia, M. D., Neves, E. D., Dias, L. C., & Antunes, C. H. (2018). Performance evaluation of Portuguese mutual fund portfolios using the value-based DEA method. Journal of the Operational Research Society, 69(10), 1628–1639. doi: 10.1057/s41274-017-0259-7
  • Çelikyurt, U., & Özekici, S. (2007). Multiperiod portfolio optimization models in stochastic markets using the mean–variance approach. European Journal of Operational Research, 179(1), 186–202. doi: 10.1016/j.ejor.2005.02.079
  • Chang, T.-J., Meade, N., Beasley, J., & Sharaiha, Y. (2000). Heuristics for cardinality constrained portfolio optimisation. Computers & Operations Research, 27(13), 1271–1302. doi: 10.1016/S0305-0548(99)00074-X
  • Charnes, A., Cooper, W. W., & Ijiri, Y. (1963). Breakeven budgeting and programming to goals. Journal of Accounting Research, 1(1), 16–43. doi: 10.2307/2489841
  • Chava, S., Stefanescu, C., & Turnbull, S. (2011). Modeling the loss distribution. Management Science, 57(7), 1267–1287. doi: 10.1287/mnsc.1110.1345
  • Chavarnakul, T., & Enke, D. (2008). Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications, 34(2), 1004–1017. doi: 10.1016/j.eswa.2006.10.028
  • Chavez-Demoulin, V., Embrechts, P., & Nešlehová, J. (2006). Quantitative models for operational risk: Extremes, dependence and aggregation. Journal of Banking & Finance, 30(10), 2635–2658. doi: 10.1016/j.jbankfin.2005.11.008
  • Chen, C., & Zhou, Y. S. (2018). Robust multiobjective portfolio with higher moments. Expert Systems with Applications, 100, 165–181. doi: 10.1016/j.eswa.2018.02.004
  • Chen, G., & Åstebro, T. (2012). Bound and collapse Bayesian reject inference for credit scoring. Journal of the Operational Research Society, 63(10), 1374–1387. doi: 10.1057/jors.2011.149
  • Chen, H.-H. (2008). Stock selection using data envelopment analysis. Industrial Management & Data Systems, 108(9), 1255–1268. doi: 10.1108/02635570810914928
  • Cheng, D., & Cirillo, P. (2018). A reinforced urn process modeling of recovery rates and recovery times. Journal of Banking & Finance, 96, 1–17. doi: 10.1016/j.jbankfin.2018.08.014
  • Chiu, M. C., & Wong, H. Y. (2012). Mean–variance asset–liability management: Cointegrated assets and insurance liability. European Journal of Operational Research, 223(3), 785–793. doi: 10.1016/j.ejor.2012.07.009
  • Christodoulakis, G. A., & Satchell, S. (2008). The analytics of risk model validation. London: Academic Press.
  • Chrzanowska, M., Alfaro, E., & Witkowska, D. (2009). The individual borrowers recognition: Single and ensemble trees. Expert Systems with Applications, 36(3), 6409–6414. doi: 10.1016/j.eswa.2008.07.048
  • Colapinto, C., La Torre, D., & Aouni, B. (in press). Goal programming for financial portfolio management: a state-of-the-art review. Operational Research.
  • Colladon, A. F., & Remondi, E. (2017). Using social network analysis to prevent money laundering. Expert Systems with Applications, 67, 49–58. doi: 10.1016/j.eswa.2016.09.029
  • Consigli, G., Moriggia, V., Vitali, S., & Mercuri, L. (2018). Optimal insurance portfolios risk-adjusted performance through dynamic stochastic programming. Computational Management Science, 15(3–4), 599–632. doi: 10.1007/s10287-018-0328-7
  • Consiglio, A., Lotfi, S., & Zenios, S. A. (2018). Portfolio diversification in the sovereign credit swap markets. Annals of Operations Research, 266(1–2), 5–33. doi: 10.1007/s10479-017-2565-5
  • Consiglio, A., & Staino, A. (2012). A stochastic programming model for the optimal issuance of government bonds. Annals of Operations Research, 193(1), 159–172. doi: 10.1007/s10479-010-0755-5
  • Creamer, G. (2012). Model calibration and automated trading agent for euro futures. Quantitative Finance, 12(4), 531–545. doi: 10.1080/14697688.2012.664921
  • Creamer, G. (2015). Can a corporate network and news sentiment improve portfolio optimization using the Black–Litterman model? Quantitative Finance, 15(8), 1405–1416. doi: 10.1080/14697688.2015.1039865
  • Crook, J., & Bellotti, T. (2010). Time varying and dynamic models for default risk in consumer loans. Journal of the Royal Statistical Society: Series A (Statistics in Society), 173(2), 283–305. doi: 10.1111/j.1467-985X.2009.00617.x
  • Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447–1465. doi: 10.1016/j.ejor.2006.09.100
  • Crouhy, M., Galai, D., & Mark, R. (2000). A comparative analysis of current credit risk models. Journal of Banking & Finance, 24(1–2), 59–117. doi: 10.1016/S0378-4266(99)00053-9
  • D’Amico, G., Janssen, J., & Manca, R. (2016). Downward migration credit risk problem: A non-homogeneous backward semi-Markov reliability approach. Journal of the Operational Research Society, 67(3), 393–401. doi: 10.1057/jors.2015.35
  • Dempster, M., & Leemans, V. (2006). An automated FX trading system using adaptive reinforcement learning. Expert Systems with Applications, 30(3), 543–552. doi: 10.1016/j.eswa.2005.10.012
  • de Paulo, W. L. D., Oliveira, E. M., & do Valle Costa, O. L. (2016). Enhanced index tracking optimal portfolio selection. Finance Research Letters, 16, 93–102. doi: 10.1016/j.frl.2015.10.005
  • Didimo, W., Giamminonni, L., Liotta, G., Montecchiani, F., & Pagliuca, D. (2018). A visual analytics system to support tax evasion discovery. Decision Support Systems, 110, 71–83. doi: 10.1016/j.dss.2018.03.008
  • Dikmen, B., & Küçükkocaoğlu, G. (2010). The detection of earnings manipulation: The three-phase cutting plane algorithm using mathematical programming. Journal of Forecasting, 29, 442–466.
  • Dirick, L., Claeskens, G., & Baesens, B. (2017). Time to default in credit scoring using survival analysis: A benchmark study. Journal of the Operational Research Society, 68(6), 652–665. doi: 10.1057/s41274-016-0128-9
  • Do, H. X., Rsch, D., & Scheule, H. (2018). Predicting loss severities for residential mortgage loans: A three-step selection approach. European Journal of Operational Research, 270(1), 246–259. doi: 10.1016/j.ejor.2018.02.057
  • Doumpos, M., & Figueira, J. R. (2019). A multicriteria outranking approach for modeling corporate credit ratings: An application of the ELECTRE TRI-nC method. Omega, 82, 166–180. doi: 10.1016/j.omega.2018.01.003
  • Doumpos, M., Lemonakis, C., Niklis, D., & Zopounidis, C. (2019). Analytical techniques in the assessment of credit risk. New York: Springer.
  • Doumpos, M., Niklis, D., Zopounidis, C., & Andriosopoulos, K. (2015). Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms. Journal of Banking & Finance, 50, 599–607. doi: 10.1016/j.jbankfin.2014.01.010
  • Doumpos, M., & Zopounidis, C. (2011). A multicriteria outranking modeling approach for credit rating. Decision Sciences, 42(3), 721–742. doi: 10.1111/j.1540-5915.2011.00328.x
  • Doumpos, M., & Zopounidis, C. (2014). Multicriteria analysis in finance. New York: Springer.
  • Duarte, T. B., Valladão, D. M., & Veiga, Á. (2017). Asset liability management for open pension schemes using multistage stochastic programming under solvency-II-based regulatory constraints. Insurance: Mathematics and Economics, 77, 177–188. doi: 10.1016/j.insmatheco.2017.09.022
  • Dupačová, J., & Kopa, M. (2012). Robustness in stochastic programs with risk constraints. Annals of Operations Research, 200(1), 55–74. doi: 10.1007/s10479-010-0824-9
  • Dymova, L., Sevastianov, P., & Bartosiewicz, P. (2010). A new approach to the rule-base evidential reasoning: Stock trading expert system application. Expert Systems with Applications, 37(8), 5564–5576. doi: 10.1016/j.eswa.2010.02.056
  • Edirisinghe, N., & Zhang, X. (2007). Generalized DEA model of fundamental analysis and its application to portfolio optimization. Journal of Banking & Finance, 31(11), 3311–3335. doi: 10.1016/j.jbankfin.2007.04.008
  • Edirisinghe, N., & Zhang, X. (2008). Portfolio selection under DEA-based relative financial strength indicators: case of US industries. Journal of the Operational Research Society, 59(6), 842–856. doi: 10.1057/palgrave.jors.2602442
  • Eling, M., & Jung, K. (2018). Copula approaches for modeling cross-sectional dependence of data breach losses. Insurance: Mathematics and Economics, 82, 167–180. doi: 10.1016/j.insmatheco.2018.07.003
  • Ertenlice, O., & Kalayci, C. B. (2018). A survey of swarm intelligence for portfolio optimization: Algorithms and applications. Swarm and Evolutionary Computation, 39, 36–52. doi: 10.1016/j.swevo.2018.01.009
  • Evans, C., Pappas, K., & Xhafa, F. (2013). Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation. Mathematical and Computer Modelling, 58(5–6), 1249–1266. doi: 10.1016/j.mcm.2013.02.002
  • Fabozzi, F. J., Kolm, P. N., Pachamanova, D. A., & Focardi, S. M. (2007). Robust portfolio optimization and management. New York: John Wiley.
  • Ferreira, F., Esperança, J., Xavier, M., Costa, R., & Pérez-Gladish, B. (in press). A socio-technical approach to the evaluation of social credit applications. Journal of the Operational Research Society.
  • Fethi, M. D., & Pasiouras, F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European Journal of Operational Research, 204(2), 189–198. doi: 10.1016/j.ejor.2009.08.003
  • Feuerriegel, S., & Gordon, J. (2018). aug). Long-term stock index forecasting based on text mining of regulatory disclosures. Decision Support Systems, 112, 88–97.
  • Fiévet, L., & Sornette, D. (2018). Decision trees unearth return sign predictability in the S&P 500. Quantitative Finance, 18(11), 1797–1814. doi: 10.1080/14697688.2018.1441535
  • Filippi, C., Guastaroba, G., & Speranza, M. (2016). A heuristic framework for the bi-objective enhanced index tracking problem. Omega, 65, 122–137. doi: 10.1016/j.omega.2016.01.004
  • Filomena, T. P., & Lejeune, M. A. (2012). Stochastic portfolio optimization with proportional transaction costs: Convex reformulations and computational experiments. Operations Research Letters, 40(3), 212–217. doi: 10.1016/j.orl.2012.01.003
  • Finlay, S. (2009). Are we modelling the right thing? The impact of incorrect problem specification in credit scoring. Expert Systems with Applications, 36(5), 9065–9071. doi: 10.1016/j.eswa.2008.12.016
  • Finlay, S. (2011). Multiple classifier architectures and their application to credit risk assessment. European Journal of Operational Research, 210(2), 368–378. doi: 10.1016/j.ejor.2010.09.029
  • Florez-Lopez, R., & Ramon-Jeronimo, J. M. (2015). Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment: A correlated-adjusted decision forest proposal. Expert Systems with Applications, 42(13), 5737–5753. doi: 10.1016/j.eswa.2015.02.042
  • Gaganis, C. (2009). Classification techniques for the identification of falsified financial statements: A comparative analysis. Intelligent Systems in Accounting, Finance & Management, 16(3), 207–229. doi: 10.1002/isaf.303
  • Gaivoronski, A., & Pflug, G. (2005). Value-at-risk in portfolio optimization: properties and computational approach. The Journal of Risk, 7(2), 1–31. doi: 10.21314/JOR.2005.106
  • Galagedera, D. U., Roshdi, I., Fukuyama, H., & Zhu, J. (2018). A new network DEA model for mutual fund performance appraisal: An application to U.S. equity mutual funds. Omega, 77, 168–179. doi: 10.1016/j.omega.2017.06.006
  • Galil, K., & Soffer, G. (2011). Good news, bad news and rating announcements: An empirical investigation. Journal of Banking & Finance, 35(11), 3101–3119. doi: 10.1016/j.jbankfin.2011.04.010
  • García, F., Giménez, V., & Guijarro, F. (2013). Credit risk management: A multicriteria approach to assess creditworthiness. Mathematical and Computer Modelling, 57(7–8), 2009–2015. doi: 10.1016/j.mcm.2012.03.005
  • Gavalas, D., & Syriopoulos, T. (2014). An integrated credit rating and loan quality model: application to bank shipping finance. Maritime Policy & Management, 42(6), 533–554. doi: 10.1080/03088839.2014.904948
  • Geva, T., & Zahavi, J. (2014). Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news. Decision Support Systems, 57, 212–223. doi: 10.1016/j.dss.2013.09.013
  • Giesecke, K., Kim, B., Kim, J., & Tsoukalas, G. (2014). Optimal credit swap portfolios. Management Science, 60(9), 2291–2307. doi: 10.1287/mnsc.2013.1890
  • Glancy, F. H., & Yadav, S. B. (2011). A computational model for financial reporting fraud detection. Decision Support Systems, 50(3), 595–601. doi: 10.1016/j.dss.2010.08.010
  • Glasserman, P., Kang, W., & Shahabuddin, P. (2008). Fast simulation of multifactor portfolio credit risk. Operations Research, 56(5), 1200–1217. doi: 10.1287/opre.1080.0558
  • Glen, J. J. (2011). Mean-variance portfolio rebalancing with transaction costs and funding changes. Journal of the Operational Research Society, 62(4), 667–676. doi: 10.1057/jors.2009.148
  • Glpinar, N., & Pachamanova, D. (2013). A robust optimization approach to asset-liability management under time-varying investment opportunities. Journal of Banking & Finance, 37(6), 2031–2041. doi: 10.1016/j.jbankfin.2013.01.025
  • Goldfarb, D., & Iyengar, G. (2003). Robust portfolio selection problems. Mathematics of Operations Research, 28(1), 1–38. doi: 10.1287/moor.28.1.1.14260
  • Goldstein, M. A., Kumar, P., & Graves, F. C. (2014). Computerized and high-frequency trading. Financial Review, 49(2), 177–202. doi: 10.1111/fire.12031
  • Gorgulho, A., Neves, R., & Horta, N. (2011). Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition. Expert Systems with Applications, 38(11), 14072–14085.
  • Gül, S., Kabak, Ö., & Topcu, I. (2018). A multiple criteria credit rating approach utilizing social media data. Data & Knowledge Engineering, 116, 80–99.
  • Guo, X., Zhang, H., & Tian, T. (2018). Development of stock correlation networks using mutual information and financial big data. PLoS One, 13(4), e0195941. doi: 10.1371/journal.pone.0195941
  • Guo, Y., Zhou, W., Luo, C., Liu, C., & Xiong, H. (2016). Instance-based credit risk assessment for investment decisions in P2P lending. European Journal of Operational Research, 249(2), 417–426. doi: 10.1016/j.ejor.2015.05.050
  • Gupta, P., Mehlawat, M. K., & Saxena, A. (2008). Asset portfolio optimization using fuzzy mathematical programming. Information Sciences, 178(6), 1734–1755. doi: 10.1016/j.ins.2007.10.025
  • Gutiérrez-Nieto, B., Serrano-Cinca, C., & Camón-Cala, J. (2014). A credit score system for socially responsible lending. Journal of Business Ethics, 133(4, 691–701. doi: 10.1007/s10551-014-2448-5
  • Hallerbach, W., Ning, H., Soppe, A., & Spronk, J. (2004). A framework for managing a portfolio of socially responsible investments. European Journal of Operational Research, 153(2), 517–529. doi: 10.1016/S0377-2217(03)00172-3
  • Hamzaçebi, C., & Pekkaya, M. (2011). Determining of stock investments with grey relational analysis. Expert Systems with Applications, 38(8), 9186–9195. doi: 10.1016/j.eswa.2011.01.070
  • Hazan, E., & Kale, S. (2015). An online portfolio selection algorithm with regret logarithmic in price variation. Mathematical Finance, 25(2), 288–310. doi: 10.1111/mafi.12006
  • He, J., Zhang, Y., Shi, Y., & Huang, G. (2010). Domain-driven classification based on multiple criteria and multiple constraint-level programming for intelligent credit scoring. IEEE Transactions on Knowledge and Data Engineering, 22(6), 826–838. doi: 10.1109/TKDE.2010.43
  • Heaton, J. B., Polson, N. G., & Witte, J. H. (2017). Deep learning for finance: Deep portfolios. Applied Stochastic Models in Business and Industry, 33(1), 3–12.
  • Hibiki, N. (2006). Multi-period stochastic optimization models for dynamic asset allocation. Journal of Banking & Finance, 30(2), 365–390. doi: 10.1016/j.jbankfin.2005.04.027
  • Hillier, F. S. (1963). The derivation of probabilistic information for the evaluation of risky investments. Management Science, 9(3), 443–457. doi: 10.1287/mnsc.9.3.443
  • Huang, C.-F. (2012). A hybrid stock selection model using genetic algorithms and support vector regression. Applied Soft Computing, 12(2), 807–818. doi: 10.1016/j.asoc.2011.10.009
  • Huang, D., Mu, D., Yang, L., & Cai, X. (2018). CoDetect: Financial fraud detection with anomaly feature detection. IEEE Access, 6, 19161–19174. doi: 10.1109/ACCESS.2018.2816564
  • Huang, S.-Y., Tsaih, R.-H., & Yu, F. (2014). Topological pattern discovery and feature extraction for fraudulent financial reporting. Expert Systems with Applications, 41(9), 4360–4372. doi: 10.1016/j.eswa.2014.01.012
  • Iazzolino, G., Bruni, M. E., & Beraldi, P. (2013). Using DEA and financial ratings for credit risk evaluation: an empirical analysis. Applied Economics Letters, 20(14), 1310–1317. doi: 10.1080/13504851.2013.806771
  • Iscoe, I., Kreinin, A., Mausser, H., & Romanko, O. (2012). Portfolio credit-risk optimization. Journal of Banking & Finance, 36(6), 1604–1615. doi: 10.1016/j.jbankfin.2012.01.013
  • Ivorra, B., Mohammadi, B., & Ramos, A. M. (2007). Optimization strategies in credit portfolio management. Journal of Global Optimization, 43(2-3, 415–427. doi: 10.1007/s10898-007-9221-6
  • Janabi, M. A. A., Hernandez, J. A., Berger, T., & Nguyen, D. K. (2017). Multivariate dependence and portfolio optimization algorithms under illiquid market scenarios. European Journal of Operational Research, 259(3, 1121–1131. doi: 10.1016/j.ejor.2016.11.019
  • Jeong, G., & Kim, H. Y. (2019). Improving financial trading decisions using deep q-learning: Predicting the number of shares, action strategies, and transfer learning. Expert Systems with Applications, 117, 125–138. doi: 10.1016/j.eswa.2018.09.036
  • Jobst, N., Horniman, M., Lucas, C., & Mitra, G. (2001). Computational aspects of alternative portfolio selection models in the presence of discrete asset choice constraints. Quantitative Finance, 1(5), 489–501. doi: 10.1088/1469-7688/1/5/301
  • Jondeau, W., & Rockinger, M. (2006). Optimal portfolio allocation under higher moments. European Financial Management, 12(1), 29–55. doi: 10.1111/j.1354-7798.2006.00309.x
  • Jorion, P. (2009). Value at risk (3rd ed.). New York: McGraw-Hill.
  • Jung, J., & Kim, S. (2015). An adaptively managed dynamic portfolio selection model using a time-varying investment target according to the market forecast. Journal of the Operational Research Society, 66(7), 1115–1131. doi: 10.1057/jors.2014.72
  • Kalyagin, V. A., Koldanov, A., Koldanov, P., Pardalos, P., & Zamaraev, V. (2014). Measures of uncertainty in market network analysis. Physica A: Statistical Mechanics and Its Applications, 413, 59–70. doi: 10.1016/j.physa.2014.06.054
  • Kalyagin, V. A., Koldanov, A. P., Koldanov, P. A., & Pardalos, P. M. (2017). Optimal decision for the market graph identification problem in a sign similarity network. Annals of Operations Research, 266(1–2), 313–327. doi: 10.1007/s10479-017-2491-6
  • Kalyagin, V. A., Pardalos, P. M., & Rassias, T. M. (Eds.). (2014). Network models in economics and finance. Berlin, Germany: Springer International Publishing.
  • Kapsos, M., Christofides, N., & Rustem, B. (2014). Worst-case robust omega ratio. European Journal of Operational Research, 234(2), 499–507. doi: 10.1016/j.ejor.2013.04.025
  • Kardas, G., Challenger, M., Yildirim, S., & Yamuc, A. (2011). Design and implementation of a multiagent stock trading system. Software: Practice and Experience, 42(10), 1247–1273. doi: 10.1002/spe.1137
  • Kim, H. Y., & Won, C. H. (2018). Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models. Expert Systems with Applications, 103, 25–37.
  • Kim, J.-Y., & Cho, S.-B. (2019). Deep dense convolutional networks for repayment prediction in peer-to-peer lending. In M. Graña (Ed.), International joint conference SOCO’18-CISIS’18-ICEUTE’18 (pp. 134–144). Cham: Springer International Publishing.
  • Kim, W. C., Kim, J. H., Ahn, S. H., & Fabozzi, F. J. (2012). What do robust equity portfolio models really do? Annals of Operations Research, 205(1), 141–168. doi: 10.1007/s10479-012-1247-6
  • Kiris, S., & Ustun, O. (2012). An integrated approach for stock evaluation and portfolio optimization. Optimization, 61(4), 423–441. doi: 10.1080/02331934.2011.644285
  • Ko, C.-C., Lin, T. T., & Yang, C. (2011). The venture capital entry model on game options with jump-diffusion process. International Journal of Production Economics, 134(1), 87–94. doi: 10.1016/j.ijpe.2011.02.016
  • Kocheturov, A., Batsyn, M., & Pardalos, P. M. (2014). Dynamics of cluster structures in a financial market network. Physica A: Statistical Mechanics and Its Applications, 413, 523–533. doi: 10.1016/j.physa.2014.06.077
  • Koldanov, A. P., Koldanov, P. A., Kalyagin, V. A., & Pardalos, P. M. (2013). Statistical procedures for the market graph construction. Computational Statistics & Data Analysis, 68, 17–29. doi: 10.1016/j.csda.2013.06.005
  • Kosmidou, K., & Zopounidis, C. (2008). Generating interest rate scenarios for bank asset liability management. Optimization Letters, 2(2), 157–169. doi: 10.1007/s11590-007-0050-9
  • Kozeny, V. (2015). Genetic algorithms for credit scoring: Alternative fitness function performance comparison. Expert Systems with Applications, 42(6), 2998–3004. doi: 10.1016/j.eswa.2014.11.028
  • Krger, S., Oehme, T., Rsch, D., & Scheule, H. (2018). A copula sample selection model for predicting multi-year LGDs and lifetime expected losses. Journal of Empirical Finance, 47, 246–262. doi: 10.1016/j.jempfin.2018.04.001
  • Krger, S., & Rsch, D. (2017). Downturn LGD modeling using quantile regression. Journal of Banking & Finance, 79, 42–56.
  • Kvamme, H., Sellereite, N., Aas, K., & Sjursen, S. (2018). Predicting mortgage default using convolutional neural networks. Expert Systems with Applications, 102, 207–217. doi: 10.1016/j.eswa.2018.02.029
  • Leow, M., Mues, C., & Thomas, L. (2014). The economy and loss given default: Evidence from two UK retail lending data sets. Journal of the Operational Research Society, 65(3), 363–375. doi: 10.1057/jors.2013.120
  • Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136. doi: 10.1016/j.ejor.2015.05.030
  • Li, A., Shi, Y., & He, J. (2008). MCLP-based methods for improving “bad” catching rate in credit cardholder behavior analysis. Applied Soft Computing, 8(3), 1259–1265. doi: 10.1016/j.asoc.2007.02.014
  • Li, J., Wei, L., Li, G., & Xu, W. (2011). An evolution strategy-based multiple kernels multi-criteria programming approach: The case of credit decision-making. Decision Support Systems, 51(2), 292–298. doi: 10.1016/j.dss.2010.11.022
  • Li, S.-T., & Kuo, S. (2008). Knowledge discovery in financial investment for forecasting and trading strategy through wavelet-based SOM networks. Expert Systems with Applications, 34(2), 935–951. doi: 10.1016/j.eswa.2006.10.039
  • Liu, G. (2015). Simulating risk contributions of credit portfolios. Operations Research, 63(1), 104–121. doi: 10.1287/opre.2015.1351
  • Liu, H., Mulvey, J., & Zhao, T. (2016). A semiparametric graphical modelling approach for large-scale equity selection. Quantitative Finance, 16(7), 1053–1067. doi: 10.1080/14697688.2015.1101149
  • Liu, Q., Guo, Z., & Wang, J. (2012). A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization. Neural Networks, 26, 99–109. doi: 10.1016/j.neunet.2011.09.001
  • Liu, X., Cao, Y., Ma, C., & Shen, L. (2019). Wavelet-based option pricing: An empirical study. European Journal of Operational Research, 272(3), 1132–1142. doi: 10.1016/j.ejor.2018.07.025
  • Lobo, M. S., Fazel, M., & Boyd, S. (2007). Portfolio optimization with linear and fixed transaction costs. Annals of Operations Research, 152(1), 341–365. doi: 10.1007/s10479-006-0145-1
  • Loterman, G., Brown, I., Martens, D., Mues, C., & Baesens, B. (2012). Benchmarking regression algorithms for loss given default modeling. International Journal of Forecasting, 28(1), 161–170. doi: 10.1016/j.ijforecast.2011.01.006
  • Lotfi, S., & Zenios, S. A. (2018). Robust VaR and CVaR optimization under joint ambiguity in distributions, means, and covariances. European Journal of Operational Research, 269(2), 556–576. doi: 10.1016/j.ejor.2018.02.003
  • Luo, C., Wu, D., & Wu, D. (2017). A deep learning approach for credit scoring using credit default swaps. Engineering Applications of Artificial Intelligence, 65, 465–470. doi: 10.1016/j.engappai.2016.12.002
  • Lyra, M., Paha, J., Paterlini, S., & Winker, P. (2010). Optimization heuristics for determining internal rating grading scales. Computational Statistics & Data Analysis, 54(11), 2693–2706. doi: 10.1016/j.csda.2009.03.004
  • Mabu, S., Hirasawa, K., Obayashi, M., & Kuremoto, T. (2013). Enhanced decision-making mechanism of rule-based genetic network programming for creating stock trading signals. Expert Systems with Applications, 40(16), 6311–6320. doi: 10.1016/j.eswa.2013.05.037
  • Malik, M., & Thomas, L. C. (2010). Modelling credit risk of portfolio of consumer loans. Journal of the Operational Research Society, 61(3), 411–420. doi: 10.1057/jors.2009.123
  • Maringer, D. (2005). Portfolio management with heuristic optimization. Dordrecht: Springer-Verlag.
  • Maringer, D., & Parpas, P. (2009). Global optimization of higher order moments in portfolio selection. Journal of Global Optimization, 43(2–3), 219–230. doi: 10.1007/s10898-007-9224-3
  • Markowitz, H. M. (1959). Portfolio selection: Efficient diversification of investment. New York: John Wiley & Sons.
  • Marqués, A., García, V., & Sánchez, J. (2012). Two-level classifier ensembles for credit risk assessment. Expert Systems with Applications, 39(12), 10916–10922. doi: 10.1016/j.eswa.2012.03.033
  • Marqués, A., García, V., & Sánchez, S. (2013). A literature review on the application of evolutionary computing to credit scoring. Journal of the Operational Research Society, 64(9), 1384–1399. doi: 10.1057/jors.2012.145
  • Martens, D., Baesens, B., Gestel, T. V., & Vanthienen, J. (2007). Comprehensible credit scoring models using rule extraction from support vector machines. European Journal of Operational Research, 183(3), 1466–1476. doi: 10.1016/j.ejor.2006.04.051
  • Martens, D., Gestel, T. V., Backer, M. D., Haesen, R., Vanthienen, J., & Baesens, B. (2010). Credit rating prediction using ant colony optimization. Journal of the Operational Research Society, 61(4), 561–573. doi: 10.1057/jors.2008.164
  • Mencía, J. (2012). Assessing the risk-return trade-off in loan portfolios. Journal of Banking & Finance, 36(6), 1665–1677. doi: 10.1016/j.jbankfin.2012.01.007
  • Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of Finance, 29(2), 449. doi: 10.1111/j.1540-6261.1974.tb03058.x
  • Metawa, N., Hassan, M. K., & Elhoseny, M. (2017). Genetic algorithm based model for optimizing bank lending decisions. Expert Systems with Applications, 80, 75–82. doi: 10.1016/j.eswa.2017.03.021
  • Mezali, H., & Beasley, J. E. (2013). Quantile regression for index tracking and enhanced indexation. Journal of the Operational Research Society, 64(11), 1676–1692. doi: 10.1057/jors.2012.186
  • Mitra, G., & Mitra, L. (Eds.). (2011). The handbook of news analytics in finance. New York: John Wiley & Sons, Ltd.
  • Moriggia, V., Kopa, M., & Vitali, S. (2018). Pension fund management with hedging derivatives, stochastic dominance and nodal contamination. Omega. doi: 10.1016/j.omega.2018.08.011
  • Myers, S., & Pogue, G. (1974). A programming approach to corporate financial management. The Journal of Finance, 29(2), 579–599. doi: 10.2307/2978829
  • Nakano, M., Takahashi, A., & Takahashi, S. (2018). Bitcoin technical trading with artificial neural network. Physica A: Statistical Mechanics and Its Applications, 510, 587–609. doi: 10.1016/j.physa.2018.07.017
  • Nami, S., & Shajari, M. (2018). Cost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors. Expert Systems with Applications, 110, 381–392. doi: 10.1016/j.eswa.2018.06.011
  • Nazemi, A., Pour, F. F., Heidenreich, K., & Fabozzi, F. J. (2017). Fuzzy decision fusion approach for loss-given-default modeling. European Journal of Operational Research, 262(2), 780–791. doi: 10.1016/j.ejor.2017.04.008
  • Niklis, D., Doumpos, M., & Zopounidis, C. (2014). Combining market and accounting-based models for credit scoring using a classification scheme based on support vector machines. Applied Mathematics and Computation, 234, 69–81. doi: 10.1016/j.amc.2014.02.028
  • Oreski, S., Oreski, D., & Oreski, G. (2012). Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment. Expert Systems with Applications, 39(16), 12605–12617. doi: 10.1016/j.eswa.2012.05.023
  • Óskarsdóttir, M., Bravo, C., Sarraute, C., Vanthienen, J., & Baesens, B. (2019). The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics. Applied Soft Computing, 74, 26–39. doi: 10.1016/j.asoc.2018.10.004
  • Östermark, R. (2017). Massively parallel processing of recursive multi-period portfolio models. European Journal of Operational Research, 259(1), 344–366. doi: 10.1016/j.ejor.2016.10.009
  • Pätäri, E., Karell, V., Luukka, P., & Yeomans, J. S. (2018). Comparison of the multicriteria decision-making methods for equity portfolio selection: The U.S. evidence. European Journal of Operational Research, 265(2), 655–672. doi: 10.1016/j.ejor.2017.08.001
  • Peña, A., Bonet, I., Lochmuller, C., Chiclana, F., & Góngora, M. (2018). Flexible inverse adaptive fuzzy inference model to identify the evolution of operational value at risk for improving operational risk management. Applied Soft Computing, 65, 614–631. doi: 10.1016/j.asoc.2018.01.024
  • Pendharkar, P. C., & Cusatis, P. (2018). Trading financial indices with reinforcement learning agents. Expert Systems with Applications, 103, 1–13. doi: 10.1016/j.eswa.2018.02.032
  • Peng, Y., Kou, G., Shi, Y., & Chen, Z. (2008). A multi-criteria convex quadratic programming model for credit data analysis. Decision Support Systems, 44(4), 1016–1030. doi: 10.1016/j.dss.2007.12.001
  • Pfister, T., Utz, S., & Wimmer, M. (2015). Capital allocation in credit portfolios in a multi-period setting: A literature review and practical guidelines. Review of Managerial Science, 9(1), 1–32. doi: 10.1007/s11846-014-0119-7
  • Post, T., & Kopa, M. (2017). Portfolio choice based on third-degree stochastic dominance. Management Science, 63(10), 3381–3392. doi: 10.1287/mnsc.2016.2506
  • Qi, M., & Zhao, X. (2011). Comparison of modeling methods for loss given default. Journal of Banking & Finance, 35(11), 2842–2855. doi: 10.1016/j.jbankfin.2011.03.011
  • Quah, T. (2008). DJIA stock selection assisted by neural network. Expert Systems with Applications, 35(1–2), 50–58. doi: 10.1016/j.eswa.2007.06.039
  • Quek, C., Pasquier, M., & Kumar, N. (2007). A novel recurrent neural network-based prediction system for option trading and hedging. Applied Intelligence, 29(2), 138–151. doi: 10.1007/s10489-007-0052-4
  • Quintana, D., Chávez, F., Luque Baena, R. M., & Luna, F. (2018). Fuzzy techniques for IPO underpricing prediction. Journal of Intelligent & Fuzzy Systems, 35(1), 367–381. doi: 10.3233/JIFS-169595
  • Quirini, L., & Vannucci, L. (2014). Creditworthiness dynamics and hidden Markov models. Journal of the Operational Research Society, 65(3), 323–330. doi: 10.1057/jors.2012.181
  • Rasmussen, K. M., & Clausen, J. (2007). Mortgage loan portfolio optimization using multi-stage stochastic programming. Journal of Economic Dynamics and Control, 31(3), 742–766. doi: 10.1016/j.jedc.2006.01.004
  • Rockafellar, R., & Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of Banking & Finance, 26(7), 1443–1471. doi: 10.1016/S0378-4266(02)00271-6
  • Ryoo, H. S. (2007). A compact mean-variance-skewness model for large-scale portfolio optimization and its application to the NYSE market. Journal of the Operational Research Society, 58(4), 505–515. doi: 10.1057/palgrave.jors.2602168
  • Sahin, Y., Bulkan, S., & Duman, E. (2013). A cost-sensitive decision tree approach for fraud detection. Expert Systems with Applications, 40(15), 5916–5923. doi: 10.1016/j.eswa.2013.05.021
  • Sak, H., & Hörmann, W. (2012). Fast simulations in credit risk. Quantitative Finance, 12(10), 1557–1569. doi: 10.1080/14697688.2011.564199
  • Sanford, A. D., & Moosa, I. A. (2012). A Bayesian network structure for operational risk modelling in structured finance operations. Journal of the Operational Research Society, 63(4), 431–444. doi: 10.1057/jors.2011.7
  • Scheule, H., Baesens, B., & Roesch, D. (2016). Credit risk analytics: Measurement techniques, applications, and examples in SAS. Hoboken, NJ: John Wiley & Sons Inc.
  • Schumaker, R. P., Zhang, Y., Huang, C.-N., & Chen, H. (2012). Evaluating sentiment in financial news articles. Decision Support Systems, 53(3), 458–464. doi: 10.1016/j.dss.2012.03.001
  • Sermpinis, G., Stasinakis, C., Rosillo, R., & Fuente, D. D L. (2017). European exchange trading funds trading with locally weighted support vector regression. European Journal of Operational Research, 258(1), 372–384. doi: 10.1016/j.ejor.2016.09.005
  • Sermpinis, G., Stasinakis, C., Theofilatos, K., & Karathanasopoulos, A. (2015). Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations. European Journal of Operational Research, 247(3), 831–846. doi: 10.1016/j.ejor.2015.06.052
  • Serrano-Cinca, C., & Gutiérrez-Nieto, B. (2016). The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Decision Support Systems, 89, 113–122. doi: 10.1016/j.dss.2016.06.014
  • Serrano-Cinca, C., Gutiérrez-Nieto, B., & López-Palacios, L. (2015). Determinants of default in P2P lending. PLoS One, 10(10), e0139427.doi: 10.1371/journal.pone.0139427
  • Serrano-Silva, Y. O., Villuendas-Rey, Y., & Yáñez-Márquez, C. (2018). Automatic feature weighting for improving financial decision support systems. Decision Support Systems, 107, 78–87. doi: 10.1016/j.dss.2018.01.005
  • Sevastjanov, P., & Dymova, L. (2009). Stock screening with use of multiple criteria decision-making and optimization. Omega, 37(3), 659–671. doi: 10.1016/j.omega.2008.04.002
  • Shevchenko, P. V. (2009). Implementing loss distribution approach for operational risk. Applied Stochastic Models in Business and Industry, 26(3), 277–307. doi: 10.1002/asmb.812
  • Shevchenko, P. V. (2011). Modelling operational risk using Bayesian inference. Berlin, Heidelberg: Springer.
  • Sirignano, J. A., Tsoukalas, G., & Giesecke, K. (2016). Large-scale loan portfolio selection. Operations Research, 64(6), 1239–1255. doi: 10.1287/opre.2016.1537
  • Skabar, A. (2013). Direction-of-change financial time series forecasting using a similarity-based classification model. Journal of Forecasting, 32(5), 409–422. doi: 10.1002/for.2247
  • Smales, L. (2016). News sentiment and bank credit risk. Journal of Empirical Finance, 38, 37–61. doi: 10.1016/j.jempfin.2016.05.002
  • Song, Q., Liu, A., & Yang, S. Y. (2017). Stock portfolio selection using learning-to-rank algorithms with news sentiment. Neurocomputing, 264, 20–28. doi: 10.1016/j.neucom.2017.02.097
  • Sreekantha, D. K., & Kulkarni, R. V. (2012). Expert system design for credit risk evaluation using neuro-fuzzy logic. Expert Systems, 29(1), 56–69.
  • Strub, O., & Baumann, P. (2018). Optimal construction and rebalancing of index-tracking portfolios. European Journal of Operational Research, 264(1), 370–387. doi: 10.1016/j.ejor.2017.06.055
  • Tan, Z., Quek, C., & Cheng, P. Y. (2011). Stock trading with cycles: A financial application of ANFIS and reinforcement learning. Expert Systems with Applications, 38(5), 4741–4755. doi: 10.1016/j.eswa.2010.09.001
  • Thomas, L. C. (2009). Consumer credit models. Oxford, UK: Oxford University Press.
  • Throckmorton, C. S., Mayew, W. J., Venkatachalam, M., & Collins, L. M. (2015, June). Financial fraud detection using vocal, linguistic and financial cues. Decision Support Systems, 74, 78–87. doi: 10.1016/j.dss.2015.04.006
  • Tian, X., Xu, Z., & Fujita, H. (2018). Sequential funding the venture project or not? A prospect consensus process with probabilistic hesitant fuzzy preference information. Knowledge-Based Systems, 161, 172–184. doi: 10.1016/j.knosys.2018.08.002
  • Tobback, E., Martens, D., Gestel, T. V., & Baesens, B. (2014). Forecasting loss given default models: impact of account characteristics and the macroeconomic state. Journal of the Operational Research Society, 65(3), 376–392. doi: 10.1057/jors.2013.158
  • Treleaven, P., Galas, M., & Lalchand, V. (2013). Algorithmic trading review. Communications of the ACM, 56(11), 76–85. doi: 10.1145/2500117
  • Valladão, D. M., Veiga, Á., & Street, A. (2018). A linear stochastic programming model for optimal leveraged portfolio selection. Computational Economics, 51(4), 1021–1032. doi: 10.1007/s10614-017-9656-x
  • Valladão, D. M., Veiga, Á., & Veiga, G. (2014). A multistage linear stochastic programming model for optimal corporate debt management. European Journal of Operational Research, 237(1), 303–311. doi: 10.1016/j.ejor.2014.01.028
  • van der Hart, J., Slagter, E., & van Dijk, D. (2003). Stock selection strategies in emerging markets. Journal of Empirical Finance, 10(1–2), 105–132. doi: 10.1016/S0927-5398(02)00022-1
  • Vasicek, O. A. (1987). Probability of loss on loan portfolio. San Francisco, USA: KMV Corporation.
  • Vercher, E., & Bermudez, J. D. (2013). A possibilistic mean-downside risk-skewness model for efficient portfolio selection. IEEE Transactions on Fuzzy Systems, 21(3), 585–595. doi: 10.1109/TFUZZ.2012.2227487
  • Viswanathan, P., Ranganatham, M., & Balasubramanian, G. (2014). Modeling asset allocation and liability composition for Indian banks. Managerial Finance, 40(7), 700–723. doi: 10.1108/MF-10-2013-0276
  • Wei, Y., Yildirim, P., Bulte, C. V., den., & Dellarocas, C. (2016). Credit scoring with social network data. Marketing Science, 35(2), 234–258. doi: 10.1287/mksc.2015.0949
  • Witzany, J. (2017). Credit risk management - Pricing, measurement, and modeling. Cham, Switzerland: Springer.
  • Woodside-Oriakhi, M., Lucas, C., & Beasley, J. (2011). Heuristic algorithms for the cardinality constrained efficient frontier. European Journal of Operational Research, 213(3), 538–550. doi: 10.1016/j.ejor.2011.03.030
  • Xidonas, P., Mavrotas, G., Hassapis, C., & Zopounidis, C. (2017). Robust multiobjective portfolio optimization: A minimax regret approach. European Journal of Operational Research, 262(1), 299–305. doi: 10.1016/j.ejor.2017.03.041
  • Xidonas, P., Mavrotas, G., Krintas, T., Psarras, J., & Zopounidis, C. (2012). Multicriteria portfolio management. New York: Springer.
  • Xidonas, P., Mavrotas, G., & Psarras, J. (2010). A multiple criteria decision-making approach for the selection of stocks. Journal of the Operational Research Society, 61(8), 1273–1287. doi: 10.1057/jors.2009.74
  • Xidonas, P., Mavrotas, G., Zopounidis, C., & Psarras, J. (2011). IPSSIS: An integrated multicriteria decision support system for equity portfolio construction and selection. European Journal of Operational Research, 210(2), 398–409. doi: 10.1016/j.ejor.2010.08.028
  • Xu, L., Zhang, L., & Yao, D. (2017). Optimal investment and reinsurance for an insurer under Markov-modulated financial market. Insurance: Mathematics and Economics, 74, 7–19. doi: 10.1016/j.insmatheco.2017.02.005
  • Xu, X., He, F., Chen, R., & Zhang, Q. (2015). Solving non-linear portfolio optimization problems with interval analysis. Journal of the Operational Research Society, 66(6), 885–893. doi: 10.1057/jors.2014.31
  • Yan, W., & Clack, C. D. (2010). Evolving robust GP solutions for hedge fund stock selection in emerging markets. Soft Computing, 15(1), 37–50. doi: 10.1007/s00500-009-0511-4
  • Yang, P.-Y., Lai, Z.-R., Wu, X., & Fang, L. (2018). Trend representation based log-density regularization system for portfolio optimization. Pattern Recognition, 76, 14–24. doi: 10.1016/j.patcog.2017.10.024
  • Yao, X., Crook, J., & Andreeva, G. (2015). Support vector regression for loss given default modelling. European Journal of Operational Research, 240(2), 528–538. doi: 10.1016/j.ejor.2014.06.043
  • Yao, X., Crook, J., & Andreeva, G. (2017). Enhancing two-stage modelling methodology for loss given default with support vector machines. European Journal of Operational Research, 263(2), 679–689. doi: 10.1016/j.ejor.2017.05.017
  • Yao, Y., Zhai, J., Cao, Y., Ding, X., Liu, J., & Luo, Y. (2017). Data analytics enhanced component volatility model. Expert Systems with Applications, 84, 232–241. doi: 10.1016/j.eswa.2017.05.025
  • Yeh, C.-C., Lin, F., & Hsu, C.-Y. (2012). A hybrid KMV model, random forests and rough set theory approach for credit rating. Knowledge-Based Systems, 33, 166–172. doi: 10.1016/j.knosys.2012.04.004
  • Yu, L., Wang, S., & Lai, K. K. (2009). An intelligent-agent-based fuzzy group decision-making model for financial multicriteria decision support: The case of credit scoring. European Journal of Operational Research, 195(3), 942–959. doi: 10.1016/j.ejor.2007.11.025
  • Zeng, Y., & Klabjan, D. (2019). Online adaptive machine learning based algorithm for implied volatility surface modeling. Knowledge-Based Systems, 163, 376–391. doi: 10.1016/j.knosys.2018.08.039
  • Zenios, S., Consiglio, A., & Nielsen, S. S. (2010). Practical Financial Optimization. New York: John Wiley & Sons, Ltd.
  • Zhang, J., & Maringer, D. (2015). Using a genetic algorithm to improve recurrent reinforcement learning for equity trading. Computational Economics, 47(4), 551–567. doi: 10.1007/s10614-015-9490-y
  • Zhang, T., Dai, Q., & Ma, Z. (2015). Extreme learning machines’ ensemble selection with GRASP. Applied Intelligence, 43(2), 439–459. doi: 10.1007/s10489-015-0653-2
  • Zhang, Z., Gao, G., & Shi, Y. (2014). Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors. European Journal of Operational Research, 237(1), 335–348. doi: 10.1016/j.ejor.2014.01.044
  • Zhao, L., Li, W., & Cai, X. (2016). Structure and dynamics of stock market in times of crisis. Physics Letters A, 380(5–6), 654–666. doi: 10.1016/j.physleta.2015.11.015
  • Zhao, Z., Xu, F., Wang, M., & Yi Zhang, C. (2019). A sparse enhanced indexation model with norm and its alternating quadratic penalty method. Journal of the Operational Research Society, 70(3), 433–445. doi: 10.1080/01605682.2018.1447245
  • Zhong, H., Liu, C., Zhong, J., & Xiong, H. (2018). Which startup to invest in: A personalized portfolio strategy. Annals of Operations Research, 263(1–2), 339–360. doi: 10.1007/s10479-016-2316-z
  • Zong-Chang, Y., Hong, K., Ji-Sheng, X., & Hong, S. (2015). Artificial immune algorithm-based credit evaluation for mobile telephone customers. Journal of the Operational Research Society, 66(9), 1533–1541. doi: 10.1057/jors.2014.105
  • Zopounidis, C., Doumpos, M., & Niklis, D. (2018). Financial decision support: An overview of developments and recent trends. EURO Journal on Decision Processes, 6(1–2), 63–76. doi: 10.1007/s40070-018-0078-3

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.