Computational Economics and Finance (Maringer)

Lenhard, Gregor (2024) ‘Learning from the Past: The Role of Personal Experiences in Artificial Stock Markets ’, WWZ Working Paper. Wirtschaftswissenschaftliche Fakultät (Computational Economics and Finance, 1).

Balaneji, Farshid (2024) Textual Insights for Financial Wisdom:A NLP-Driven Approach to Market Forecasting - Three Essays on the Application of Natural Language Processing in Financial Forecasting. . Translated by Dietmar Maringer, Irene Spasic. University of Basel.

Balaneji, Farshid, Maringer, Dietmar and Spasić, ‪Irena (2024) ‘The Power of Words: Predicting Stock Market Returns with Fine-Grained Sentiment Analysis and XGBoost’, in Kohei Arai (ed.), Kohei Arai (tran.) Intelligent Systems and Applications. Cham: Springer Science and Business Media Deutschland GmbH (Lecture Notes in Networks and Systems), pp. 577–596. Available at: https://doi.org/10.1007/978-3-031-47721-8_39.

Lenhard, Gregor (2024) Advances in Financial Modeling - Integrating Machine Learning and Behavioral Insights. . Translated by Dietmar Maringer, Tim Kröncke. University of Basel.

Sulas, A., Maringer, D. and Paterlini, S. (2024) ‘Systemic Risk from Overlapping Portfolios: A Multi-Objective Optimization Framework’. Elsevier BV. Available at: https://doi.org/10.2139/ssrn.4837176.

Balaneji, Farshid and Maringer, Dietmar (2022) ‘Applying Sentiment Analysis, Topic Modeling, and XGBoost to Classify Implied Volatility’. IEEE: IEEE. Available at: https://doi.org/10.1109/cifer52523.2022.9776196.

Chintala, P., Dornberger, R. and Hanne, T. (2022) ‘Robotic Path Planning by Q Learning and a Performance Comparison with Classical Path Finding Algorithms’, International Journal of Mechanical Engineering and Robotics Research, 11(6), pp. 373–378. Available at: https://doi.org/10.18178/ijmerr.11.6.373-378.

Frey et al. (2022) ‘A Genetic Algorithm to Solve the Order Picking Problem in a Warehouse with Systematic Item Distribution’. Springer: Springer.

Lenhard, Gregor and Maringer, Dietmar (2022) ‘State-ANFIS: A Generalized Regime-Switching Model for Financial Modeling’. IEEE: IEEE. Available at: https://doi.org/10.1109/cifer52523.2022.9776208.

Maringer, D., Craig, B. and Paterlini, S. (2022) ‘Constructing banking networks under decreasing costs of link formation’, Computational Management Science, 19(1), pp. 41–64. Available at: https://doi.org/10.1007/s10287-021-00393-w.

Minder et al. (2022) ‘Assessing the Quality of Car Racing Controllers in a Virtual Setting under Changed Conditions’. ACM International Conference Proceeding Series: ACM International Conference Proceeding Series.

Saner, K. et al. (2022) ‘Optimization of Artificial Landscapes with a Hybridized Firefly Algorithm’, Journal of Advances in Information Technology, 13(4), pp. 374–380. Available at: https://doi.org/10.12720/jait.13.4.374-380.

Schären et al. (2022) ‘The Xoshiro+ Pseudorandom Number Generator in a Computer Chess Program’. Springer: Springer.

Subaskaran et al. (2022) ‘Comparison of Ant Colony Optimization Algorithms for Small-Sized Travelling Salesman Problems’. Springer: Springer.

Tosoni, Deniz et al. (2022) ‘Benchmarking Metaheuristic Optimization Algorithms on Travelling Salesman Problems’. ACM International Conference Proceeding Series: ACM International Conference Proceeding Series.

Wong et al. (2022) ‘An analysis of weight initialization methods in connection with different activation functions for feedforward neural networks’, Evolutionary Intelligence, p. https://doi.org/10.1007/s12065–022–00795–y. Available at: https://doi.org/10.1007/s12065-022-00795-y.

Fink et al. (2022) ‘Optimizing an Inventory Routing Problem Using a Modified Tabu Search’, in Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) (ed.) Lecture Notes on Data Engineering and Communications Technologies. Singapore: Springer Nature (Lecture Notes on Data Engineering and Communications Technologies), pp. 577–586.

Johner et al. (2022) ‘Comparing the Pathfinding Algorithms A*, Dijkstra’s, Bellman-Ford, Floyd-Warshall, and Best First Search for the Paparazzi Problem’, in Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) (ed.) Lecture Notes on Data Engineering and Communications Technologies. Singapore: Springer Nature (Lecture Notes on Data Engineering and Communications Technologies), pp. 561–576.

Ates, Caner and Maringer, Dietmar (2021) ‘A Parsimonious Macroeconomic ABM for Labor Market Regulations’, LEM Working Papers [Preprint]. Laboratory of Economics and Management, Pisa (LEM Working Papers). Available at: http://www.lem.sssup.it/WPLem/2021-46.html.

Gilli, Manfred and Schumann, Enrico (2021) ‘Risk-Reward Ratio Optimisation (Revisited)’, in Dawid, Herbert; Arifovic, Jasmina (ed.) Dynamic Analysis in Complex Economic Environments: Essays in Honor of Christophe Deissenberg. Springer Nature Switzerland AG: Springer (Dynamic Modeling and Econometrics in Economics and Finance), pp. 29–57. Available at: https://doi.org/10.1007/978-3-030-52970-3_3.

Aussenegg, Wolfgang et al. (2019) ‘Time Varying Factors in the Performance of Corporate Bond Indices’. SSRN. Available at: https://doi.org/10.2139/ssrn.3303160.

Gilli, Manfred, Maringer, Dietmar and Schumann, Enrico (2019) Numerical Methods and Optimization in Finance. 2nd (revised and extended). London: Academic Press, Elsevier. Available at: https://doi.org/10.1016/c2017-0-01621-x.

Maringer, Dietmar, Craig, Ben R. and Paterlini, Sandra (2019) ‘Recreating Banking Networks under Decreasing Fixed Costs’, RDB of Cleveland Working Paper. Federal Reserve Bank of Cleveland (RDB of Cleveland Working Paper, 21). Available at: https://doi.org/10.2139/ssrn.3485745.

Deininger, Sebastian and Maringer, Dietmar (2017) ‘Channels of Sovereign Risk Spillovers and Investment in the Manufacturing Sector’, WWZ Working Papers. WWZ, University of Basel (WWZ Working Papers, 07).

Deininger, Sebastian and Maringer, Dietmar (2016) ‘Channels of Sovereign Risk Spillovers and Investment in the Manufacturing Sector’. International Finance and Banking Society: International Finance and Banking Society.

Maringer, Dietmar and Deininger, Sebastian H. M. (2016) ‘Selecting and estimating interest rate models with evolutionary methods’, Evolutionary Intelligence, 9(4), pp. 137–151. Available at: https://doi.org/10.1007/s12065-016-0145-2.

Mohler, Lukas, Deininger, Sebastian and Müller, Daniel (2016) ‘Energy Elasticities and the Rebound Effect: A Comprehensive Empirical Analysis’. Bundesamt für Energie.

Oesch, Christian and Maringer, Dietmar (2016) ‘Low-latency liquidity inefficiency strategies’, Quantitative finance, 17(5), pp. 717–727. Available at: https://doi.org/10.1080/14697688.2016.1242765.

James, Jessica et al. (2015) ‘Special Issue of Quantitative Finance on ‘Financial Data Analytics’’, Quantitative finance, pp. 1617–1617. Available at: https://doi.org/10.1080/14697688.2015.1075707.

Maringer, Dietmar, Pohl, Walter and Vanini, Paolo (2015) ‘Structured products: performance, costs, and investments’, White Paper [Preprint]. Swiss Finance Institute (White Paper). Available at: https://doi.org/10.2139/ssrn.2620300.

Zhang, Jin and Maringer, Dietmar (2015) ‘Using a Genetic Algorithm to Improve Recurrent Reinforcement Learning for Equity Trading’, Computational Economics, 47(4), pp. 551–567. Available at: https://doi.org/10.1007/s10614-015-9490-y.

Maringer, Dietmar and Kriete-Dodds, Susan (2015) ‘Overconfidence in the Credit Card Market’, in Diehl, Marting; Alexandrova-Kabadjova, Biliana; Heuver, Richard; Martínez-Jaramillo, Serafín (ed.) Analyzing the Economics of Financial Market Infrastructures. Hershey, PA, USA: IGI Global (Analyzing the Economics of Financial Market Infrastructures), pp. 150–168.

Oesch, Christian and Maringer, Dietmar (2015) ‘A Neutral Mutation Operator in Grammatical Evolution’, in Angelov, P.; Atanassov, K. T.; Doukovska, L.; Hadjiski, M.; Jotsov, V.; Kacprzyk, J.; Kasabov, N.; Sotirov, S.; Szmidt, E.; Zadrożny, S. (ed.) Intelligent System′2014. Cham, Heidelberg, New York, Dordrecht, London: Springer International Publishing (Advances in Intelligent Systems and Computing), pp. 439–449. Available at: https://doi.org/10.1007/978-3-319-11313-5_39.

Maringer, Dietmar and Deininger, Sebastian (2014) ‘Estimating time series models with heuristic methods: the case of economic parity conditions’. International Association for Statistical Computing: International Association for Statistical Computing. Available at: http://web.tecnico.ulisboa.pt/mcasquilho/ist/public/2014compstatBook-of-Abstracts.pdf.

Maringer, Dietmar and Zhang, Jin (2014) ‘Transition Variable Selection for Regime Switching Recurrent Reinforcement Learning’, in IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE: IEEE (IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)). Available at: https://doi.org/10.1109/cifer.2014.6924102.

Oesch, Christian (2014) ‘An agent-based model for market impact’, in IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE: IEEE (IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)). Available at: https://doi.org/10.1109/cifer.2014.6924049.

Zhang, Jin (2014) ‘Automating transition functions: a way to improve trading profits with recurrent reinforcement learning’, in Iliadis, Lazaros; Maglogiannis, Ilias; Papadopolos, Harris (ed.) IFIP Advances in Information and Communication Technology. Springer: Springer (IFIP Advances in Information and Communication Technology). Available at: https://doi.org/10.1007/978-3-662-44654-6.

Zhang, Jin and Maringer, Dietmar (2014) ‘Two Parameter Update Schemes for Recurrent Reinforcement Learning’, in IEEE Congress on Evolutionary Computation (CEC). IEEE: IEEE (IEEE Congress on Evolutionary Computation (CEC)). Available at: https://doi.org/10.1109/cec.2014.6900330.

Deininger, Sebastian, Mohler, Lukas and Müller, Daniel (2013) ‘Estimating factor substitution elasticities in Swiss manufacturing’. International Association for Energy Economics: International Association for Energy Economics.

Oesch, Christian and Maringer, Dietmar (2013) ‘Portfolio optimization under market impact costs’. IEEE: IEEE. Available at: https://doi.org/10.1109/cec.2013.6557546.

Zhang, Jin and Maringer, Dietmar (2013) ‘Indicator selection for daily equity trading with recurrent reinforcement learning’. ACM: ACM. Available at: https://doi.org/10.1145/2464576.2480773.

Brabazon, Anthony, O’Neill, Michael and Maringer, Dietmar (2012) Natural Computing in Computational Finance (Vol. 1 (2008) ; Vol. 2 (2009) ; Vol. 3 (2010) ; Vol. 4 (2012)), Studies in Computational Intelligence. Berlin: Springer (Studies in Computational Intelligence). Available at: https://doi.org/10.1007/978-3-540-77477-8.

Kriete-Dodds, Susan and Maringer, Dietmar (2012) ‘Subscription markets: an agent-based approach’. Selbstverl. des Fachbereichs Geographie und Geologie der Univ. Salzburg: Selbstverl. des Fachbereichs Geographie und Geologie der Univ. Salzburg.

Maringer, Dietmar, Paterlini, Sandra and Winker, Peter (2012) ‘Editorial: The 3rd Special Issue on Optimization Heuristics in Estimation’, Computational statistics & data analysis, pp. 2963–2964. Available at: https://doi.org/10.1016/j.csda.2012.05.006.

Maringer, Dietmar and Ramtohul, Tikesh (2012) ‘Regime-switching recurrent reinforcement learning for investment decision making’, Computational Management Science, 9(1), pp. 89–107. Available at: https://doi.org/10.1007/s10287-011-0131-1.

Zhang, J. and Maringer, D. (2011) ‘Distributing weights under hierarchical clustering: A way in reducing performance breakdown’, Expert Systems with Applications, 38(12), pp. 14952–14959. Available at: https://doi.org/10.1016/j.eswa.2011.05.052.

Chen, XiaoHua and Maringer, Dietmar (2011) ‘Detecting time-variation in corporate bond index returns’, Journal of Banking and Finance, 35(1), pp. 95–103. Available at: https://doi.org/10.1016/j.jbankfin.2010.07.023.

Gilli, Manfred, Maringer, Dietmar and Schumann, Enrico (2011) Numerical Methods and Optimization in Finance. Amsterdam: Elsevier.

Lengwiler, Yvan and Maringer, Dietmar (2011) ‘Autonomously Interacting Banks’, WWZ Discussion Papers. WWZ (WWZ Discussion Papers, 07). Available at: http://wwz.unibas.ch/uploads/tx_x4epublication/Interacting-Banks_01.pdf.

Maringer, Dietmar and Ramtohul, Tikesh (2011) ‘GP-based rebalancing triggers for the CPPI’, in Symposium Series on Computational Intelligence. IEEE: IEEE (Symposium Series on Computational Intelligence). Available at: https://doi.org/10.1109/cifer.2011.5953561.

Zhang, Jin and Maringer, Dietmar (2011) ‘Selecting pair-copulas with downside risk minimisation’, Journal of Financial Markets and Derivatives, 2(1-2), pp. 121–48. Available at: https://doi.org/10.1504/ijfmd.2011.038532.

Maringer, Dietmar and Ramtohul, Tikesh (2011) ‘Regime-switching recurrent reinforcement learning in automated trading’, in Brabazon, Anthony; O’Neill, Michael; Maringer, Dietmar (ed.) Natural Computing in Computational Finance. Berlin: Springer-Verlag (Studies in Computational Intelligence), pp. 93–121. Available at: https://doi.org/10.1007/978-3-642-23336-4_6.

Maringer, Dietmar and Ramtohul, Tikesh (2010) ‘Threshold recurrent reinforcement learning model for automated trading’, in DiChio, C; Brabazon, A; DiCaro, GA; Ebner, M; Farooq, M; Fink, A; Grahl, J; Greenfield, G; Machado, P; ONeill, M; Tarantino, E; Urquhart, N (ed.) Lecture Notes in Computer Science. Springer: Springer (Lecture Notes in Computer Science). Available at: https://doi.org/10.1007/978-3-642-12242-2_22.

Zhang, Qingfu et al. (2010) ‘MOEA/D with NBI-like Tchebycheff approach for Portfolio Management’, in IEEE (ed.). IEEE: IEEE. Available at: https://doi.org/10.1109/cec.2010.5586185.

Maringer, Dietmar and Zhang, Jin (2010) ‘A clustering application in portfolio management’, in Ao, S.-I.; Geman, L. (ed.) Electronic engineering and computing technology. Dordrecht: Springer (Electronic engineering and computing technology), p. S. 309–321. Available at: https://doi.org/10.1007/978-90-481-8776-8_27.

Saks, Philip and Maringer, Dietmar (2010) ‘Evolutionary money management’, in Natural Computing in Computational Finance. New York: Springer (Studies in Computational Intelligence), pp. 169–190. Available at: https://doi.org/10.1007/978-3-642-13950-5_10.

Zhang, Jin and Maringer, Dietmar (2010) ‘Index Mutual Fund Replication’, in Natural Computing in Computational Finance. New York: Springer (Studies in Computational Intelligence), pp. 109–130. Available at: https://doi.org/10.1007/978-3-642-13950-5_7.

di Tollo, G. and Maringer, D. (2009) ‘Metaheuristics for the Index Tracking Problem’, Lecture Notes in Economics and Mathematical Systems, 624, pp. 127–154. Available at: https://doi.org/10.1007/978-3-642-00939-6_8.

Maringer, Dietmar (2009) ‘Kontroverse um das Datamining’, ICT in Finance, 1 January, pp. 40–42.

Maringer, Dietmar and Parpas, Panos (2009) ‘Global optimization of higher moments in portfolio selection’, Journal for Global Optimization, 43(2-3), pp. 219–230. Available at: https://doi.org/10.1007/s10898-007-9224-3.

Saks, Philip and Maringer, Dietmar (2009) ‘Evolutionary Money Management’, in Giacobini, M. et al. (ed.) Lecture Notes in Computer Science. Springer: Springer (Lecture Notes in Computer Science). Available at: https://doi.org/10.1007/978-3-642-01129-0_20.

Winker, Peter and Maringer, Dietmar (2009) ‘The convergence of estimators based on heuristics : theory and application to a GARCH model’, Computational statistics, 24(3), pp. 533–550. Available at: https://doi.org/10.1007/s00180-008-0145-5.

Zhang, Jin and Maringer, Dietmar (2009) ‘Improving Sharpe Ratios and Stability of Portfolios by Using a Clustering Technique’, in World Congress on Engineering (ed.). IAENG: IAENG. Available at: http://www.iaeng.org/publication/WCE2009/WCE2009_pp1-6.pdf.

di Tollo, Giacomo and Maringer, Dietmar (2009) ‘Metaheuristics for index tracking’, in Geiger, MJ; Habenicht, W; Sevaux, M; Sorensen, K (ed.) Metaheuristics in the service industry. Berlin: Springer (Lecture notes in economics and mathematical systems), p. S. 127–154. Available at: https://doi.org/10.1007/978-3-642-00939-6.

Maringer, Dietmar (2009) ‘Constrained index tracking under loss aversion using differential evolution’, in Natural Computing in Computational Finance. Dordrecht: Springer (Studies in computational intelligence), p. S. 7–24.

Saks, Philip and Maringer, Dietmar (2009) ‘Statistical Arbitrage with Genetic Programming’, in Natural Computing in Computational Finance. Berlin: Springer (Studies in computational intelligence), p. S. 9–29. Available at: https://doi.org/10.1007/978-3-540-95974-8_2.

Maringer, D. (2008) ‘Constrained index tracking under loss aversion using differential evolution’, Studies in Computational Intelligence, 100, pp. 7–24. Available at: https://doi.org/10.1007/978-3-540-77477-8_2.

Maringer, D.G. and Meyer, M. (2008) ‘Smooth transition autoregressive models - New approaches to the model selection problem’, Studies in Nonlinear Dynamics and Econometrics, 12(1). Available at: https://doi.org/10.2202/1558-3708.1469.

Khuman, Anil, Maringer, Dietmar and Constantinou, Nick (2008) ‘Constant Proportion Portfolio Insurance (CPPI) : Statistical Properties and Practical Implications’. [University of Essex]. Available at: http://www.essex.ac.uk/ccfea/research/WorkingPapers/2008/23-08_KhumanMaringerConstantinou_CPPI.pdf.

Maringer, Dietmar (2008) ‘Heuristic optimization for portfolio management’, IEEE Computational Intelligence Magazine, 3(4), pp. 31–34. Available at: https://doi.org/10.1109/mci.2008.929847.

Saks, Philip and Maringer, Dietmar (2008) ‘Genetic Programming in Statistical Arbitrage’, in Giacobini, M et al. (ed.) Lecture Notes in Computer Science. Springer: Springer (Lecture Notes in Computer Science). Available at: https://doi.org/10.1007/978-3-540-78761-7_8.

Gilli, Manfred, Maringer, Dietmar and Winker, Peter (2008) ‘Applications of Heuristics in Finance’, in Handbook on information technology in finance. Berlin: Springer (International handbooks on information systems), p. S. 635–654. Available at: https://doi.org/10.1007/978-3-540-49487-4.

Maringer, Dietmar (2008) ‘Risk preferences and loss aversion in portfolio optimization’, in Computational Methods in Financial Engineering. Heidelberg: Springer (Computational Methods in Financial Engineering), p. S. 27–46. Available at: https://doi.org/10.1007/978-3-540-77958-2.

Maringer, Dietmar (2005) Portfolio Management With Heuristic Optimization, Advances in Computational Management Science. Dordrecht, Netherlands: Springer (Advances in Computational Management Science). Available at: https://doi.org/10.1007/b136219.