Araştırma Makalesi
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Ajan Bazlı Finansal Modelleme ve Uygulama Prosedürü

Yıl 2023, , 201 - 214, 25.12.2023
https://doi.org/10.30586/pek.1315128

Öz

Ajan bazlı modelleme (ABM) yöntemi gelişen bilgisayar altyapıları sayesinde giderek daha kompleks ve kullanışlı hale gelmiştir. ABM, kullanım alanlarının çeşitliliği ve modellerin karmaşık sistemler üzerindeki etkinliği nedeniyle araştırmacıların ilgisini çeken bir yöntemdir. ABM ile araştırmacılar orman yangınlarından virüs yayılımlarına hatta sağlık bilimlerine, ekonomilerden yapay piyasalara kadar birçok alanda modelleme yaparak çalışmalarını geliştirebilmişlerdir. Bu çalışmada Ajan Bazlı Finansal Modelleme (ABFM) ve ABFM ile birlikte kullanılan teknikler ele alınmıştır. Bu alandaki erken dönem çalışmalardan olan Genoa Yapay Borsası (GASM - Genoa Artificial Stock Market) ve Santa Fe Enstitüsü tarafından yayımlanmış borsa modeli (SFI-ASM – Santa Fe Instıtute Artificial Stock Market) teknik açıdan anlatılmıştır. Söz konusu çalışmalar ABM literatürünün en bilinen çalışmaları arasında yer aldıklarından bu çalışmada modellemeye örnek olması açısından incelenmiştir

Destekleyen Kurum

Yıldız Teknik Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Proje Numarası

SDK-2021-4302

Kaynakça

  • Agrawal, A., Gans, J.S., & Goldfarb, A. (2019), Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction. Journal of Economic Perspectives, 33(2), 31-50.
  • Alpaydın, E. (2010). Introduction to Machine Learning. MIT Press.
  • Aras, G. (2003). Sermaye piyasalarının gelişmesinde kurumsal yatırımcıların rolü: OECD ülkeleri ve Türkiye örneği. Kurumsal Yatırımcı Yöneticileri Derneği, İstanbul
  • Arifovic, J. (1994). Genetic algorithm learning and the cobweb model. Journal of Economic dynamics and Control, 18(1), 3-28.
  • Bajari, P., Nekipelov, D., Ryan, S. P., Yang M. (2015). Machine Learning Methods for Demand Estimation, Journal American Economic Review, 105(5), 481-485.
  • Binmore, K., & Samuelson, L. (1999). Evolutionary drift and equilibrium selection. The Review of Economic Studies, 66(2), 363-393.
  • Demirtaş, Ö., ve Güngör, Z. (2004). Portföy yönetimi ve portföy seçimine yönelik uygulama. Havacılık ve Uzay Teknolojileri Dergisi, 1(4), 103-109.
  • Demirtaş, Ş. C., & Şahin, S. Ç. (2022 ). Algorithmic trading versus human traders at different information levels. Ulakbilge, 75, 825–835.
  • Drake, Adrian E., Marks, Robert E. (2002). Genetic algorithms in economics and finance: Forecasting stock market prices and foreign exchange- A review, In Shu-Heng Chen (Ed.) Genetic Algorithms and Genetic Programming in Computational Finance, 29-54.
  • Galindo J. & Tamayo P. (2000). Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications, Computational Economics, 15, 107–143.
  • Grimm, C. M., Lee, H., Smith, K. G., & Smith, K. G. (Eds.). (2006). Strategy as action: Competitive dynamics and competitive advantage. Oxford University Press on Demand.
  • Grimm, V., & Railsback, S. F. (2012). Designing, formulating, and communicating agent-based models. In Agent-based models of geographical systems (pp. 361-377). Springer, Dordrecht.
  • Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., & Railsback, S. F. (2010). The ODD protocol: a review and first update. Ecological modelling, 221(23), 2760-2768.
  • Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S. F., Thulke, H.-H., Weiner, J., Wiegand, T., & DeAngelis, D. L. (2005). Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science, 310, 987–991.
  • Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American economic review, 70(3), 393-408.
  • Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66-73.
  • Hommes, C., & LeBaron, B. (Eds.). (2018). Computational economics: heterogeneous agent modeling, Amsterdam & Oxford, Elsevier.
  • Horst, U. (2005). Financial price fluctuations in a stock market model with many interacting agents. Economic Theory, 25(4), 917-932.
  • Kirman, A. (2002). Reflections on interaction and markets. Quantitative Finance, 2(5), 322.
  • Kirman, A. (2010). Complex economics: individual and collective rationality, London & New York, Routledge.
  • LeBaron, B. (2001). A builder's guide to agent-based financial markets, Quantitative Finance, 1(2), 254-261, doi: 10.1088/1469-7688/1/2/307.
  • LeBaron, B. (2002). Building the Santa Fe artificial stock market. Physica A, 1, 20.
  • LeBaron, B. (2006). Agent-based computational finance. In L. Tesfatsion & K. L. Judd (Eds.), Handbook of computational economics (pp. 1187–1233). Amsterdam: Elsevier.
  • LeBaron, B. (2021). Microconsistency in simple empirical agent-based financial models. Computational economics, 58(1), 83-101.
  • Maehara, T., Yabe, A., Kawarabayashi, K. (2015). Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View, Proceedings of the 32 nd International Conference on Machine Learning, Lille, France, 2015. JMLR: W&CP volume 37.
  • Mahfoud S., Mani G. (1996). Financial Forecasting Using Genetic Algorithms, Applied Artificial Intelligence, 10, 543- 565.
  • Mitchell, M. (1998). An introduction to genetic algorithms MIT Press. Cambridge, Massachusetts. London, England.
  • O’Halloran, S., Maskey, S., McAllister, G., Park, D. K., & Chen, K. (2016). Data science and political economy: application to financial regulatory structure. RSF: The Russell Sage Foundation Journal of the Social Sciences, 2(7), 87-109.
  • Pabuçcu, H. (2019). Borsa endeksi hareketlerinin makine öğrenme algoritmaları ile tahmini. Uluslararası iktisadi ve idari incelemeler dergisi, (23), 179-190.
  • Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1), 259-268.
  • Poledna, S., Miess, M. G., Hommes, C., & Rabitsch, K. (2023). Economic forecasting with an agent-based model. European Economic Review, 151, 104306.
  • Railsback, S. F. (2001). Concepts from complex adaptive systems as a framework for individual-based modelling. Ecological modelling, 139(1), 47-62.
  • Railsback, S. F., & Grimm, V. (2019). Agent-based and individual-based modeling: a practical introduction, Princeton & Oxford, Princeton university press.
  • Routledge, B. R. (1999). Adaptive learning in financial markets. The Review of Financial Studies, 12(5), 1165-1202.
  • Schaffer, J. D., Caruana, R., Eshelman, L. J., & Das, R. (1989). A study of control parameters affecting online performance of genetic algorithms for function optimization. In Proceedings of the 3rd international conference on genetic algorithms (pp. 51-60).
  • Shapiro, S. C. (1992). Encyclopedia of artificial intelligence second edition. New Jersey: A Wiley Interscience Publication.
  • Subramanian S., Venugopal M.S., Rao U.S. (2004). Usefulness of Genetic Algorithm Model for Dynamic Portfolio Selection, Journal of Financial Management and Analysis, 17(1), 45-53.
  • Tesfatsion, L., & Judd, K. L. (Eds.). (2006). Handbook of computational economics: agent-based computational economics, Amsterdam & Oxford, Elsevier.
  • Topal Koç, D. (2021). Algoritmik İktisat. İktisat ve Toplum, 110-124.
  • Yu, Q., Miche, Y., Séverin, E., & Lendasse, A. (2014). Bankruptcy prediction using extreme learning machine and financial expertise. Neurocomputing, 128, 296-302.

Agent Based Financial Modeling and Its Application Procedure

Yıl 2023, , 201 - 214, 25.12.2023
https://doi.org/10.30586/pek.1315128

Öz

Agent-based modeling (ABM) method has become increasingly complex and useful thanks to developing computer infrastructures. ABM is a method that attracts the attention of researchers due to the diversity of its uses and the effectiveness of models on complex systems. With ABM, researchers were able to improve their work by modeling in many areas from forest fires to virus spread even health sciences, from economies to artificial markets. In this study, Agent Based Financial Modelling and ABFM and the techniques used together are discussed. The early studies in this field, Genoa Artificial Stock Market (GASM - Genoa Artificial Stock Market) and the stock market model published by Santa Fe Institute (SFI-ASM - Santa Fe Institute Artificial Stock Market) are explained technically. Since these studies are among the best-known studies in the ABM literature, they are examined as an example of modeling in this study

Proje Numarası

SDK-2021-4302

Kaynakça

  • Agrawal, A., Gans, J.S., & Goldfarb, A. (2019), Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction. Journal of Economic Perspectives, 33(2), 31-50.
  • Alpaydın, E. (2010). Introduction to Machine Learning. MIT Press.
  • Aras, G. (2003). Sermaye piyasalarının gelişmesinde kurumsal yatırımcıların rolü: OECD ülkeleri ve Türkiye örneği. Kurumsal Yatırımcı Yöneticileri Derneği, İstanbul
  • Arifovic, J. (1994). Genetic algorithm learning and the cobweb model. Journal of Economic dynamics and Control, 18(1), 3-28.
  • Bajari, P., Nekipelov, D., Ryan, S. P., Yang M. (2015). Machine Learning Methods for Demand Estimation, Journal American Economic Review, 105(5), 481-485.
  • Binmore, K., & Samuelson, L. (1999). Evolutionary drift and equilibrium selection. The Review of Economic Studies, 66(2), 363-393.
  • Demirtaş, Ö., ve Güngör, Z. (2004). Portföy yönetimi ve portföy seçimine yönelik uygulama. Havacılık ve Uzay Teknolojileri Dergisi, 1(4), 103-109.
  • Demirtaş, Ş. C., & Şahin, S. Ç. (2022 ). Algorithmic trading versus human traders at different information levels. Ulakbilge, 75, 825–835.
  • Drake, Adrian E., Marks, Robert E. (2002). Genetic algorithms in economics and finance: Forecasting stock market prices and foreign exchange- A review, In Shu-Heng Chen (Ed.) Genetic Algorithms and Genetic Programming in Computational Finance, 29-54.
  • Galindo J. & Tamayo P. (2000). Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications, Computational Economics, 15, 107–143.
  • Grimm, C. M., Lee, H., Smith, K. G., & Smith, K. G. (Eds.). (2006). Strategy as action: Competitive dynamics and competitive advantage. Oxford University Press on Demand.
  • Grimm, V., & Railsback, S. F. (2012). Designing, formulating, and communicating agent-based models. In Agent-based models of geographical systems (pp. 361-377). Springer, Dordrecht.
  • Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., & Railsback, S. F. (2010). The ODD protocol: a review and first update. Ecological modelling, 221(23), 2760-2768.
  • Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S. F., Thulke, H.-H., Weiner, J., Wiegand, T., & DeAngelis, D. L. (2005). Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science, 310, 987–991.
  • Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American economic review, 70(3), 393-408.
  • Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66-73.
  • Hommes, C., & LeBaron, B. (Eds.). (2018). Computational economics: heterogeneous agent modeling, Amsterdam & Oxford, Elsevier.
  • Horst, U. (2005). Financial price fluctuations in a stock market model with many interacting agents. Economic Theory, 25(4), 917-932.
  • Kirman, A. (2002). Reflections on interaction and markets. Quantitative Finance, 2(5), 322.
  • Kirman, A. (2010). Complex economics: individual and collective rationality, London & New York, Routledge.
  • LeBaron, B. (2001). A builder's guide to agent-based financial markets, Quantitative Finance, 1(2), 254-261, doi: 10.1088/1469-7688/1/2/307.
  • LeBaron, B. (2002). Building the Santa Fe artificial stock market. Physica A, 1, 20.
  • LeBaron, B. (2006). Agent-based computational finance. In L. Tesfatsion & K. L. Judd (Eds.), Handbook of computational economics (pp. 1187–1233). Amsterdam: Elsevier.
  • LeBaron, B. (2021). Microconsistency in simple empirical agent-based financial models. Computational economics, 58(1), 83-101.
  • Maehara, T., Yabe, A., Kawarabayashi, K. (2015). Budget Allocation Problem with Multiple Advertisers: A Game Theoretic View, Proceedings of the 32 nd International Conference on Machine Learning, Lille, France, 2015. JMLR: W&CP volume 37.
  • Mahfoud S., Mani G. (1996). Financial Forecasting Using Genetic Algorithms, Applied Artificial Intelligence, 10, 543- 565.
  • Mitchell, M. (1998). An introduction to genetic algorithms MIT Press. Cambridge, Massachusetts. London, England.
  • O’Halloran, S., Maskey, S., McAllister, G., Park, D. K., & Chen, K. (2016). Data science and political economy: application to financial regulatory structure. RSF: The Russell Sage Foundation Journal of the Social Sciences, 2(7), 87-109.
  • Pabuçcu, H. (2019). Borsa endeksi hareketlerinin makine öğrenme algoritmaları ile tahmini. Uluslararası iktisadi ve idari incelemeler dergisi, (23), 179-190.
  • Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1), 259-268.
  • Poledna, S., Miess, M. G., Hommes, C., & Rabitsch, K. (2023). Economic forecasting with an agent-based model. European Economic Review, 151, 104306.
  • Railsback, S. F. (2001). Concepts from complex adaptive systems as a framework for individual-based modelling. Ecological modelling, 139(1), 47-62.
  • Railsback, S. F., & Grimm, V. (2019). Agent-based and individual-based modeling: a practical introduction, Princeton & Oxford, Princeton university press.
  • Routledge, B. R. (1999). Adaptive learning in financial markets. The Review of Financial Studies, 12(5), 1165-1202.
  • Schaffer, J. D., Caruana, R., Eshelman, L. J., & Das, R. (1989). A study of control parameters affecting online performance of genetic algorithms for function optimization. In Proceedings of the 3rd international conference on genetic algorithms (pp. 51-60).
  • Shapiro, S. C. (1992). Encyclopedia of artificial intelligence second edition. New Jersey: A Wiley Interscience Publication.
  • Subramanian S., Venugopal M.S., Rao U.S. (2004). Usefulness of Genetic Algorithm Model for Dynamic Portfolio Selection, Journal of Financial Management and Analysis, 17(1), 45-53.
  • Tesfatsion, L., & Judd, K. L. (Eds.). (2006). Handbook of computational economics: agent-based computational economics, Amsterdam & Oxford, Elsevier.
  • Topal Koç, D. (2021). Algoritmik İktisat. İktisat ve Toplum, 110-124.
  • Yu, Q., Miche, Y., Séverin, E., & Lendasse, A. (2014). Bankruptcy prediction using extreme learning machine and financial expertise. Neurocomputing, 128, 296-302.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mikro İktisat (Diğer), Finans
Bölüm Makaleler
Yazarlar

Şükrü Can Demirtaş 0000-0002-6953-3599

Senem Çakmak Şahin 0000-0003-3395-9122

Proje Numarası SDK-2021-4302
Yayımlanma Tarihi 25 Aralık 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Demirtaş, Ş. C., & Çakmak Şahin, S. (2023). Ajan Bazlı Finansal Modelleme ve Uygulama Prosedürü. Politik Ekonomik Kuram, 7(2), 201-214. https://doi.org/10.30586/pek.1315128

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