Estimating Behavioral Agent-Based Models for Financial Markets through Machine Learning Surrogates | ||||
| International Journal of Multidisciplinary Studies on Management, Business, and Economy | ||||
| Volume 5, Issue 1, June 2022, Page 1-13 PDF (706.68 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijmsbe.2022.237797 | ||||
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| Author | ||||
| Heba M. Ezzat | ||||
| 1 Department of Business Administration, Faculty of Business Administration, Economics and Political Science, The British University in Egypt, ElSherouk City, Egypt. 2 Department of Socio-Computing, Faculty of Economics and Political Science, Cairo University, Cairo, Egypt. | ||||
| Abstract | ||||
| Traditional economic assumptions such as rational, representative agents and efficient market hypothesis failed to explain the macro-behavior of financial markets. On the other hand, agent-based approach proves high potentials in modeling bounded rational and heterogeneous micro-behaviors. This approach captures important stylized facts of financial markets. However, the high complexity of estimating agent-based models parameters precludes using these models in the forecasting process. This problem limits the applicability of agent-based models in decision making and policy formulation processes. Thereafter, this research aims at introducing a prospect for estimating agent-based models for financial markets through surrogate modeling approach. Surrogate models are considered as novel parameter estimation method in economics though it is a well-defined method in engineering. Few efforts have been spent to estimate parameters using surrogate models. | ||||
| Keywords | ||||
| Agent; Based Models; Financial Markets; Machine Learning Surrogates | ||||
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