Klein, A. and Urbig, D. and Kirn, S. (2008): Who Drives the Market? Estimating a Heterogeneous Agentbased Financial Market Model Using a Neural Network Approach.
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Abstract
Introduction. The objects of investigation of this work are microlevel behaviors in stock markets. We aim at better understanding which strategies of market participants drive stock markets. The problem is that microlevel data from real stock markets are largely unobservable. We take an estimation perspective to obtain daily time series of fractions of chartists and fundamentalists among market participants. We estimate the heterogeneous agentbased financial market model introduced by Lux and Marchesi [1] to the S&P 500. This model has more realistic time series properties compared to less complex econometric and other agentbased models. Such kinds of models have a rather complex dependency between micro and macro parameters that have to be mapped to empirical data by the estimation method. This poses heavy computational burdens. Our contribution to this field is a new method for indirectly estimating timevarying microparameters of highly complex agentbased models at high frequency.
Related work. Due to the high complexity, few authors have published on this topic to date (e.g., [2], [3], and [4]). Recent approaches in directly estimating agentbased models are restricted to simpler models, make simplifying assumptions on the estimation procedure, estimate only nontime varying parameters, or estimate only low frequency time series.
Approach and computational methods. The indirect estimation method we propose is based on estimating the inverse model of a rich agentbased model that derives realistic macro market behavior from heterogeneous market participants’ behaviors. Applying the inverse model, which maps macro parameters back to micro parameters, to widely available macrolevel financial market data, allows for estimating time series of aggregated real world microlevel strategy data at daily frequency. To estimate the inverse model in the first place, a neural network approach is used, as it allows for a large degree of freedom concerning the structure of the mapping to be represented by the neural network. As basis for learning the mapping, micro and macro time series of the market model are generated artificially using a multiagent simulation based on RePast [5]. After applying several preprocessing and smoothing methods to these time series, a feedforward multilayer perceptron is trained using a variant of the LevenbergMarquardt algorithm combined with Bayesian regularization [6]. Finally, the trained network is applied to the S&P 500 to estimate daily time series of fractions of strategies used by market participants.
Results. The main contribution of this work is a modelfree indirect estimation approach. It allows estimating microparameter time series of the underlying agentbased model of high complexity at high frequency. No simplifying assumptions concerning the model or the estimation process have to be applied. Our results also contribute to the understanding of theoretical models. By investigating fundamental depen¬den¬cies in the Lux and Marchesi model by means of sensitivity analysis of the resulting neural network inverse model, price volatility is found to be a major driver. This provides additional support to findings in [1]. Some face validity for concrete estimation results obtained from the S&P 500 is shown by comparing to results of Boswijk et al. [3]. This is the work which comes closest to our approach, albeit their model is simpler and estimation frequency is yearly. We find support for Boswijk et al.’s key finding of a large fraction of chartists during the end of 1990s price bubble in technology stocks. Eventually, our work contributes to understanding what kind of microlevel behaviors drive stock markets. Analyzing correlations of our estimation results to historic market events, we find the fraction of chartists being large at times of crises, crashes, and bubbles. See also www.whodrivesthemarket.com for some continuously updated results.
Item Type:  MPRA Paper 

Original Title:  Who Drives the Market? Estimating a Heterogeneous Agentbased Financial Market Model Using a Neural Network Approach 
Language:  English 
Keywords:  stock market; heterogeneous agentbased models; indirect estimation; inverse model; trading strategies; chartists; fundamentalists; neural networks 
Subjects:  C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C32  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models G  Financial Economics > G1  General Financial Markets > G12  Asset Pricing ; Trading Volume ; Bond Interest Rates C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C45  Neural Networks and Related Topics C  Mathematical and Quantitative Methods > C8  Data Collection and Data Estimation Methodology ; Computer Programs > C81  Methodology for Collecting, Estimating, and Organizing Microeconomic Data ; Data Access C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General 
Item ID:  14433 
Depositing User:  Achim Klein 
Date Deposited:  04. Apr 2009 16:12 
Last Modified:  20. Feb 2013 21:57 
References:  [1] Lux, T. and M. Marchesi (2000): "Volatility Clustering in Financial Markets: A MicroSimulation of Interacting Agents", International Journal of Theoretical and Applied Finance, 3, 675702. [2] Alfarano, S., F. Wagner and T. Lux (2005): "Estimation of AgentBased Models: The Case of an Asymmetric Herding Model", Computational Economics, 26, 1949. [3] Boswijk, H. P., C. H. Hommes and S. Manzan (2007): "Behavioral Heterogeneity in Stock Prices", Journal of Economic Dynamics and Control, 31(6), June, 19381970. [4] Westerhoff, F. and S. Reitz (2003): "Nonlinearities and Cyclical Behavior: The Role of Chartists and Fundamentalists", Studies in Nonlinear Dynamics & Econometrics, 7(4). [5] Http://repast.sourceforge.net/, REPAST  Recursive Porous Agent Simulation Toolkit, accessed 20080101. [6] Foresee, F. D. and M. T. Hagan (1997): "GaussNewton Approximation to Bayesian Regularization", International Joint Conference on Neural Networks, vol. 3, Piscataway, NJ: IEEE Press, 19301935. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/14433 
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