Mikhail has joined the Economics Discipline Group in January 2012. He has received his PhD in economics from the Sant'Anna School of Advanced Studies (Pisa, Italy) in 2005 and has held an academic position at the University of Amsterdam.
Mikhail's research interest is in the area of bounded rationality, where he uses a mixture of analytical, simulation and experimental methods. In particular, he has worked and published on the issue of wealth evolution in the models with heterogeneous expectations bringing together the fields of Heterogeneous Agent Models and evolutionary finance. He has also worked on fitting the evolutionary model with heterogeneous expectations to the experimental data, on behavioral models of continuous double auction, and on monetary policy under heterogeneous expectations.
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- Associate editor of the Journal of Economic Dynamics and Control
- Editor of the special issue of the Journal of Economic Dynamics and Control on "Complexity in economics and finance"
Can supervise: YES
- Models of bounded rationality
- Individual and social learning
- Heterogeneous agent models
- Evolutionary finance
- Agent-based modelling
- Experimental economics
- 23941, Mathematics for Economists (PdD subject)
Previous teaching at UTS:
Other teaching areas (University of Amsterdam, European University at St.Petersburg):
- Bounded rationality
- Heterogeneous Agent Models
- Nonlinear economic dynamics
© 2018 Elsevier B.V. The paper studies an oligopoly game, where firms can choose between price-taking and price-making strategies. On a mixed market price takers are always better off than price makers, though the profits of both types decline in the number of price takers. We investigate and confront two possibilities of firms' decisions about their types: forward-looking equilibrium reasoning and backward-looking individual learning. We find that the Cournot outcome is the only equilibrium prediction and it is learnable if firms are sufficiently sensitive to profit differences. However, with a larger number of firms, a unilateral deviation from Cournot behavior becomes profitable. Under learning this incentive creates a space for permanent oscillations over different markets with a positive but low number of price takers.
Anufriev, M, Chernulich, A & Tuinstra, J 2018, 'A laboratory experiment on the heuristic switching model', Journal of Economic Dynamics and Control, vol. 91, pp. 21-42.View/Download from: UTS OPUS or Publisher's site
© 2018 Elsevier B.V. We present results from the first laboratory experiment on the seminal heuristic switching model introduced by Brock and Hommes (1997, 1998). Subjects choose between two alternatives, a sophisticated and stabilizing, but costly, heuristic, and a destabilizing, but cheap, heuristic, and are paid according to the performance of the chosen heuristic. Aggregate choices determine the evolution of a state variable and, consequently, the performance of both heuristics. Theoretically, an increase in the costs for the stabilizing heuristic generates instability and leads to endogenous fluctuations in both the state variable and the fraction of agents using that heuristic. We vary the costs of the stabilizing heuristic in the experiment and find that the predictions of the heuristic switching model are partially confirmed. For low costs the dynamics are stable. For high costs, the dynamics initially are unstable and exhibit the type of bubbles and crashes emblematic for the heuristic switching model. However, over time the pattern of bubbles and crashes disappears and the dynamics become more stable. We estimate a standard discrete choice model on aggregate choice data and observe that subjects have a tendency to become less sensitive to payoff differences when the environment is less stable, which has important implications for the application of heuristic switching models.
van de Leur, M & Anufriev, M 2018, 'Timing under individual evolutionary learning in a continuous double auction', Journal of Evolutionary Economics, vol. 28, no. 3, pp. 609-631.View/Download from: UTS OPUS or Publisher's site
© 2017 Springer-Verlag GmbH Germany The moment of order submission plays an important role for the trading outcome in a Continuous Double Auction; submitting an offer at the beginning of the trading period may yield a lower profit, as the trade is likely to be settled at the own offered price, whereas late offers result in a lower probability of trading. This timing problem makes the order submission strategy more difficult. We extend the behavioral model of Individual Evolutionary Learning to incorporate the timing problem and study the limiting distribution of submission moments and the resulting offer function that maps submission moments to offers. We find that traders submit different offers at different submission moments the distribution of which uni-modal with a peak moving from late to early as the market size increases. This behavior exacerbates efficiency loss from learning. If traders evaluate profitability of their strategies over longer history, orders are submitted later with the same effect of market size.
Anufriev, M., Bao, T. & Tuinstra, J. 2016, 'Microfoundations for switching behavior in heterogeneous agent models: An experiment', Journal of Economic Behavior and Organization, vol. 129, pp. 74-99.View/Download from: UTS OPUS or Publisher's site
© 2016 Elsevier B.V. We run a laboratory experiment to study how human subjects switch between several profitable alternatives, framed as mutual funds, in order to provide a microfoundation for so-called heterogeneous agent models. The participants in our experiment have to choose repeatedly between two, three or four experimental funds. The time series of fund returns are exogenously generated prior to the experiment and participants are paid for each period according to the return of the fund they choose. For most cases participants' decisions can be successfully described by a discrete choice switching model, often applied in heterogeneous agent models, provided that a predisposition toward one of the funds is included. The estimated intensity of choice parameter of the discrete choice model depends on the structure of the fund returns. In particular, it increases with correlation between past and future returns. This suggests human subjects do not myopically chase past returns, but are more likely to do so when past returns are more predictive of future returns, a feature that is absent in the standard heterogeneous agent models.
Anufriev, M & Panchenko, V 2015, 'Connecting the dots: Econometric methods for uncovering networks with an application to the Australian financial institutions', Journal of Banking and Finance, vol. 61, no. Supp 2, pp. S241-S255.View/Download from: UTS OPUS or Publisher's site
© 2015 Elsevier B.V. This paper connects variance-covariance estimation methods, Gaussian graphical models, and the growing literature on economic and financial networks. We construct the network using the concept of partial correlations which captures direct linear dependence between any two entities, conditional on dependence between all other entities. We relate the centrality measures of this network to shock propagation. The methodology is applied to construct the perceived network of publicly traded Australian banks and their connections to domestic economic sectors and international markets. We find strong links between the big four Australian banks, real estate and other sectors of the economy, and determine which entities play a central role in transmitting and absorbing the shocks.
Anufriev, M, Arifovic, J, Ledyard, J & Panchenko, V 2013, 'Efficiency of continuous double auctions under individual evolutionary learning with full or limited information', Journal of Evolutionary Economics, vol. 23, no. 3, pp. 539-573.View/Download from: UTS OPUS or Publisher's site
In this paper we explore how specific aspects of market transparency and agents½ behavior affect the efficiency of the market outcome. In particular, we are interested whether learning behavior with and without information about actions of other participants improves market efficiency. We consider a simple market for a homogeneous good populated by buyers and sellers. The valuations of the buyers and the costs of the sellers are given exogenously. Agents are involved in consecutive trading sessions, which are organized as a continuous double auction with order book. Using Individual Evolutionary Learning agents submit price bids and offers, trying to learn the most profitable strategy by looking at their realized and counterfactual or ½foregone½ payoffs. We find that learning outcomes heavily depend on information treatments. Under full information about actions of others, agents½ orders tend to be similar, while under limited information agents tend to submit their valuations/costs. This behavioral outcome results in higher price volatility for the latter treatment. We also find that learning improves allocative efficiency when compared to outcomes with Zero-Intelligent traders.
Anufriev, M, Assenza, T, Hommes, C & Massaro, D 2013, 'Interest rate rules and macroeconomic stability under heterogeneous expectations', Macroeconomic Dynamics, vol. 17, no. 8, pp. 1574-1604.View/Download from: UTS OPUS or Publisher's site
The recent macroeconomic literature stresses the importance of managing heterogeneous expectations in the formulation of monetary policy. We use a simple frictionless dynamic stochastic general equilibrium (DSGE) model to investigate in?ation dynamics under alternative interest rate rules when agents have heterogeneous expectations, and update their beliefs based on past performance, as in Brock and Hommes [Econometrica 65(5), 1059½1095 (1997)]. The stabilizing effect of different monetary policies depends on the ecology of forecasting rules (i.e., the composition of the set of predictors), on agents½ sensitivity to differences in forecasting performance, and on how aggressively the monetary authority sets the nominal interest rate in response to in?ation. In particular, if the monetary authority responds only weakly to in?ation, a cumulative process with rising in?ation is likely. On the other hand, a Taylor interest rate rule that sets the interest rate more than point for point in response to in?ation stabilizes in?ation dynamics, but does not always lead the system to converge to the rational expectations equilibrium, as multiple equilibria may persist
Anufriev, M, Hommes, C & Philipse, R 2013, 'Evolutionary selection of expectations in positive and negative feedback markets', Journal of Evolutionary Economics, vol. 23, no. 3, pp. 663-688.View/Download from: UTS OPUS or Publisher's site
An economic environment is a feedback system, where the dynamics of aggregate variables depend on individual expectations and vice versa. The type of feedback mechanism is crucial for the aggregate outcome. Experiments with human subjects (Heemeijer et al., J Econ Dyn Control 33:1052-1072, 2009) have shown that price converges to the fundamental level in a negative feedback environment but fails to do so under positive feedback. We present an explanation of these experimental results by means of a model of evolutionary switching between heuristics. Active heuristics are chosen endogenously, on the basis of their past performance. Under negative feedback an adaptive heuristic dominates explaining fast price convergence, whereas under positive feedback a trend-following heuristic dominates resulting in persistent price deviations and oscillations.
Anufriev, M, Kopanyi, D & Tuinstra, J 2013, 'Learning cycles in Bertrand competition with differentiated commodities and competing learning rules', Journal of Economic Dynamics and Control, vol. 37, no. 12, pp. 2562-2581.View/Download from: UTS OPUS or Publisher's site
This paper stresses the importance of heterogeneity in learning. We consider a Bertrand oligopoly with firms using either least squares learning or gradient learning for determining the price. We demonstrate that convergence properties of the rules are strongly affected by heterogeneity. In particular, gradient learning may become unstable as the number of gradient learners increases. Endogenous choice between the learning rules may induce cyclical switching. Stable gradient learning gives higher average profit than least squares learning, making firms switch to gradient learning. This can destabilize gradient learning which, because of decreasing profits, makes firms switch back to least squares learning.
Anufriev, M. & Tuinstra, J. 2013, 'The impact of short-selling constraints on financial marketstability in a heterogeneous agents model', Journal of Economic Dynamics and Control, vol. 37, no. 8, pp. 1523-1543.View/Download from: UTS OPUS or Publisher's site
Recent turmoil on global financial markets has led to a discussion on which policy measures should or could be taken to stabilize financial markets. One such a measure that resurfaced is the imposition of short-selling constraints. It is conjectured that these short-selling constraints reduce speculative trading and thereby have the potential to stabilize volatile financial markets. The purpose of the current paper is to investigate this conjecture in a standard asset pricing model with heterogeneous beliefs. We model short-selling constraints by imposing trading costs for selling an asset short. We find that the local stability properties of the fundamental rational expectations equilibrium do not change when trading costs for short-selling are introduced. However, when the asset is overvalued, costs for short-selling increase mispricing and price volatility.
Anufriev, M & Hommes, C 2012, 'Evolutionary selection of individual expectations and aggregate outcomes in asset pricing experiments', American Economic Journal. Microeconomics, vol. 4, no. 4, pp. 35-64.View/Download from: UTS OPUS or Publisher's site
In recent "learning to forecast" experiments (Hommes et al. 2005), three different patterns in aggregate price behavior have been observed: slow monotonic convergence, permanent oscillations, and dampened fluctuations. We show that a simple model of individual learning can explain these different aggregate outcomes within the same experimental setting. The key idea is evolutionary selection among heterogeneous expectation rules, driven by their relative performance. The out-of-sample predictive power of our switching model is higher compared to the rational or other homogeneous expectations benchmarks. Our results show that heterogeneity in expectations is crucial to describe individual forecasting and aggregate price behavior. (JEL C53, C91, D83, D84, G12)
Anufriev, M. & Bottazzi, G. 2012, 'Asset pricing with heterogeneous investment horizons', Studies in NonLinear Dynamics and Econometrics, vol. 16, no. 4, pp. 1-36.View/Download from: UTS OPUS or Publisher's site
We consider an analytically tractable asset pricing model describing the trading activity in a stylized market with two assets. Traders are boundedly rational expected utility maximizers with different beliefs about future prices and different investment horizons. In particular, we analyze the effects of the latter source of heterogeneity on the dynamics of price. We find that in the case with homogeneous agents, longer investment horizons lead to more stable dynamics. This is not true, however, in the case of a mixed population of traders, when the increase of heterogeneity in the investment horizons can introduce instability in the system. Furthermore, the role of heterogeneity turns out to be different for different trading behaviors and its effect on the aggregate dynamics depends on the whole ecology of agents' beliefs.
The time evolution of aggregate economic variables, such as stock prices, is affected by market expectations of individual investors. Neoclassical economic theory assumes that individuals form expectations rationally, thus forcing prices to track economic fundamentals and leading to an efficient allocation of resources. However, laboratory experiments with human subjects have shown that individuals do not behave fully rationally but instead follow simple heuristics. In laboratory markets, prices may show persistent deviations from fundamentals similar to the large swings observed in real stock prices. Here we show that evolutionary selection among simple forecasting heuristics can explain coordination of individual behavior, leading to three different aggregate outcomes observed in recent laboratory market-forecasting experiments: slow monotonic price convergence, oscillatory dampened price fluctuations, and persistent price oscillations. In our model, forecasting strategies are selected every period from a small population of plausible heuristics, such as adaptive expectations and trend-following rules. Individuals adapt their strategies over time, based on the relative forecasting performance of the heuristics. As a result, the evolutionary switching mechanism exhibits path dependence and matches individual forecasting behavior as well as aggregate market outcomes in the experiments. Our results are in line with recent work on agent-based models of interaction and contribute to a behavioral explanation of universal features of financial markets.
Anufriev, M., Bottazzi, G., Marsili, M. & Pin, P. 2012, 'Excess covariance and dynamic instability in a multi-asset model', Journal of Economic Dynamics and Control, vol. 36, no. 8, pp. 1142-1161.View/Download from: UTS OPUS or Publisher's site
The presence of excess covariance in financial price returns is an accepted empirical fact: the price dynamics of financial assets tend to be more correlated than their fundamentals would justify. We advance an explanation of this fact based on an intertemporal equilibrium multi-assets model of financial markets with an explicit and endogenous price dynamics. The market is driven by an exogenous stochastic process of dividend yields paid by the assets that we identify as market fundamentals. The model is rather flexible and allows for the coexistence of different trading strategies. The evolution of assets price and traders' wealth is described by a high-dimensional stochastic dynamical system. We identify the equilibria of the model consistent with a baseline assumption of procedural rationality. We show that these equilibria are characterized by excess covariance in prices with respect to the dividend process. Moreover, we show that in equilibrium there is a positive expected marginal profit in choosing more risky portfolios. As a consequence, the evolutionary pressure generates a trend towards more remunerative strategies, which, in turn, increase the variance of prices and the dynamic instability of the system.
Anufriev, M & Dindo, P 2010, 'Wealth-driven selection in a financial market with heterogeneous agents', Journal of Economic Behavior and Organization, vol. 73, no. 3, pp. 327-358.View/Download from: UTS OPUS or Publisher's site
We study the co-evolution of asset prices and individual wealth in a financial market with an arbitrary number of heterogeneous boundedly rational investors. Using wealth dynamics as a selection device we are able to characterize the long run market outcomes, i.e., asset returns and wealth distributions, for a general class of competing investment behaviors. Our investigation illustrates that market interaction and wealth dynamics pose certain limits on the outcome of agents' interactions even within the "wilderness of bounded rationality". As an application we consider the case of heterogeneous mean-variance optimizers and provide insights into the results of the simulation model introduced by Levy, Levy and Solomon (1994).
We analyze the endogenous price formation mechanism of a pure exchange economy with two assets, riskless and risky. The economy is populated by an arbitrarily large number of traders whose investment choices are described by means of generic smooth functions of past realizations. These choices can be consistent with (but not limited to) the solutions of expected utility maximization problems. Under the assumption that individual demand for the risky asset is expressed as a fraction of individual wealth, we derive a complete characterization of equilibria. It is shown that irrespectively of the number of agents and of their behavior, all possible equilibria belong to a one-dimensional "Equilibrium Market Curve". This geometric tool helps to illustrate the possibility of different phenomena, as multiple equilibria, and can be used for comparative static analysis. We discuss the relative performances of different strategies and the selection principle governing market dynamics on the basis of the stability analysis of equilibria.
Anufriev, M & Panchenko, V 2009, 'Asset prices, traders' behavior and market design', Journal of Economic Dynamics and Control, vol. 33, no. 5, pp. 1073-1090.View/Download from: UTS OPUS or Publisher's site
The dynamics of a financial market with heterogeneous agents are analyzed under different market architectures. We start with a tractable behavioral model under Walrasian market clearing and simulate it under different trading protocols. The key behavioral feature of the model is the switching by agents between simple forecasting rules on the basis of a fitness measure. By analyzing the dynamics under order-driven protocols we show that the behavioral and structural assumptions of the model are closely intertwined. The high responsiveness of agents to a fitness measure causes excess volatility, but the frictions of the order-driven markets may stabilize the dynamics. We also analyze and compare allocative efficiency and time series properties under different protocols.
Anufriev, M. & Branch, W. 2009, 'Introduction to special issue on complexity in economics and finance', Journal of Economic Dynamics and Control, vol. 33, no. 5, pp. 1019-1022.View/Download from: UTS OPUS or Publisher's site
Anufriev, M. 2008, 'Wealth-driven competition in a speculative financial market: Examples with maximizing agents', Quantitative Finance, vol. 8, no. 4, pp. 363-380.View/Download from: UTS OPUS or Publisher's site
This paper demonstrates how both the quantitative and qualitative results of a general, analytically tractable asset-pricing model in which heterogeneous agents behave consistently with a constant relative risk-aversion assumption can be applied to the special case of optimizing behaviour. The analysis of the asymptotic properties of the market is performed using a geometric approach that allows the visualization of all possible equilibria by means of a simple one-dimensional Equilibrium Market Curve. The case of linear (particularly, mean-variance) investment functions is thoroughly analysed. This analysis highlights the features that are specific to linear investment functions. As a consequence, some previous contributions of the agent-based literature are generalized.
Anufriev, M., Bottazzi, G. & Pancotto, F. 2006, 'Equilibria, stability and asymptotic dominance in a speculative market with heterogeneous traders', Journal of Economic Dynamics and Control, vol. 30, no. 9-10, pp. 1787-1835.View/Download from: UTS OPUS or Publisher's site
We consider a pure exchange economy where one risky and one riskless security are traded in discrete time. Individual demands are expressed as fractions of individual wealth and depend on traders' forecasts about future price movement. Introducing the 'Equilibrium Market Line' as the locus of all possible equilibrium returns, we show that, irrespectively of the number of traders and of their investment behavior, the economy possesses isolated equilibria where a single agent dominates the market and continuous manifolds of equilibria where many agents hold finite wealth shares. Moreover, we prove that no global dominance order relation among strategies can be defined.
Anufriev, M. & Hommes, C. 2008, 'Evolutionary switching between forecasting heuristics: An explanation of the asset-pricing experiment' in Schredelseker, K. & Hauser, F. (eds), Complexity and artificial markets, Springer, Germany, pp. 41-53.View/Download from: UTS OPUS
In this paper we propose an explanation of the findings of a recent laboratory market forecasting experiment. In the experiment the participants were asked to predict prices for 50 periods on the basis of past realizations. Three different aggregate outcomes were observed in an identical environment: slow monotonic price convergence, persistent price oscillations, and oscillatory dampened price fluctuations. Individual predictions exhibited a high degree of coordination, although the individual forecasts were not commonly known. To explain these findings we propose an evolutionary model of reinforcement learning over a set of simple forecasting heuristics. The key element of our model is the switching between heuristics on the basis of their past performance. Simulations show that such evolutionary learning can reproduce the qualitative patterns observed in the experiment.
Anufriev, M. & Bottazzi, G. 2006, 'Noisy trading in the large market limit' in Mathieu, P., Beaufils, B. & Brandouy, O. (eds), Artificial economics: Agent-based methods in finance, game theory and their applications, Springer, Germany, pp. 137-145.
This paper analyzes to what extent and how the trading activity of a group of heterogeneous agents can be described, in the aggregate, as the result of the investment decision of a single "representative" agent. We consider a two-asset pure exchange economy populated by CRRA traders whose individual demands are functions of the past market history. If individual choices are expressed as noisy versions of a common behavior, and the number of agents is large, one can consider the Large Market Limit of the economy and reduce the model to a low-dimensional stochastic system. We investigate the goodness of this approximation under different market conditions and different agents ecologies. The results of the analysis can be used in the study of the general case with an arbitrary number of heterogeneous agents.
Anufriev, M. & Dindo, P. 2006, 'Equilibrium return and agents' survival in a multiperiod asset market: Analytic support of a simulation model' in Charlotte Bruun (ed), Advances in artificial economics: The economy as a complex dynamic system, Springer, Germany, pp. 269-282.View/Download from: UTS OPUS
We provide explanations for the results of the Levy, Levy and Solomon model, a recent simulation model of financial markets. These explanations are based upon mathematical analysis of a dynamic model of a market with an arbitrary number of heterogeneous investors allocating their wealth between two assets. The investors choices are endogenously modeled in a general way and, in particular, consistent with the maximization of an expected utility. We characterize the equilibria of the model and their stability and discuss implications for the market return and agents survival. These implications are in agreement with the results of previous simulations. Thus, our analytic approach allows to explore the robustness of the previous analysis and to expand its spectrum.
Anufriev, M., Bottazzi, G. & Pancotto, F. 2005, 'Speculative equilibria and asymptotic dominance in a market with adaptive CRRA traders', Proceeding of the SPIE Conference: Noise and econophysics and finance, SPIE, Austin, Texas, USA.
Anufriev, M., Deghi, A., Panchenko, V. & Pin, P. 2016, 'A Model of Network Formation for the Overnight Interbank Market'.
Anufriev, M., Bao, T. & Tuinstra, J. 2015, 'Microfoundations for Switching Behavior in Heterogeneous Agent Models: An Experiment'.
We run a laboratory experiment to study how people switch between several profitable alternatives, framed as mutual funds, in order to provide a microfoundation for so-called heterogeneous agent models. The participants in our experiment have to choose repeatedly between two, three or four experimental funds. The time series of fund returns are exogenously generated prior to the experiment and participants are paid for each period according to the return of the fund they choose. For most cases participants' decisions can be successfully described by a discrete choice switching model, often applied in heterogeneous agent models, provided that a predisposition towards one of the funds is included. The estimated intensity of choice parameter of the discrete choice model depends on the structure of the fund returns. In particular, it increases with correlation between past and future returns. This suggests people do not myopically chase past returns, but are more likely to do so when past returns are more predictive of future returns, a feature that is absent in the standard heterogeneous agent models.
Anufriev, M., Bao, T., Sutan, A. & Tuinstra, J. 2015, 'Fee structure, return chasing and mutual fund choice: an experiment'.
We present an experiment that investigates the eect of the fee structure and past returns on mutual fund choice. We nd that subjects pay too little attention to the (periodic and small) operation expenses fee, but
that the more salient front-end load is used as a commitment device and leads to lock-in into one of the funds. In addition we nd that, even when subjects know that future returns are independent of past returns, these past
returns are an important determinant of subjects' investment choices.
Anufriev, M., Hommes, C. & Makarewicz, T. 2015, 'Simple Forecasting Heuristics that Make us Smart: Evidence from Different Market Experiments'.
We study a model in which individual agents use simple linear first order price forecasting rules, adapting them to the complex evolving market environment with a smart Genetic Algorithm optimization procedure.
The novelties are: (1) a parsimonious experimental foundation of individual forecasting behaviour; (2) an explanation of individual and aggregate behavior in four different experimental settings,
(3) improved one-period and 50-period ahead forecasting of lab experiments, and (4) a characterization of the mean, median and empirical distribution of forecasting heuristics. The median of the distribution of GA forecasting
heuristics can be used in designing or validating simple Heuristic Switching Model.
Anufriev, M., Hommes, C.H. & Makarewicz, T.A. 2015, 'Simple Forecasting Heuristics that Make us Smart: Evidence from Different Market Experiments'.
We study a model in which individual agents use simple linear first order price forecasting rules, adapting them to the complex evolving market environment with a smart Genetic Algorithm optimization procedure. The novelties are: (1) a parsimonious experimental foundation of individual forecasting behaviour; (2) an explanation of individual and aggregate behavior in four different experimental settings, (3) improved one-period and 50-period ahead forecasting of lab experiments, and (4) a characterization of the mean, median and empirical distribution of forecasting heuristics. The median of the distribution of GA forecasting heuristics can be used in designing or validating simple Heuristic Switching Model.
Anufriev, M., Kopányiz, D. & Tuinstra, J. 2013, 'Learning Cycles in Bertrand Competition with Differentiated Commodities and Competing Learning Rules'.
This paper stresses the importance of heterogeneity in learning rules. We introduce an evolutionary competition between different learning rules and demonstrate that, though these rules can coexist, their convergence
properties are strongly affected by heterogeneity. We consider a Bertrand oligopoly with differentiated goods. Firms do not have full information about the demand structure and they want to maximize their perceived one-period profit
by applying one of two different learning rules: OLS learning and gradient learning. We analytically show that the stability of gradient learning depends on the distribution of learning rules over firms. In particular, as the number
of gradient learners increases, gradient learning may become unstable. We study evolutionary competition between the learning rules by means of computer simulations and illustrate that this change in stability for gradient learning
may lead to cyclical switching between the rules. Stable gradient learning typically gives higher average profit than OLS learning, making firms switch to gradient learning. This however, destabilizes gradient learning which, because
of decreasing profits, makes firms switch back to OLS learning. This cycle may repeat itself indefinitely.
Anufriev, M., Tuinstra, J. & Bao, T. 2013, 'Fund Choice Behavior and Estimation of Switching Models: An Experiment'.
We run a laboratory experiment that contributes to the finance literature on "return chasing behavior" studying how investors switch between mutual funds driven by past performance of the funds. The subjects in this experiment make discrete choices between several (2, 3 or 4) experimental funds in multiple periods. The time series of funds' profits are exogenously generated prior to the experiment and subjects are paid for that period according to the profit of the fund they choose. The experimental results show that the investment decision can to a large extent be explained by a discrete choice model ("switching model") with a few lags and a predisposition effect. The intensity of choice parameter \beta in the discrete choice model depends on the structure of the profit time series of the funds, and there is no evidence that it is influenced by experience.
Anufriev, M. & Tuinstra, J. 2010, 'The impact of short-selling constraints on financial market stability in a model with heterogeneous agents', Working Paper Series, Center for Nonlinear Dynamics in Economics and Finance.
Working Paper Number: 10-03
Anufriev, M. & Branch, W.A. 2009, 'Introduction to the Journal of Economic Dynamics and Control special issue on Complexity in Economics and Finance'.
Anufriev, M. & Hommes, C. 2009, 'Evolutionary selection of individual expectations and aggregate outcomes', Working Paper Series, Center for Nonlinear Dynamics in Economics and Finance.
Working Paper Number: 09-09 In recent 'learning to forecast' experiments with human subjects (Hommes, et al. 2005), three different patterns in aggregate asset price behavior have been observed: slow monotonic convergence, permanent oscillations and dampened fluctuations. We construct a simple model of individual learning, based on performance based evolutionary selection or reinforcement learning among heterogeneous expectations rules, explaining these different aggregate outcomes. Out-of-sample predictive power of our switching model is higher compared to the rational or other homogeneous expectations benchmarks. Our results show that heterogeneity in expectations is crucial to describe individual forecast- ing behavior as well as aggregate price behavior.
Anufriev, M. & Bottazzi, G., 'Behavioral Consistent Market Equilibria under Procedural Rationality'.
In this paper we analyze a dynamic, asset pricing model where an arbitrary number of heterogeneous, procedurally rational investors divide their wealth between two assets. Both fundamental dividend process and behavior of traders are modeled in a very general way. In particular, agents' choices are described by means of the generic smooth functions defined on a commonly available information set. The choices are consistent with (but not limited to) the solutions of the expected utility maximization problems.
As a natural rest point of the corresponding dynamics we propose the notion of the Behavioral Consistent Equilibria (BCE) where the aggregate dynamics are consistent with the agents' investment choices. We show that provided that the dividend process is given, all possible equilibria of the system can be characterized by means of one-dimensional Equilibrium Market Line (EML). This geometric tool allows to separate the effects of dividend process and agents' behaviors on the aggregate dynamics. Namely, the precise shape of this line depends on the character of the dividend process, but the realized equilibrium, i.e.~a point on the line, is determined by the ecology of agents' behaviors. We argue that the EML can be useful in investigation of the questions of existence, multiplicity and stability of the BCE and provide corresponding examples. The EML also allows to make the comparative static exercises in a framework with heterogeneous agents and discuss the relative performances of different strategies.
The notion of BCE can be considered as a generalization of the Rational Expectations Equilibrium on the framework with heterogeneous traders. It can be, therefore, useful also in other fields of economics where heterogeneity of actors plays an important role for the aggregate outcome
Anufriev, M., Assenza, T., Hommes, C. & Massaro, D., 'Interest Rate Rules and Macroeconomic Stability under Heterogeneous Expectations'.
This discussion paper led to a publication in 'Macroeconomic Dynamics' (2013). Vol. 17(8), pp. 1574-1604.
The recent macroeconomic literature stresses the importance of managing heterogeneous expectations in the formulation of monetary policy. We use a stylized macro model of Howitt (1992) to investigate inflation dynamics under alternative interest rate rules when agents have heterogeneous expectations and update their beliefs based on past performance as in Brock and Hommes (1997). The stabilizing effect of different monetary policies depends on the ecology of forecasting rules, on agents' sensitivity to differences in forecasting performance and on how aggressively the monetary authority sets the nominal interest rate in response to inflation. In particular, if the monetary authority only responds weakly to inflation, a cumulative process with rising inflation is likely. On the other hand, a Taylor interest rate rule that sets the interest rate more than point for point in response to inflation stabilizes inflation dynamics, but does not always lead the system to converge to the rational expectations equilibrium as multiple equilibria may persist, even when a fully rational, but costly, expectations rule is part of the ecology of forecasting strategies.