Beating the market? A mathematical puzzle for market efficiency

The efficient market hypothesis is highly discussed in economic literature. In its strongest form, it states that there are no price trends. When weakening the non-trending assumption to arbitrary short, small, and fully unknown trends, we mathematically prove for a specific class of control-based trading strategies positive expected gains. These strategies are model free, i.e., a trader neither has to think about predictable patterns nor has to estimate market parameters such as the trend’s sign like momentum traders have to do. That means, since the trader does not have to know any trend, even trends too small to find are enough to beat the market. Adjustments for risk and comparisons with buy-and-hold strategies do not satisfactorily solve the problem. In detail, we generalize results from the literature on control-based trading strategies to market settings without specific model assumptions, but with time-varying parameters in discrete and continuous time. We give closed-form formulae for the expected gain as well as the gain’s variance and generalize control-based trading rules to a setting where older information counts less. In addition, we perform an exemplary backtesting study taking transaction costs and bid-ask spreads into account and still observe—on average—positive gains.

     

Panel data estimates of the production function and product and labor market imperfections

Consistent with two models of imperfect competition in the labor market—the efficient bargaining model and the monopsony model—we provide two extensions of a microeconomic version of Hall's framework for estimating price‐cost margins. We show that both product and labor market imperfections generate a wedge between factor elasticities in the production function and their corresponding shares in revenue, which can be characterized by a ‘joint market imperfections parameter’. Using an unbalanced panel of 10,646 French firms in 38 manufacturing industries over the period 1978–2001, we can classify these industries into six different regimes depending on the type of competition in the product and the labor market. By far the most predominant regime is one of imperfect competition in the product market and efficient bargaining in the labor market (IC‐EB), followed by a regime of imperfect competition in the product market and perfect competition or right‐to‐manage bargaining in the labor market (IC‐PR), and by a regime of perfect competition in the product market and monopsony in the labor market (PC‐MO). For each of these three predominant regimes, we assess within‐regime firm differences in the estimated average price‐cost mark‐up and rent sharing or labor supply elasticity parameters, following the Swamy methodology to determine the degree of true firm dispersion. To assess the plausibility of our findings in the case of the dominant regime (IC‐EB), we also relate our industry and firm‐level estimates of price‐cost mark‐up and extent of rent sharing to industry characteristics and firm‐specific variables respectively. Copyright © 2011 John Wiley & Sons, Ltd.     

Real Asset Valuation under Imperfect Competition: Can We Forget About Market Fundamentals?

Real assets are usually valued by computing the stream of profits they can bring to a price‐taking firm in a liquid market. This method ignores market fundamentals by assuming that all the relevant information is included in the spot price. Our article analyses the bias resulting from such an approach when the market is imperfectly competitive. We propose a stylised two‐period model of the natural gas market with no uncertainty, focusing on strategic interactions between two types of oligopolistic players—pure traders and suppliers with downstream customers—who have access to storage. We show that the true value of storage capacity is not the same for traders and for suppliers. Comparing the latter value with the traditional price‐taking valuation reveals a systematic bias that tends to induce underinvestment.     

Volatility clustering and herding agents: does it matter what they observe?

Recent agent-based models have demonstrated that agents’ herding behavior causes volatility clustering in stock markets. We examine economies where agents herd on others, yet they have limited sets of information on other agents to imitate. In particular, we conduct experiments on economies with agents with different levels of information sharing where agents can imitate: (1) the strategies of others but with an error, (2) the strategies of only a fraction of agents, or (3) the strategies of others, but update their parameters only by a proportion. In each experiment we change the likelihood that agents make errors to copy the strategy of others, the fraction of agents to herd, or the proportion of the parameter that agents update, in order to examine the effect of the different degrees of information sharing on volatility clustering. We show that volatility clustering tends to disappear when agents have limited information on the strategies of others, and agents need to imitate the strategy details of others in order to generate the clustered volatility.     

The influence of seller learning and time constraints on sequential bargaining in an artificial perishable goods market

This paper investigates the formation of prices in a perishable goods market where agents bargain repeatedly through pair-wise interactions. After extensive field observations, we chose to focus on two aspects that seem important to actors of this market: the passage of time and update in judgement when gathering information. The main feature of the market is that a seller bargaining with a buyer has incomplete information about buyer's willingness to pay and is not sure how her trading partner will evaluate an offer or compare it with other options. On the other hand, buyers have limited time to look for goods and cannot meet all possible sellers before making a decision. Hence agents cannot calculate the best price to offer but receive information through limited interactions, and use this information to choose their actions.An agent-based model was built to represent a framework that mimics the observed market institution and where agent's possible behaviors and learning was made as consistent as possible with gathered data. Simulations were run, first for sensitivity analysis concerning main parameters, then to test the dependance of agents’ learning to (a) the time buyers can spend on the market and (b) the frequency of update in learning by sellers. To validate the model, features produced by the simulated market are compared to the stylized facts gathered for negotiation about four goods. We reproduce the main features of the data on the dynamics of offers, transaction prices and agents’ behavior during the bargaining phases.     

Zero-intelligence agents looking for a job

We study a simple agent-based model of a decentralized matching market game in which agents (workers or job seekers) make proposals to other agents (firms) in order to be matched to a position within the firm. The aggregate result of agents interactions can be summarised in the form of a Beveridge curve, which determines the relationship between unmatched agents, unemployed job seekers and vacancies in firms. We open the black box of matching technology, by modelling how agents behave (make proposals) according to their information perception. We observe more efficient results—in the form of a downward shift of the Beverage curve in the case of simple zero-intelligent agents. Our comparative statics indicate that market conditions, such as the heterogeneity of agents’ preferences, will also shift the Beveridge curve downwards. Moreover, market thickness affects movement along the Beverage curve. Movement right-down along the curve if there is an increasing number of agents compared to positions within firms. Furthermore, we show that frictions in re-matching, such as commitment to a match, could be another factor shifting the Beveridge curve toward the origin.     

Fat tails and spurious estimation of consumption‐based asset pricing models

The standard generalized method of moments (GMM) estimation of Euler equations in heterogeneous‐agent consumption‐based asset pricing models is inconsistent under fat tails because the GMM criterion is asymptotically random. To illustrate this, we generate asset returns and consumption data from an incomplete‐market dynamic general equilibrium model that is analytically solvable and exhibits power laws in consumption. Monte Carlo experiments suggest that the standard GMM estimation is inconsistent and susceptible to Type II errors (incorrect nonrejection of false models). Estimating an overidentified model by dividing agents into age cohorts appears to mitigate Type I and II errors.     

Adaptive learning in an asymmetric auction: genetic algorithm approach

Agent-based simulations are performed to study adaptive learning in the context of asymmetric first-price auctions. Non-linearity of the Nash equilibrium strategies is used to investigate the effect of task complexity on adaptive learning by varying the degree of approximation the agents can handle. In addition, learning in different information environments is explored. Social learning allows agents to imitate each other’s bidding strategies based on their relative success. Under individual learning agents are limited to their own experience. We observe convergence to steady states near the predicted equilibrium in all cases. The ability to learn non-linear functions helps the agents with a non-linear equilibrium strategy but hurts the agents with an almost linear one. Better information about the opponent population has a relatively modest impact. A larger number of strategies to experiment with and an ability to systematically compare strategies by holding a number of factors constant have a comparatively stronger beneficial effect.     

Simple agent-based dynamical system models for efficient financial markets: Theory and examples

We propose an agent-based framework, based on simple piecewise linear time-invariant continuous-time dynamical systems models, as a means for describing efficient financial markets. We show by examples that many of the common agent-specific trading strategies occurring in the academic literature, including chartists and fundamentalists of various kinds, can be described in the proposed framework. We present definitions for weak and strong market efficiency and provide necessary and sufficient conditions for them to hold. We present minimal examples of strongly and weakly efficient markets to show that these concepts are natural and easy to satisfy in agent-based models, and that the models can reproduce both statistical and behavioral stylized facts of real markets. We provide examples to demonstrate that the framework can be extended for agents with delays in information processing, as well as for agents with time-varying strategies and for nonlinear market impact functions. We also provide a counterexample to show that the proposed market efficiency concepts may require modification in generalizations for nonlinear trading strategies.     

Intergenerational mobility and sample selection in short panels

Using data from the first 11 waves of the BHPS, this paper measures the extent of the selection bias induced by standard coresidence conditions—bias that is expected to be severe in short panels—on measures of intergenerational mobility in occupational prestige. We try to limit the impact of other selection biases, such as those induced by labour market restrictions that are typically imposed in intergenerational mobility studies, by using different measures of socio‐economic status that account for missing labour market information. We stress four main results. First, there is evidence of an underestimation of the true intergenerational elasticity, the extent of which ranges between 12% and 39%. Second, the proposed methods used to correct for the selection bias seem to be unable to attenuate it, except for the propensity score weighting procedure, which performs well in most circumstances. This result is confirmed both under the assumption of missing‐at‐random data as well as under the assumption of not‐missing‐at‐random data. Third, the two previous sets of results (direction and extent of the bias, and differential abilities to correct for it) are also robust when we account for measurement error. Fourth, restricting the sample to a period shorter than the 11 waves under analysis leads to a severe sample selection bias. In the cases when the analysis is limited to eight waves, this bias ranges from about 40% to 65%. Copyright © 2006 John Wiley & Sons, Ltd.     

Impact of high‐growth start‐ups on entrepreneurial environment based on the level of national economic development

In this paper, we examine the relationship between the conditions of the entrepreneurial environment and high‐potential entrepreneurship according to the stage of economic development of the country. A structural equation model was designed based on data from the Global Entrepreneurship Monitor project, which contains information about 62 countries. Our results suggest that economic development and high‐potential entrepreneurship have a greater impact on the entrepreneurial ecosystem than entrepreneurial activity in general—with special effects on government programs, R&D transfer, and access to the domestic market. Notably, the level of influence of sustainable economic development and high‐potential entrepreneurship depends on the level of economic development of the country.     

Chain-Store Pricing Across Local Markets

Chain‐stores now dominate most areas of retailing. While retailers may operate nationally or even internationally, the markets they compete in are largely local. How should they best operate pricing policy in respect of the different markets served—price uniformly across the local markets or on a local basis according to market conditions? We model this by allowing local market differences, with retail markets differing by their size and the number of players present. We show that practising price discrimination is not always best for a chain‐store. Competitive conditions exist under which uniform pricing can raise profits.     

Frankfurt Artificial Stock Market: a microscopic stock market model with heterogeneous interacting agents in small-world communication networks

We study the relationship between communication network topologies, namely the small-world networks introduced by Watts and Strogatz, and the simulation results of an artificial stock market, here the Frankfurt Artificial Stock Market. Heterogeneous interacting agents communicate their success and trading strategy to their nearest neighbors. A process of information diffusion arises through the adaptive behavior of agents when encountering more successful strategies in their direct neighborhood. We will show that an increasing rewiring probability of the small-world network will lead to higher volatility and distortion within our simulation model. It seems probable that the spatial position of traders within a communication network affects the price building process.     

How cultural dimensions,legal systems,and industry affect environmental reporting? Empirical evidence from an international perspective

Past research has paid little attention to the impact of stakeholder engagement, cultural, legal, and industrial contexts on environmental disclosure. Thus, the aim of this paper is to explore how these three institutional factors affect the reporting of environmental information by companies in different countries. This research draws on institutional theory: normative isomorphism, coercive isomorphism, and mimetic isomorphism. This study uses the generalised method of moments procedure. The findings show that the legal system and certain cultural dimensions such as individualism, uncertainty avoidance, long‐term orientation, and indulgence are determinants of voluntary disclosure of environmental information (individualism and indulgence—negatively; uncertainty avoidance and long‐term orientation—positively), particularly when companies belong to industries with high environmental risk.     

A discrete‐choice model with social interactions: with an application to high school teen behavior

We develop an empirical discrete‐choice interaction model with a finite number of agents. We characterize its equilibrium properties—in particular the correspondence between interaction strength, number of agents, and the set of equilibria—and propose to estimate the model by means of simulation methods. In an empirical application, we analyze the individual behavior of high school teenagers in almost 500 school classes from 70 schools. In our baseline model endogenous social interaction effects are strong for behavior closely related to school (truancy), somewhat weaker for behavior partly related to school (smoking, cell phone ownership, and moped ownership) and absent for behavior far away from school (asking parents' permission for purchases). Intra‐gender interactions are generally much stronger than cross‐gender interactions. In a model with school‐specific fixed effects social interaction effects are insignificant, with the exception of intra‐gender interactions for truancy. Copyright © 2007 John Wiley & Sons, Ltd.     

Real indeterminacy of equilibria and manipulability

In this paper, we analyze the indeterminacy of equilibria in financial markets and propose a selection mechanism. We suggest that there is one equilibrium that prevails over the others, as a result of the market power of the agents that some states of nature become monopolists of certain commodities. Given a financial assets model, we define a price game and show the existence of mixed strategies equilibria. Then we purify these equilibria by considering a price game with incomplete information.     

Emergence of anti-coordination through reinforcement learning in generalized minority games

In this paper we propose adaptive strategies to solve coordination failures in a prototype generalized minority game model with a multi-agent, multi-choice environment. We illustrate the model with an application to large scale distributed processing systems with a large number of agents and servers. In our set up, agents are assigned responsibility to complete tasks that require unit time. They request servers to process these tasks. Servers can process only one task at a time. Agents have to choose servers independently and simultaneously, and have access to the outcomes of their own past requests only. Coordination failure occurs if more than one agent simultaneously requests the same server to process tasks at the same time, while other servers remain idle. Since agents are independent, this leads to multiple coordination failures. In this paper, we propose strategies based on reinforcement learning that minimize such coordination failures. We also prove a null result that a large category of probabilistic strategies which attempts to combine information about other agents’ strategies, asymptotically converge to uniformly random choices over the servers.

     

Moment matching versus Bayesian estimation: Backward-looking behaviour in a New-Keynesian baseline model

The paper considers an elementary New-Keynesian three-equation model and compares its Bayesian estimation based on conventional priors to the results from the method of moments (MM), which seeks to match a finite set of the model-generated second moments of inflation, output and the interest rate to their empirical counterparts. It is found that in the Great Inflation (GI) period—though not quite in the Great Moderation (GM)—the two estimations imply a significantly different covariance structure. Regarding the parameters, special emphasis is placed on the degree of backward-looking behaviour in the Phillips curve. While, in line with much of the literature, it plays a minor role in the Bayesian estimations, MM yields values of the price indexation parameter close to or even at its maximal value of unity. For both GI and GM, these results are worth noticing since in (strong or, respectively, weak) contrast to the Bayesian parameters, the covariance matching thus achieved appears rather satisfactory.     

Random encounters and information diffusion about product quality

This paper explores how social interactions among consumers shape markets. In a two-country model, consumers meet and exchange information about the quality of the goods. As information spreads, demand evolves, affecting the prices and quantities manufactured by profit-maximizing firms. We show that market prices with informational frictions reach the duopoly price with full information at the limit. However, this convergence can take different paths depending on the size asymmetry between countries. In particular, when the country producing the low-quality good is relatively large, the single market does not immediately turn into a duopoly and can be temporarily trapped in a situation of price instability where no Nash equilibrium in pure (but only in mixed) strategies exists and prices can fluctuate between their monopoly and duopoly levels. It follows that the classical price-reducing effects of international trade may take longer to appear. In view of an intense globalization process, understanding how social meetings affect market outcomes is critical for understanding the performance of international economic integration.     

Market clearing by maximum entropy in agent models of stock markets

We replace the conventional market clearing process by maximizing the information entropy. At fixed return agents optimize their demands using an utility with a statistical tolerance against deviations from the deterministic maximum of the utility which leads to information entropies for the agents. Interactions are described by coupling the agents to a large system, called ‘market’. The main problem in economic markets is the absence of the analogue of the first law of thermodynamics (energy conservation in physics). We solve this problem by restricting the utilities to be at most quadratic in the demand (ideal gas approximation). Maximizing the sum of agent and market entropy serves to eliminate the unknown properties of the latter. Assuming a stochastic volatility decomposition for the return we derive an expression for the pdf of the return which is in excellent agreement with the daily DAX data. The pdf exhibits a power law behaviour with an integer tail index equal to the number of agent groups. With the assumption of an Ornstein Uhlenbeck model for the risk aversity parameters of the agents the autocorrelation for the absolute return is well described up to time lags of 2.5 years.