In standard asset pricing theory, investors are assumed to invest directly in financial markets. The role of financial institutions is ignored. The focus in corporate finance is on agency problems. How do you ensure that managers act in shareholders’ interests? There is an inconsistency in assuming that when you give your money to a financial institution there is no agency problem but when you give it to a firm there is. It is argued both areas need to take proper account of the role of financial institutions and markets. Appropriate concepts for analyzing particular situations should be used. 1 DO FINANCIAL INSTITUTIONS MATTER? When I was an assistant professor my view on referees was that nine out of ten of them were complete idiots. They obviously had no idea what my papers were about or they wouldn’t have rejected them. Fortunately the remaining one out of ten was astute and sometimes would actually recommend a revise and resubmit. Over the years I learned where the problem lay and it was not with the referees. By the time I was an editor my opinion on referees had been reversed and I realized how much they could

In many applications it is necessary to use a simple and therefore highly misspecified econometric model as the basis for decision-making. We propose an approach to developing a possibly misspecified econometric model that will be used as the beliefs of an objective expected utility maximiser. A discrepancy between model and ‘truth ’ is introduced that is interpretable as a measure of the model’s value for this decision-maker. Our decision-based approach utilises this discrepancy in estimation, selection, inference and evaluation of parametric or semiparametric models. The methods proposed nest quasilikelihood methods as a special case that arises when model value is measured by the Kullback-Leibler information discrepancy and also provide an econometric approach for developing parametric decision rules (e.g. technical trading rules) with desirable properties. The approach is illustrated and applied in the context of a CARA investor’s decision problem for which analytical, simulation and empirical results suggest it is very effective.

The recent economic crisis highlights the role of financial markets in allowing economic agents, including prominent banks, to speculate on the future returns of different financial assets, such as mortgage-backed securities. This paper introduces a dynamic general equilibrium model with aggregate shocks, potentially incomplete markets and heterogeneous agents to investigate this role of financial markets. In addition to their risk aversion and endowments, agents differ in their beliefs about the future aggregate states of the economy. The difference in beliefs induces them to take large bets under frictionless complete financial markets, which enable agents to leverage their future wealth. Consequently, as hypothesized by Friedman (1953), under complete markets, agents with incorrect beliefs will eventually be driven out of the markets. In this case, they also have no influence on asset prices and real investment in the long run. In contrast, I show that under incomplete markets generated by collateral constraints, agents with heterogeneous (potentially incorrect) beliefs survive in the long run and their speculative activities drive up asset price volatility and real investment volatility permanently. I also show that collateral constraints are always binding even if the supply of collateralizable assets endogenously responds to their price. I use this framework to study the e¤ects of di¤erent types of regulations and the distribution of endowments on leverage, asset price volatility and investment. Lastly, the analytical tools developed in this framework enable me to prove the existence of the recursive equilibrium in Krusell and Smith (1998) with a finite number of types. This has been an open question in the literature.

This paper presents a new agent-based financial market. It is designed to be both simple enough to gain insights into the nature and structure of what is going on at both the agent and macro levels, but remain rich enough to allow for many interesting evolutionary experiments. The model is driven by heterogeneous agents who put varying weights on past information as they design portfolio strategies. It faithfully generates many of the common stylized features of asset markets. It also yields some insights into the dynamics of agent strategies and how they yield market instabilities.

Evolutionary metaphors have been prominent in both economics and finance. They are often used as basic foundations for rational behavior and efficient markets. Theoretically, a mechanism which selects for rational investors actually requires many caveats, and is far from generic. This paper tests wealth based evolution in a simple, stylized agent-based financial market. The setup borrows extensively from current research in finance that considers optimal behavior with some amount of return predictability. The results confirm that with a homogeneous world of log utility investors wealth will converge onto optimal adaptive forecasting parameters. However, in the case of utility functions which differ from log, wealth selection alone converges to parameters which are economically far from the optimal forecast parameters. This serves as a strong reminder that wealth selection and utility maximization are not the same thing. Therefore, suboptimal financial forecasting strategies may be difficult to drive out of a market, and may even do quite well for some time.

This short note compares and contrasts two forms of learning which are present in most agent-based financial markets. First, passive learning refers to a form of “as if rationality ” where wealth accumulates on strategies which have done relatively well. Second, active learning refers to the active switching of agents across strategies. Most heterogeneous agent markets contain some form of both these types of learning. From what we know so far the dynamics of each may be quite different, and may yield a rich and complex joint dynamic.

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Abstract. Using an agent-based multi-asset artificial stock market, we simulate the survival dynamics of investors with different risk preferences. It is found that the survivability of investors is closely related to their risk preferences. Among the eight types of investors considered in this paper, only the CRRA investors with RRA coefficients close to one can survive in the long run. Other types of agents are eventually driven out of the market, including the famous CARA agents and agents who base their decision on the capital asset pricing model. 1

The market selection hypothesis states that, among expected utility maximizers, competitive markets select for agents with correct beliefs. In some economies this holds, while in others it fails. It holds in complete market economies with a common discount factor and bounded aggregate consumption. It can fail when markets are incomplete, when consumption grows too quickly, or when discount factors and beliefs are correlated. These insights have implication for the analysis of the heterogeneous agent stochastic dynamic general equilibrium models common in finance and macroeconomics. 1 “The trading floor is a jungle, ” he went on, “and the guy you end up working for is your jungle leader. Whether you succeed here or not depends on knowing how to survive in the jungle.” Lewis (1989, pp. 39–40.) 1

Milton Friedman argued that irrational traders will consistently lose money, will not survive, and, therefore, cannot influence long-run asset prices. Since his work, survival and price impact have been assumed to be the same. In this paper, we demonstrate that survival and price impact are two independent concepts. The price impact of irrational traders does not rely on their long-run survival, and they can have a significant impact on asset prices even when their wealth becomes negligible. We also show that irrational traders ’ portfolio policies can deviate from their limits long after the price process approaches its long-run limit. MOST NEOCLASSICAL ASSET PRICING MODELS RELY on the assumption that market participants (traders) are rational in the sense that they behave in ways that are consistent with the objective probabilities of the states of the economy (e.g., Radner (1972) and Lucas (1978)). In particular, they maximize expected utilities using the true probabilities of uncertain economic states. This approach is firmly rooted in the tradition of going from the normative to the positive in economics, yet there is mounting evidence that it is not descriptive of the observed behavior of the average market participant (see, e. g., Alpert and Raiffa (1982),