The Santa Fe Artificial Stock Market Re-Examined - Suggested Corrections

This paper rectifies a design problem in the Santa Fe Artificial Stock Market Model. Due to a faulty mutation operator, the resulting bit distribution in the classifier system was systematically upwardly biased, thus suggesting increased levels of technical trading for smaller GA-invocation intervals. The corrected version partly supports the Marimon-Sargent-Hypothesis that adaptive classifier agents in an artificial stock market will always discover the homogeneous rational expectation equilibrium. While agents always find the correct solution of non-bit usage, analyzing the time series data still suggests the existence of two different regimes depending on learning speed. Finally, classifier systems and neural networks as data mining techniques in artificial stock markets are discussed.Asset Pricing; Learning; Financial Time Series; Genetic Algorithms; Classifier Systems; Agent-Based Simulation

MARKET BASED COMPENSATION, TRADING AND LIQUIDITY

This paper examines the role of trading and liquidity in a large competitive market with dispersed heterogenous information on market-based managerial compensation. The paper recognizes the endogenous nature of a firm’s stock price - it is the outcome of self-interested speculative trading motivated by imperfect information about future firm value. Using the stock price as performance measure means bench-marking the manager’s performance against the market’s expectation of that performance. We obtain two main results: first, the degree of market-based compensation is proportional to the market depth, which is a measure of the ease of information trading. Secondly, using the dynamic trading model of Vives (1995) we show that if the investment horizon of informed traders decreases, at equilibrium the managerial e.ort reduces, and the optimal contract prescribes stock-compensation with longer vesting period.

On the Benefit of Nonlinear Control for Robust Logarithmic Growth: Coin Flipping Games as a Demonstration Case

The takeoff point for this letter is the voluminous body of literature addressing recursive betting games with expected logarithmic growth of wealth being the performance criterion. Whereas almost all existing papers involve use of linear feedback, the use of nonlinear control is conspicuously absent. This is epitomized by the large subset of this literature dealing with Kelly Betting. With this as the high-level motivation, we study the potential for use of nonlinear control in this framework. To this end, we consider a “demonstration case” which is one of the simplest scenarios encountered in this line of research: repeated flips of a biased coin with probability of heads p , and even-money payoff on each flip. First, we formulate a new robust nonlinear control problem which we believe is both simple to understand and apropos for dealing with concerns about distributional robustness; i.e., instead of assuming that p is perfectly known as in the case of the classical Kelly formulation, we begin with a bounding set P⊆[0,1] for this probability. Then, we provide a theorem, our main result, which gives a closed-form description of the optimal robust nonlinear controller and a corollary which establishes that it robustly outperforms linear controllers such as those found in the literature. A second, less significant, contribution of this letter bears upon the computability of our solution. For an n-flip game, whereas an admissible controller has 2n−1 parameters, at the optimum only O( n2 ) of them turn out to be distinct. Finally, it is noted that the initial assumptions on payoffs and the use of the uniform distribution on p are made mainly for simplicity of the exposition and compliance with length requirements for a Letter. Accordingly, this letter also includes a new Section with a discussion indicating how these assumptions can be relaxed