We show how a multi-agent simulator can support two important but distinct methods for assessing a trading strategy: Market Replay and Interactive Agent-Based Simulation (IABS). Our solution is important because each method offers strengths and weaknesses that expose or conceal flaws in the subject strategy. A key weakness of Market Replay is that the simulated market does not substantially adapt to or respond to the presence of the experimental strategy. IABS methods provide an artificial market for the experimental strategy using a population of background trading agents. Because the background agents attend to market conditions and current price as part of their strategy, the overall market is responsive to the presence of the experimental strategy. Even so, IABS methods have their own weaknesses, primarily that it is unclear if the market environment they provide is realistic. We describe our approach in detail, and illustrate its use in an example application: The evaluation of market impact for various size orders.
In our studies discussed below, we populate our IABS with hundreds of simple agents, referred to as Zero Intelligence'' or ZI agents. The term agent was coined by @cite_4 to describe a family of automated market participants that submit random bid and ask orders. In this seminal work, two types of agents were considered: ZI-U (unconstrained) agents which place orders entirely at random within fixed extents, and ZI-C (constrained) agents which are prohibited from placing orders that result in an immediate loss. These ZI agents were initially used to demonstrate that the allocative efficiency of a market arises from its structure and not the particular strategy or intelligence of its participants, i.e., individual strategies are subsumed by the market as whole.
@cite_4
2620687831 2018567859 2761043131 2097397187
We present an agent-based model of manipulating prices in financial markets through spoofing: submitting spurious orders to mislead traders who observe the order book. Built around the standard limit-order mechanism, our model captures a complex market environment with combined private and common values, the latter represented by noisy observations upon a dynamic fundamental time series. We consider background agents following two types of trading strategies: zero intelligence (ZI) that ignores the order book and heuristic belief learning (HBL) that exploits the order book to predict price outcomes. By employing an empirical game-theoretic analysis to derive approximate strategic equilibria, we demonstrate the effectiveness of HBL and the usefulness of order book information in a range of non-spoofing environments. We further show that a market with HBL traders is spoofable, in that a spoofer can qualitatively manipulate prices towards its desired direction. After re-equilibrating games with spoofing, we find spoofing generally hurts market surplus and decreases the proportion of HBL. However, HBL's persistence in most environments with spoofing indicates a consistently spoofable market. Our model provides a way to quantify the effect of spoofing on trading behavior and efficiency, and thus measures the profitability and cost of an important form of market manipulation. We report market experiments in which human traders are replaced by "zero-intelligence" programs that submit random bids and offers. Imposing a budget constraint (i.e., not permitting traders to sell below their costs or buy above their values) is sufficient to raise the allocative efficiency of these auctions close to 100 percent. Allocative efficiency of a double auction derives largely from its structure, independent of traders' motivation, intelligence, or learning. Adam Smith's invisible hand may be more powerful than some may have thought; it can generate aggregate rationality not only from individual rationality but also from individual irrationality. Agent-based modeling of zapping behavior of viewers, television commercial allocation, and advertisement markets by Hiroyuki Kyan and Jun-ichi Inoue.- Agent based modeling of Housing asset bubble: A simple utility function based investigation by Kausik Gangopadhyay and Kousik Guhathakurta.- Urn model-based adaptive Multi-arm clinical trials: A stochastic approximation approach by Sophie Laruelle and Gilles Pages.- Logistic modeling of a Religious Sect features by Marcel Ausloos.- Characterizing financial crisis by means of the three states random field Ising model by Mitsuaki Murota and Jun-ichi Inoue.- Themes and applications of kinetic exchange models: Redux by Asim Ghosh, Anindya S. Chakrabarti, Anjan Kumar Chandra and Anirban Chakraborti.- Kinetic exchange opinion model: solution in the single parameter map limit by Krishanu Roy Chowdhury, Asim Ghosh, Soumyajyoti Biswas and Bikas K. Chakrabarti.- An overview of the new frontiers of Economic Complexity by Matthieu Cristelli, Andrea Tacchella, Luciano Pietronero.- Jan Tinbergen's legacy for economic networks: from the gravity model to quantum statistics by Tiziano Squartini and Diego Garlaschelli.- A macroscopic order of consumer demand due to heterogenous consumer behaviors on Japanese household demand tested by the random matrix theory by Yuji Aruka,Yuichi Kichikawa and Hiroshi Iyetomi.- Uncovering the network structure of the world currency market: Cross-correlations in the fluctuations of daily exchange rates by Sitabhra Sinha and Uday Kovur.- Systemic risk in Japanese credit network by Hideaki Aoyama.- Pricing of goods with Bandwagon properties: The curse of coordination by Mirta B. Gordon, Jean-Pierre Nadal, Denis Phan and Viktoriya Semeshenko.- Evolution of Econophysics by Kishore C. Dash.- Econophysics and sociophysics: Problems and prospects by Asim Ghosh and Anindya S. Chakrabarti.- A discussion on Econophysics by Hideaki Aoyama. We introduce a minimal Agent Based Model for financial markets to understand the nature and Self-Organization of the Stylized Facts. The model is minimal in the sense that we try to identify the essential ingredients to reproduce the main most important deviations of price time series from a Random Walk behavior. We focus on four essential ingredients: fundamentalist agents which tend to stabilize the market; chartist agents which induce destabilization; analysis of price behavior for the two strategies; herding behavior which governs the possibility of changing strategy. Bubbles and crashes correspond to situations dominated by chartists, while fundamentalists provide a long time stability (on average). The Stylized Facts are shown to correspond to an intermittent behavior which occurs only for a finite value of the number of agents N. Therefore they correspond to finite size effect which, however, can occur at different time scales. We propose a new mechanism for the Self-Organization of this state which is linked to the existence of a threshold for the agents to be active or not active. The feedback between price fluctuations and number of active agents represent a crucial element for this state of Self-Organized-Intermittency. The model can be easily generalized to consider more realistic variants.