Less Than Human: Simple Adaptive Trading Agents for CDA Markets

Information Sciences, 2007

This paper is an editorial guide for the second special issue on Computational Intelligence in economics and finance, which is a continuation of the special issue of Information Sciences, Vol. 170, No.

2011

Abstract This extended abstract summarizes the research presented in Dr. Pardoe's recently-completed Ph. D. thesis [Pardoe 2011]. The thesis considers how adaptive trading agents can take advantage of previous experience (real or simulated) in other markets while remaining robust in the face of novel situations in a new market. Its contributions are at the intersection of machine learning and electronic commerce, with particular focus on transfer learning and fully autonomous trading agents.

Proc. of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology 2005, 2005

This paper aims to determine whether an adaptive agent population performs better than a static population. A static population is evolved on historical equity market data from the DAX-30, split into training and testing segments. An adaptive population is retrained continuously over the most recent available data that becomes available with each passing day. For comparison their performance over the out-of-sample test data is measured. Results obtained indicate a clear superiority of the adaptive over the static approach.

2019

Agent-based artificial financial market generally models the agents' interactions and their impact on the price dynamics and the market stability. We propose to conceive and simulate a new approach of traders' models that adapt their behaviors to the market conditions (stability V.S instability) variation, to prove the dynamic of the price security formation in the financial market. We consider three kind of traders: The rational-adaptive investors who are more fundamentalists during stable regime but dynamically mutate to behavioral and mimetic during crisis regime. The noise traders who are irrationals; they switch between overconfident and loss adverse behaviors; Mimetic traders who adopt a mimetic behavior and follow the most dominant decision in the market. Our experimental results show that the dynamic behavior of the rational-adaptive investors, that become irrational in instability periods, is a relevant determinant of the crisis periods.

CompSciRN: Industry Practical Application (Topic), 2021

I introduce PRZI (Parameterised-Response Zero Intelligence), a new form of zero-intelligence trader intended for use in simulation studies of auction markets. Like Gode & Sunder's classic Zero-Intelligence Constrained (ZIC) trader, PRZI generates quote-prices from a random distribution over some specified domain of discretely-valued allowable quote-prices. Unlike ZIC, which uses a uniform distribution to generate prices, the probability distribution in a PRZI trader is parameterised in such a way that its probability mass function (PMF) is determined by a real-valued control variable s in the range [-1.0, +1.0] that determines the strategy for that trader. When s is zero, a PRZI trader behaves identically to the ZIC strategy, with a flat/rectangular PMF; but when s is close to plus or minus one the PRZI trader's PMF becomes asymptotically maximally skewed to one extreme or the other of the price-range, thereby enabling the PRZI trader to act in the same way as the "Shav...

SSRN Electronic Journal, 2001

We construct a computer simulation of a repeated double-auction market, designed to match those in experimental-market settings with human subjects, to model complex interactions among arti cially-intelligent traders endowed with varying degrees of learning capabilities. In the course of six di erent experimental designs, we i n vestigate a number of features of our agent-based model: the price e ciency of the market, the speed at which prices converge to the rational expectations equilibrium price, the dynamics of the distribution of wealth among the di erent types of AI-agents, trading volume, bid/ask spreads, and other aspects of market dynamics. We are able to replicate several ndings of human-based experimental markets, however, we also nd intriguing di erences between agent-based and human-based experiments.

2008

Simulation experiments are conducted on simple continuous double auction (CDA) markets based on the experimental economics work of Vernon Smith. CDA models within experimental economics usually consist of a sequence of discrete trading periods or "days", with allocations of stock and currency replenished at the start of each day, a situation we call "periodic" replenishment. In our experiments we look at both periodic and continuous-replenishment versions of the CDA. In this we build on the work of Cliff and Preist with human subjects, but we replace human traders with Zero Intelligence Plus (ZIP) trading agents, a minimal algorithm that can produce equilibrating market behaviour in CDA models. Our results indicate that continuous-replenishment (CR) CDA markets are similar to conventional periodic CDA markets in their ability to show equilibration dynamics. Secondly we show that although both models produce the same behaviour of price formation, they are different playing fields, as periodic markets are more efficient over time than their continuous counterparts. We also find, however, that the volume of trade in periodic CDA markets is concentrated in the early period of each trading day, and the market is in this sense inefficient. We look at whether ZIP agents require different parameters for optimal behaviour in each market type, and find that this is indeed the case. Overall, our conclusions mirror earlier findings on the robustness of the CDA, but we stress that a CR-CDA marketplace equilibrates in a different way to a periodic one.

2004

Stock markets strive to provide an ecient trading platform for investors. Trading rules and mechanisms issued to accomplish this dier among stock markets, and are subject to modification over time. Furthermore, market participants assume a broad range of roles and trading strategies. Such vari- ation poses problems to those involved in the study of market dynamics, when developing an artificial

2008

This paper investigates market microstructure in an experimental artificial futures market using the data generated by the submitted trading agents in two laboratory experiments. Since our previous analyses reveal that the price series successfully replicate so-called "stylized facts" in some regards, the aim of this study is to check whether such phenomena are also observed at microlevel. Our empirical results confirm that although there are locally mispricing effects of several trading agents, in most cases the market liquidity improves over the rounds. Future perspectives of agent-based computational finance and experimental economics are also discussed.

Physica A: Statistical Mechanics and its Applications, 2010

Real world markets display power-law features in variables such as price fluctuations in stocks. To further understand market behavior, we have conducted a series of market experiments on our web-based prediction market platform which allows us to reconstruct transaction networks among traders. From these networks, we are able to record the degree of a trader, the size of a community of traders, the transaction time interval among traders and other variables that are of interest. The distributions of all these variables show power-law behavior. On the other hand, agent-based models have been proposed to study the properties of real financial markets. We here study the statistical properties of these agent-based models and compare them with the results from our web-based market experiments. In this work, three agentbased models are studied, namely, zero-intelligence (ZI), zero-intelligenceplus (ZIP) and Gjerstad-Dickhaut (GD). Computer simulations of variables based on these three agent-based models were carried out. We found that although being the most naive agent-based model, ZI indeed best describes the properties observed in real markets. Our study suggests that the basic ingredient to produce the observed properties from real world markets could in fact be the result of a continuously evolving dynamical system with basic features similar to the ZI model.