Results 1 - 10
of
24
The Santa Fe Artificial Stock Market Re-Examined -- Suggested Corrections
, 2002
"... 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 interval ..."
Abstract
-
Cited by 7 (0 self)
- Add to MetaCart
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.
Latency Arbitrage, Market Fragmentation, and Efficiency: A Two-Market Model
- 14th ACM Conference on Electronic Commerce
, 2013
"... We study the effect of latency arbitrage on allocative efficiency and liquidity in fragmented financial markets. We propose a simple model of latency arbitrage in which a single security is traded on two exchanges, with aggregate information available to regular traders only after some delay. An inf ..."
Abstract
-
Cited by 3 (1 self)
- Add to MetaCart
(Show Context)
We study the effect of latency arbitrage on allocative efficiency and liquidity in fragmented financial markets. We propose a simple model of latency arbitrage in which a single security is traded on two exchanges, with aggregate information available to regular traders only after some delay. An infinitely fast arbitrageur profits from market fragmentation by reaping the surplus when the two markets diverge due to this latency in cross-market communication. We develop a discrete-event simulation system to capture this processing and information transfer delay, and using an agent-based approach, we simulate the interactions between high-frequency and zero-intelligence trading agents at the millisecond level. We then evaluate allocative efficiency and market liquidity arising from the simulated order streams, and we find that market fragmentation and the presence of a latency arbitrageur reduces total surplus and negatively impacts liquidity. By replacing continuous-time markets with periodic call markets, we eliminate latency arbitrage opportunities and achieve further efficiency gains through the aggregation of orders over short time periods. 1
Asset pricing in a Lucas Framework with Boundedly Rational, Heterogeneous Agents
- Florida State University, Department of Mathematics
, 2007
"... The Office of Graduate Studies has verified and approved the above named committee members. ii I dedicate this dissertation to my parents, whose love and support made my success possible. iii ACKNOWLEDGEMENTS It is difficult to overstate my gratitude to my dissertation advisors, Dr. Paul Beaumont an ..."
Abstract
-
Cited by 3 (1 self)
- Add to MetaCart
The Office of Graduate Studies has verified and approved the above named committee members. ii I dedicate this dissertation to my parents, whose love and support made my success possible. iii ACKNOWLEDGEMENTS It is difficult to overstate my gratitude to my dissertation advisors, Dr. Paul Beaumont and Dr. Alec Kercheval. Most doctoral students consider themselves lucky to have an academic advisor who possesses the intelligence and patience necessary to guide them to the completion of their degree. I was extremely lucky to have two advisors who possess these qualities. In the past three years, Paul and Alec have provided me with the inspiration and support I desperately needed to complete this dissertation. I consider myself extremely fortunate to know these gentlemen and to have had the opportunity to work alongside them for the past three years. I would also like to thank my committee members, Dr. Don Schlagenhauf, Dr. Yevgeny Goncharov and Dr. David Kopriva for taking the time out of their extremely busy schedules to assist me in any way I needed. In particular, I owe a special thanks to Dr. Kopriva for
Microsimulation of artificial stock markets based on trader roles
- in Proceedings of the International Workshop on Data Mining and Adaptive Methods for Economics and Management
, 2003
"... Abstract On financial markets trading takes place continuously and market prices are typically formed whenever two traders make an agreement. Most of the artificial markets, however, implement discrete time modelling and try to set the market price at equilibrium, where most demands and supplies ca ..."
Abstract
-
Cited by 3 (1 self)
- Add to MetaCart
(Show Context)
Abstract On financial markets trading takes place continuously and market prices are typically formed whenever two traders make an agreement. Most of the artificial markets, however, implement discrete time modelling and try to set the market price at equilibrium, where most demands and supplies can be matched. This paper describes the structural design of an artificial market environment that supports continuous trading and helps to study how different traders affect market dynamics in different situations. We identify different types of traders and describe an architecture based on their role and the market microstructure where they interact. In order to get an accurate representation of the market dynamics we apply a bottomup microsimulation approach, and further, represent traders by intelligent agents. We start building from a more basic level than current approaches in the sense that we consider continuous order matching mechanisms and implement agents' behaviour based on their role in the market. For this reason we trace the life-cycle of the orders observing the changes they suffer especially caused by traders' different decision till they trigger market prices.
An agent-based framework for artificial stock markets
- In 16th Belgian-Dutch Conference on Artificial Intelligence (BNAIC
, 2004
"... Stock markets strive to provide an efficient trading platform for investors. Trading rules and mechanisms issued to accomplish this differ among stock markets, and are subject to modification over time. Furthermore, market participants assume a broad range of roles and trading strategies. Such vari- ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
Stock markets strive to provide an efficient trading platform for investors. Trading rules and mechanisms issued to accomplish this differ 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 stock market for experimentation and analysis. More than once, the resulting artificial stock markets, and thus the experi-mental results, are based on very restrictive assumptions. This paper intro-duces an agent-based framework for artificial stock market development and experimentation. The framework is flexible in the sense that multiple market structures are supported, and an infinite range of trading strategies by mar-ket participants can be captured. Such features are accomplished through the configuration of framework properties, and the appropriate hooks for extension of the framework’s components. 1
The Emergence of Knowledge Exchange: An Agent-Based Model of a Software Market
- Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
, 2008
"... Abstract—We investigate knowledge exchange among commer-cial organizations, the rationale behind it, and its effects on the market. Knowledge exchange is known to be beneficial for in-dustry, but in order to explain it, authors have used high-level concepts like network effects, reputation, and trus ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
Abstract—We investigate knowledge exchange among commer-cial organizations, the rationale behind it, and its effects on the market. Knowledge exchange is known to be beneficial for in-dustry, but in order to explain it, authors have used high-level concepts like network effects, reputation, and trust. We attempt to formalize a plausible and elegant explanation of how and why companies adopt information exchange and why it benefits the market as a whole when this happens. This explanation is based on a multiagent model that simulates a market of software providers. Even though the model does not include any high-level concepts, information exchange naturally emerges during simulations as a successful profitable behavior. The conclusions reached by this agent-based analysis are twofold: 1) a straightforward set of as-sumptions is enough to give rise to exchange in a software market, and 2) knowledge exchange is shown to increase the efficiency of the market. Index Terms—Adaptive behavior, agent-based modeling, busi-ness economics, cooperative systems, intelligent agents, multiagent systems. I.
FROM DISCRETE-TIME MODELS TO CONTINUOUS-TIME, ASYNCHRONOUS MODELING OF FINANCIAL MARKETS
"... Most agent-based simulation models of financial markets are discrete-time in nature. In this paper, we investigate to what degree such models are extensible to continuous-time, asynchronous modeling of financial markets. We study the behavior of a learning market maker in a market with information a ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
Most agent-based simulation models of financial markets are discrete-time in nature. In this paper, we investigate to what degree such models are extensible to continuous-time, asynchronous modeling of financial markets. We study the behavior of a learning market maker in a market with information asymmetry, and investigate the difference caused in the market dynamics between the discrete-time simulation and continuous-time, asynchronous simulation. We show that the characteristics of the market prices are different in the two cases, and observe that additional information is being revealed in the continuous-time, asynchronous models, which can be acted upon by the agents in such models. Because most financial markets are continuous and asynchronous in nature, our results indicate that explicit consideration of this fundamental characteristic of financial markets cannot be ignored in their agent-based modeling. Key words: artificial stock markets, agent-based computational economics, continuous-time simulation, information asymmetry, market maker.
D.: Natural computing in finance: a review
- In: Handbook of Natural Computing: Theory
, 2010
"... Provided by the author(s) and University College Dublin Library in accordance with publisher policies. Please ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
(Show Context)
Provided by the author(s) and University College Dublin Library in accordance with publisher policies. Please
A Corrected Version of the Santa Fe Institute Artificial Stock Market Model
, 2004
"... This paper rectifies a design problem in the Santa Fe Institute Artificial Stock Market Model. The mutation operator caused the resulting bit distribution to be systematically upwardly biased, thus suggesting emergent technical trading at faster learning speeds. The modified version now partly suppo ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
This paper rectifies a design problem in the Santa Fe Institute Artificial Stock Market Model. The mutation operator caused the resulting bit distribution to be systematically upwardly biased, thus suggesting emergent technical trading at faster learning speeds. The modified version now partly supports the Marimon-Sargent-Hypothesis which states that adaptive classifier agents in an artificial stock market will discover the homogeneous rational expectation equilibrium. While agents always learn the correct solution of non-bit usage, analyzing the simulated price series reveals that the updated model still shifts into a more complex regime, however, only at faster learning rates than the original model suggests.
Agent-Based Computational Economics and Finance: early research and design issues
- DREAM project, IST-1999-12679. European Community’s “Information Society Technologies” Programme
, 2003
"... Modelling economic or social systems in general and financial markets in particular with distributed networks of evolutionary agents is a very active and growing field known as Agent-based Computational Economics (ACE). It aims at explaining global behaviours and structures of social systems in ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
Modelling economic or social systems in general and financial markets in particular with distributed networks of evolutionary agents is a very active and growing field known as Agent-based Computational Economics (ACE). It aims at explaining global behaviours and structures of social systems in terms of multiple iterative interactions of simple but adaptive localized agents. A concise survey of literature is conducted here that outlines key seminal works. It essentially builds over a broad survey by Tesfatsion [28] and two by LeBaron [17, 18]. It proceeds as follows.
