Learning and Information Dissemination in Limit Order Markets

Wei, Lijian; Zhang, Wei; He, X. T.; Zhang, Yongjie

doi:10.2139/ssrn.2276350

Cited by 3 publications

(11 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More specifically, our market is similar to the market presented in Wei et al (2013). The fundamental value V t occur according to a Poisson process N(t) with parameter φ and initial fundamental value V 0 .…”

Section: Asset Pricingmentioning

confidence: 99%

“…Investors are classified accordingly as fundamentalist or informed traders, which might be perfectly informed, Uninformed, Noise traders and Switchers. To speed up the simulations, we assume that there are N = 100 traders, following Wei et al (2013). Among which, there are N I = 14 informed traders, N U = 30 uninformed traders, and N ZI = 56 ZI traders in our agent-based computational model.…”

Section: Investment Strategymentioning

confidence: 99%

“…The details of the Artificial Neural Networks learning is given in Wei, Zhang, He and Zhang (2013) who show that the model developed in this paper is able to generate a number of important stylized facts, including leptokurtosis, volatility clustering, ARCH effect and fat tails.…”

Section: Informed Agentsmentioning

confidence: 99%

“…We introduce similar order submission rules as in Rosu (2010) and Wei et al (2013) where agents who submit limit orders need liquidity compensation, which is at least able to cover the transaction, cost μ. In other terms, buy limit order price can be obtained by subtracting μ from P e t ; sell limit order price can be obtained by adding μ on pet.…”

Section: The Order Execution Mechanismmentioning

confidence: 99%

See 3 more Smart Citations

Price Dynamics and Market Volatility: Behavioral Heterogeneity under Switching Trading Strategies on Artificial Financial Market

Rekik¹,

Boujelbène²

2015

International Journal of Financial Research

View full text Add to dashboard Cite

In recent years, studying stock markets using multi-agent based models has grow into an important research area due to the fact that this line of attack replicates the nature of the financial market where varied traders with a mixture of expectations and diverse points of rationality network with each other through the market. The main aim of this paper is to include selected finding from behavioral finance into agent model hypothesis. In particular we revise the switching of the trading strategies such as the switching between, uninformed agents, informed agents, zero-intelligence and switchers traders. More precisely we focus on the relationship concerning switching and the excess volatility of the stock market prices and to addresses the question of why traders switch from one type to another.

show abstract

Section: Asset Pricingmentioning

confidence: 99%

Section: Investment Strategymentioning

confidence: 99%

Section: Informed Agentsmentioning

confidence: 99%

Section: The Order Execution Mechanismmentioning

confidence: 99%

See 2 more Smart Citations

Price Dynamics and Market Volatility: Behavioral Heterogeneity under Switching Trading Strategies on Artificial Financial Market

Rekik¹,

Boujelbène²

2015

International Journal of Financial Research

View full text Add to dashboard Cite

show abstract

“…To do so we design experiments with different configurations of switchers. Similar to [28], we will fix the percentages of the informed and zero-intelligence agents to be 12% and 58%. In our model the uninformed agents are divided into switchers and uninformed traders who can not switch.…”

Section: Computational Designmentioning

confidence: 99%

Impact of information cost and switching of trading strategies in an artificial stock market

Liu

Zhang

et al. 2014

Physica A: Statistical Mechanics and its Applications

Self Cite

View full text Add to dashboard Cite

International audienceThis paper studies the switching of trading strategies and its effect on the market volatility in a continuous double auction market. We describe the behavior when some uninformed agents, who we call switchers, decide whether or not to pay for information before they trade. By paying for the information they behave as informed traders. First we verify that our model is able to reproduce some of the stylized facts in real financial markets. Next we consider the relationship between switching and the market volatility under different structures of investors. We find that there exists a positive relationship between the market volatility and the percentage of switchers. We therefore conclude that the switchers are a destabilizing factor in the market. However, for a given fixed percentage of switchers, the proportion of switchers that decide to buy information at a given moment of time is negatively related to the current market volatility. In other words, if more agents pay for information to know the fundamental value at some time, the market volatility will be lower. This is because the market price is closer to the fundamental value due to information diffusion between switchers

show abstract

Learning and Evolution of Trading Strategies in Limit Order Markets

Chiarella

Wei

2013

SSRN Journal

Self Cite

View full text Add to dashboard Cite

How do traders process and learn from market information, what trading strategies should they use, and how does learning affect the market? This paper proposes a learning model of an artificial limit order market with asymmetric information to address these issues. Using a genetic algorithm as a learning mechanism, we show that learning, in particular the learning from uninformed traders, improves market informational efficiency and has a significant impact on the stylized facts of limit order markets, order submission, liquidity supply and consumption, the hump shaped order book near the quote, and the bid-ask spread. Moreover, the learning affects the evolution process of the trading strategies for all traders. The model provides some insights into market efficiency, the interaction of traders, the dynamics of limit order books, and the evolution of trading strategies.

show abstract

Learning and Information Dissemination in Limit Order Markets

Cited by 3 publications

References 49 publications

Price Dynamics and Market Volatility: Behavioral Heterogeneity under Switching Trading Strategies on Artificial Financial Market

Price Dynamics and Market Volatility: Behavioral Heterogeneity under Switching Trading Strategies on Artificial Financial Market

Impact of information cost and switching of trading strategies in an artificial stock market

Learning and Evolution of Trading Strategies in Limit Order Markets

Contact Info

Product

Resources

About