TRADER SPECIES WITH DIFFERENT DECISION STRATEGIES AND PRICE DYNAMICS IN FINANCIAL MARKETS: AN AGENT-BASED MODELING PERSPECTIVE

2010 ◽  
Vol 09 (02) ◽  
pp. 327-344 ◽  
Author(s):  
WEI ZHANG ◽  
GEN LI ◽  
XIONG XIONG ◽  
YONG JIE ZHANG
Keyword(s):  
Financial Markets ◽  
Asset Price ◽  
Agent Based Modeling ◽  
Price Dynamics ◽  
Trading Behavior ◽  
Agent Based ◽  
Price Process ◽  
Trading Decisions ◽  
The Individual ◽  
Analytical Approaches

Investors with different trading strategies can be viewed as different "species" in financial markets. Since the asset price is ultimately determined by the individual trading decisions, the combination and evolution of different trader species in financial market ecology will have great impact to the price dynamics. Considering the limitations and shortcomings of traditional analytical approaches in financial economics in dealing with this issue, an agent-based computational model is introduced in this paper. With the co-existence of 3-type trader species that make different decisions based on their own beliefs and constrains, it is found that although rational speculation destabilizes the price process with the presence of positive feedback strategy, as suggested in the literature, introducing extra noise trading behavior to the market will make the price process back to a more stationary situation, meaning that the market will be healthier if more diversified trader species co-exist in the markets.

Agent-based economic models and econometrics

2012 ◽  
Vol 27 (2) ◽  
pp. 187-219 ◽  
Author(s):  
Shu-Heng Chen ◽  
Chia-Ling Chang ◽  
Ye-Rong Du
Keyword(s):  
Financial Markets ◽  
Agent Based Modeling ◽  
Computational Finance ◽  
Agent Based ◽  
Second Stage ◽  
The Past ◽  
Stage Development ◽  
Three Stages ◽  
The One ◽  
Two Stages

AbstractThis paper reviews the development of agent-based (computational) economics (ACE) from an econometrics viewpoint. The review comprises three stages, characterizing the past, the present, and the future of this development. The first two stages can be interpreted as an attempt to build the econometric foundation of ACE, and, through that, enrich its empirical content. The second stage may then invoke a reverse reflection on the possible agent-based foundation of econometrics. While ACE modeling has been applied to different branches of economics, the one, and probably the only one, which is able to provide evidence of this three-stage development is finance or financial economics. We will, therefore, focus our review only on the literature of agent-based computational finance, or, more specifically, the agent-based modeling of financial markets.

scholarly journals An Interval of No-Arbitrage Prices in Financial Markets with Volatility Uncertainty

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Hanlei Hu ◽  
Zheng Yin ◽  
Weipeng Yuan
Keyword(s):  
Brownian Motion ◽  
Financial Markets ◽  
Stock Price ◽  
Contingent Claims ◽  
Price Dynamics ◽  
Complex Situation ◽  
No Arbitrage ◽  
Price Process ◽  
Arbitrage Opportunities

In financial markets with volatility uncertainty, we assume that their risks are caused by uncertain volatilities and their assets are effectively allocated in the risk-free asset and a risky stock, whose price process is supposed to follow a geometric G-Brownian motion rather than a classical Brownian motion. The concept of arbitrage is used to deal with this complex situation and we consider stock price dynamics with no-arbitrage opportunities. For general European contingent claims, we deduce the interval of no-arbitrage price and the clear results are derived in the Markovian case.

Survey of Trading Systems for Individual Investors

2011 ◽  
pp. 157-176 ◽  
Author(s):  
M. Kersch ◽  
G. Schmidt
Keyword(s):  
Financial Markets ◽  
Stock Exchange ◽  
Point Of View ◽  
Individual Investors ◽  
Individual Investor ◽  
Trading System ◽  
Trading Systems ◽  
Trading Decisions ◽  
The Individual

Trading decisions in financial markets can be supported by the use of trading algorithms. To evaluate trading algorithms and to generate orders to be executed on the stock exchange trading systems are used. In this chapter, we define the individual investors’ requirements on a trading system, and analyze 17 trading systems from an individual investor’s point of view. The results of our study point out that the best alternative for an individual investor is not one single trading system, but a combination of two different classes of trading systems.

Analyzing the Influences of Passive Investment Strategies on Financial Markets via Agent-Based Modeling

2008 ◽  
pp. 224-238 ◽  
Author(s):  
Hiroshi Takahashi ◽  
Satoru Takahashi ◽  
Takao Terano
Keyword(s):  
Asset Pricing ◽  
Financial Markets ◽  
Financial Market ◽  
Investment Strategy ◽  
Agent Based Modeling ◽  
Investment Strategies ◽  
Agent Based Model ◽  
Efficient Market ◽  
Agent Based ◽  
Price Fluctuations

This chapter develops an agent-based model to analyze microscopic and macroscopic links between investor behaviors and price fluctuations in a financial market. This analysis focuses on the effects of Passive Investment Strategy in a financial market. From the extensive analyses, we have found that (1) Passive Investment Strategy is valid in a realistic efficient market, however, it could have bad influences such as instability of market and inadequate asset pricing deviations, and (2) under certain assumptions, Passive Investment Strategy and Active Investment Strategy could coexist in a Financial Market.

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