Stylized Facts in Different Dynamic Regimes of a Agent-Based Artificial Stock Market

2009 ◽  
Author(s):  
Tongkui Yu ◽  
Mingyu Yuan ◽  
Honggang Li
Keyword(s):  
Stock Market ◽  
Stylized Facts ◽  
Agent Based ◽  
Artificial Stock Market

scholarly journals Traders’ Networks of Interactions and Structural Properties of Financial Markets: An Agent-Based Approach

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Linda Ponta ◽  
Silvano Cincotti
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Market Maker ◽  
Clearing House ◽  
Stylized Facts ◽  
Risky Assets ◽  
Agent Based ◽  
Price Process ◽  
Different Types ◽  
Artificial Stock Market

An information-based multiasset artificial stock market characterized by different types of stocks and populated by heterogeneous agents is presented and studied so as to determine the influences of agents’ networks on the market’s structure. Agents are organized in networks that are responsible for the formation of the sentiments of the agents. In the market, agents trade risky assets in exchange for cash and share their sentiments by means of interactions that are determined by sparsely connected graphs. A central market maker (clearing house mechanism) determines the price process for each stock at the intersection of the demand and the supply curves. A set of market’s structure indicators based on the main single-assets and multiassets stylized facts have been defined, in order to study the effects of the agents’ networks. Results point out an intrinsic structural resilience of the stock market. In fact, the network is necessary in order to archive the ability to reproduce the main stylized facts, but also the market has some characteristics that are independent from the network and depend on the finiteness of traders’ wealth.

scholarly journals An Agent-Based Approach to Artificial Stock Market Modeling

2020 ◽  
Vol 168 ◽  
pp. 161-169
Author(s):  
Samuel Vanfossan ◽  
Cihan H. Dagli ◽  
Benjamin Kwasa
Keyword(s):  
Stock Market ◽  
Agent Based ◽  
Artificial Stock Market

scholarly journals Anchoring Heuristics, Investor Sentiment and Stylized Facts in the Stock Market: An Agent Based Model

2020 ◽  
Vol 10 (01) ◽  
pp. 198-217
Author(s):  
Hermes Yukio Higachi ◽  
Ana Cristina Cruz de Faria ◽  
Adriana Sbicca ◽  
Jefferson Kato
Keyword(s):  
Stock Market ◽  
Investor Sentiment ◽  
Agent Based Model ◽  
Stylized Facts ◽  
Agent Based

scholarly journals TECHNICAL ANALYSIS BASED ON PRICE-VOLUME SIGNALS AND THE POWER OF TRADING BREAKS

2006 ◽  
Vol 09 (02) ◽  
pp. 227-244 ◽  
Author(s):  
FRANK H. WESTERHOFF
Keyword(s):  
Stock Market ◽  
Nonlinear Model ◽  
Stock Markets ◽  
Trading Volume ◽  
Market Model ◽  
Fat Tails ◽  
Stylized Facts ◽  
Volatility Clustering ◽  
Intrinsic Values ◽  
Artificial Stock Market

We propose a novel stock market model and investigate the effectiveness of trading breaks. Our nonlinear model consists of two types of traders: while fundamentalists expect prices to return towards their intrinsic values, chartists extrapolate past price movements into the future. Moreover, chartists condition their orders on past trading volume. The model is able to replicate several stylized facts of stock markets such as fat tails and volatility clustering. Using the model as an artificial stock market laboratory we find that trading breaks have the power to reduce volatility and — if fundamentals do not move too strongly — also mispricing.

scholarly journals MARKET FLUCTUATIONS EXPLAINED BY DIVIDENDS AND INVESTOR NETWORKS

2017 ◽  
Vol 20 (08) ◽  
pp. 1750007 ◽  
Author(s):  
MATTHEW OLDHAM
Keyword(s):  
Stock Market ◽  
Complex Systems ◽  
Financial Markets ◽  
Network Topology ◽  
Network Science ◽  
Agent Based ◽  
Excess Volatility ◽  
Artificial Stock Market ◽  
Analytical Frameworks ◽  
Insight Into

The inability of investors and academics to consistently predict, and understand the behavior of financial markets has forced the search for alternative analytical frameworks. Analyzing financial markets as complex systems is a framework that has demonstrated great promises, with the use of agent-based models (ABMs) and the inclusion of network science playing an important role in increasing the relevance of the framework. Using an artificial stock market created via an ABM, this paper provides a significant insight into the mechanisms that drive the returns in financial markets, including periods of elevated prices and excess volatility. The paper demonstrates that the network topology that investors form and the dividend policy of firms significantly affect the behavior of the market. However, if investors have a bias to following their neighbors then the topology becomes redundant. By successfully addressing these issues this paper helps refine and shape a variety of additional research tasks for the use of ABMs in uncovering the dynamics of financial markets.

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