MICROSTRUCTURE DYNAMICS AND AGENT-BASED FINANCIAL MARKETS: CAN DINOSAURS RETURN?

2012 ◽  
Vol 15 (supp02) ◽  
pp. 1250060 ◽  
Author(s):  
MICHAEL KAMPOURIDIS ◽  
SHU-HENG CHEN ◽  
EDWARD TSANG
Keyword(s):  
Stock Market ◽  
Genetic Programming ◽  
Financial Markets ◽  
Trading Strategies ◽  
Self Organizing Maps ◽  
Market Models ◽  
Agent Based ◽  
Time Periods ◽  
Artificial Stock Market ◽  
Rule Inference

This paper formalizes observations made under agent-based artificial stock market models into a concrete hypothesis, which is called the Dinosaur Hypothesis. This hypothesis states that the behavior of financial markets constantly changes and that the trading strategies in a market need to continuously co-evolve with it in order to remain effective. After formalizing the hypothesis, we suggest a testing methodology and run tests under 10 international financial markets. Our tests are based on a framework that we recently developed, which used Genetic Programming as a rule inference engine, and Self-Organizing Maps as a clustering machine for the above rules. However, an important assumption of that study was that maps among different periods were directly comparable with each other. In order to allow this to happen, we had to keep the same clusters throughout the different time periods of our experiments. Nevertheless, this assumption could be considered as strict or even unrealistic. In this paper, we relax this assumption. This makes our model more realistic. In addition, this allows us to investigate in depth the dynamics of market behavior and test for the plausibility of the Dinosaur Hypothesis. The results show that indeed markets' behavior constantly changes. As a consequence, strategies need to continuously co-evolve with the market; if they do not, they become obsolete or dinosaurs.

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 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.

Evolution of Agents in a Simple Artificial Market

2010 ◽  
pp. 118-133
Author(s):  
Hiroshi Sato ◽  
Masao Kubo ◽  
Akira Namatame
Keyword(s):  
Stock Market ◽  
Comparative Study ◽  
Wealth Distribution ◽  
Trading Strategies ◽  
The Other ◽  
Complex Behavior ◽  
Artificial Market ◽  
Agent Based ◽  
Other Hand ◽  
Artificial Stock Market

In this chapter, we conduct a comparative study of various traders following different trading strategies. We design an agent-based artificial stock market consisting of two opposing types of traders: “rational traders” (or “fundamentalists”) and “imitators” (or “chartists”). Rational traders trade by trying to optimize their short-term income. On the other hand, imitators trade by copying the majority behavior of rational traders. We obtain the wealth distribution for different fractions of rational traders and imitators. When rational traders are in the minority, they can come to dominate imitators in terms of accumulated wealth. On the other hand, when rational traders are in the majority and imitators are in the minority, imitators can come to dominate rational traders in terms of accumulated wealth. We show that survival in a finance market is a kind of minority game in behavioral types, rational traders and imitators. The coexistence of rational traders and imitators in different combinations may explain the market’s complex behavior as well as the success or failure of various trading strategies. We also show that successful rational traders are clustered into two groups: In one group traders always buy and their wealth is accumulated in stocks; in the other group they always sell and their wealth is accumulated in cash. However, successful imitators buy and sell coherently and their wealth is accumulated only in cash.

The Market Fraction Hypothesis under Different Genetic Programming Algorithms

2011 ◽  
pp. 37-54
Author(s):  
Michael Kampouridis ◽  
Shu-Heng Chen ◽  
Edward Tsang
Keyword(s):  
Genetic Programming ◽  
Uniform Distribution ◽  
International Markets ◽  
Inference Engine ◽  
Self Organizing Maps ◽  
Dominant Type ◽  
Agent Based ◽  
Long Run ◽  
Programming Algorithms ◽  
Rule Inference

In a previous work, inspired by observations made in many agent-based financial models, we formulated and presented the Market Fraction Hypothesis, which basically predicts a short duration for any dominant type of agents, but then a uniform distribution over all types in the long run. We then proposed a two-step approach, a rule-inference step, and a rule-clustering step, to test this hypothesis. We employed genetic programming as the rule inference engine, and applied self-organizing maps to cluster the inferred rules. We then ran tests for 10 international markets and provided a general examination of the plausibility of the hypothesis. However, because of the fact that the tests took place under a GP system, it could be argued that these results are dependent on the nature of the GP algorithm. This chapter thus serves as an extension to our previous work. We test the Market Fraction Hypothesis under two new different GP algorithms, in order to prove that the previous results are rigorous and are not sensitive to the choice of GP. We thus test again the hypothesis under the same 10 empirical datasets that were used in our previous experiments. Our work shows that certain parts of the hypothesis are indeed sensitive on the algorithm. Nevertheless, this sensitivity does not apply to all aspects of our tests. This therefore allows us to conclude that our previously derived results are rigorous and can thus be generalized.

scholarly journals Risks of Traders on the World Stock Market on the Example of Ukraine

2019 ◽  
Vol 67 ◽  
pp. 06001 ◽  
Author(s):  
George Abuselidze ◽  
Olga Mohylevska ◽  
Nina Merezhko ◽  
Nadiia Reznik ◽  
Anna Slobodianyk
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
World Market ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Practical Significance ◽  
External Shocks ◽  
Domino Effect ◽  
The World ◽  
The Impact

The article reveals the essence and features of the development of the stock market in Ukraine. It was established that the vigorous activity of countries in the world financial markets means that they also face a risk of global financial turmoil (the so-called “domino effect”). It is determined that the impact of global financial instability on the country depends on the openness of its economy that will lead to significant external “shocks”. The possibility of providing effective influence on domestic stock market activity with taking into account the changing world situation, development of perfect trading strategies for each participant is substantiated. The conducted analysis of the world market conditions of stock markets in recent years has made it possible to assess the real risks for new participants in the stock market and become the basis for the development of an appropriate effective trading strategy. The practical significance of the results is that they allow for a measurable approach to assessing the existing risk when choosing one or another trading strategy to move to the world stock market.

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
Sign in / Sign up

Export Citation Format

Share Document