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.

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.

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 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 Behavioral agent-based framework for interacting financial markets

2020 ◽  
Vol 5 (2) ◽  
pp. 94-115
Author(s):  
Heba M. Ezzat
Keyword(s):  
Financial Markets ◽  
Trading Strategy ◽  
Past Performance ◽  
Time Step ◽  
Monetary Policies ◽  
Content Type ◽  
Agent Based ◽  
Market Makers ◽  
Novel Approach ◽  
Excess Volatility

Purpose This paper aims at developing a behavioral agent-based model for interacting financial markets. Additionally, the effect of imposing Tobin taxes on market dynamics is explored. Design/methodology/approach The agent-based approach is followed to capture the highly complex, dynamic nature of financial markets. The model represents the interaction between two different financial markets located in two countries. The artificial markets are populated with heterogeneous, boundedly rational agents. There are two types of agents populating the markets; market makers and traders. Each time step, traders decide on which market to participate in and which trading strategy to follow. Traders can follow technical trading strategy, fundamental trading strategy or abstain from trading. The time-varying weight of each trading strategy depends on the current and past performance of this strategy. However, technical traders are loss-averse, where losses are perceived twice the equivalent gains. Market makers settle asset prices according to the net submitted orders. Findings The proposed framework can replicate important stylized facts observed empirically such as bubbles and crashes, excess volatility, clustered volatility, power-law tails, persistent autocorrelation in absolute returns and fractal structure. Practical implications Artificial models linking micro to macro behavior facilitate exploring the effect of different fiscal and monetary policies. The results of imposing Tobin taxes indicate that a small levy may raise government revenues without causing market distortion or instability. Originality/value This paper proposes a novel approach to explore the effect of loss aversion on the decision-making process in interacting financial markets framework.

scholarly journals Inventory Effects on Daily Returns in Financial Markets

2003 ◽  
Vol 06 (07) ◽  
pp. 739-765
Author(s):  
Andreas Krause
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Market Microstructure ◽  
Empirical Investigation ◽  
Substantial Part ◽  
Simulation Studies ◽  
Market Data ◽  
First Order ◽  
Excess Volatility ◽  
Daily Returns

In this paper we investigate the properties of daily returns arising from inventory effects. We therefore use the well established framework of inventory-based models from market microstructure theory. It is shown using simulation studies that from this model daily returns exhibit excess volatility, negative first-order autocovariances and the volatility has a positive first-order autocovariance, which is consistent with a GARCH-process. An empirical investigation shows that a substantial part of the properties of daily returns in stock market data can be explained by inventory effects.

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.

Sign in / Sign up

Export Citation Format

Share Document