Prediction of Financial Markets Using Agent-Based Modeling with Optimization Driven by Statistical Evaluation of Historical Data

2012 ◽  
pp. 308-313
Author(s):  
Jana Kočišová ◽  
Denis Horváth ◽  
Tomáš Kasanický ◽  
Ján Buša
Keyword(s):  
Financial Markets ◽  
Statistical Evaluation ◽  
Historical Data ◽  
Agent Based Modeling ◽  
Agent Based

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.

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.

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.

AN ANALYSIS OF THE INFLUENCE OF DISPERSION OF VALUATIONS ON FINANCIAL MARKETS THROUGH AGENT-BASED MODELING

2012 ◽  
Vol 11 (01) ◽  
pp. 143-166 ◽  
Author(s):  
HIROSHI TAKAHASHI
Keyword(s):  
Financial Markets ◽  
Financial Constraints ◽  
Investment Strategy ◽  
Agent Based Modeling ◽  
Investment Strategies ◽  
Market Prices ◽  
Agent Based ◽  
Short Sale Constraints ◽  
Short Sale ◽  
Fundamental Values

This research analyzes the influence of dispersion of valuations on financial markets, taking several aspects of real financial market into consideration (such as financial constraints, investment strategies and so on). As a result of intensive experiments in the market, we made the following findings: (1) Dispersion of fundamentalists' valuations has little effect on the market when financial constraints are absent; (2) When financial constraints — such as short-sale constraints — are introduced, certain situations arise in which deviations from fundamental values become larger, according to the level of the dispersion of valuations; (3) A passive investment strategy, as is consistent with traditional financial theory, is valid even when the introduction of financial constraints causes market prices to deviate significantly from fundamental values. These results contribute to clarifying the mechanism of price fluctuations in financial markets and are notable from both academic and practical view points.

Agent-based Modeling for Financial Markets

2018 ◽  
Author(s):  
Giulia Iori ◽  
James Porter
Keyword(s):  
Financial Markets ◽  
Data Gathering ◽  
Simple Zero ◽  
Agent Based Modeling ◽  
Policy Makers ◽  
Agent Based ◽  
Macro Scale ◽  
Learning Capabilities ◽  
Active Data ◽  
Zero Intelligence

This chapter discusses a step in the evolution of agent-based model (ABM) research in finance. Agent-based modeling has concentrated on the development of stylized market models, which have been extremely useful for understanding how complex macro-scale phenomena emerge from micro-rules. In order to further develop ABMs from proof of concept into robust tools for policy makers, to control and forecast complex real-world financial markets, it is essential to permit agents to behave as active data-gathering decision makers with sophisticated learning capabilities. The main focus of this chapter is to show how agent based models (ABMs) in financial markets have evolved from simple zero- intelligence agents that follow arbitrary rules of thumb into sophisticated agents described by microfounded rules of behavior. The chapter then briefly looks at the challenges posed by and approaches to model calibration and provides examples of how ABMs have been successful at offering useful insights for policy making.

Sign in / Sign up

Export Citation Format

Share Document