scholarly journals ABM with Behavioral Bias and Applications in Simulating China Stock Market

2015 ◽  
Vol 5 (4) ◽  
pp. 257-270 ◽  
Author(s):  
Guocheng Wang ◽  
Shiguo Zhang
Keyword(s):  
Stock Market ◽  
Behavioral Finance ◽  
Conduction Mechanism ◽  
Agent Based Modeling ◽  
Behavioral Characteristics ◽  
Impulsive Behavior ◽  
Agent Based ◽  
China Stock Market ◽  
Internal Relations ◽  
Real Behavior

Abstract One of the most important advantage of ABM (Agent-Based Modeling) used in social and economic calculation simulation is that the critical behavioral characteristics of the micro agents can be deeply depicted by the approach. Why, what and how real behavior(s) should be incorporated into ABM and is it appropriate and effective to use ABM with HSCA collaboration and micro-macro link features for complex economy/finance analysis? Through deepening behavioral analysis and using computational experimental methods incorporating HS (Human Subject) into CA (Computational Agent), which is extended ABM, based on the theory of behavioral finance and complexity science as well, we constructed a micro-macro integrated model with the key behavioral characteristics of investors as an experimental platform to cognize the conduction mechanism of complex capital market and typical phenomena in this paper, and illustrated briefly applied cases including the internal relations between impulsive behavior and the fluctuation of stock’s, the asymmetric cognitive bias and volatility cluster, deflective peak and fat-tail of China stock market.

New developments in financial economics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Thomas D. Willett
Keyword(s):  
Behavioral Finance ◽  
Financial Economics ◽  
Recent Literature ◽  
Agent Based Modeling ◽  
New Developments ◽  
Content Type ◽  
Agent Based ◽  
New Approaches ◽  
New Methodologies ◽  
Practical Implications

Purpose This study aims to critically review recent contributions to the methodology of financial economics and discuss how they relate to one another and directions for further research. Design/methodology/approach A critical review of recent literature on new methodologies for financial economics. Findings Recent books have made important contributions to the study of financial economics. They suggest new approaches that include an emphasis on radical uncertainty, adaptive markets, agent-based modeling and narrative economics, as well as extensions of behavioral finance to include concepts such as diagnostic expectations. Many of these contributions can be seen more as complements than substitutes and provide fruitful directions for further research. Efficient markets can be seen as holding under particular circumstances. A major them of most of these contributions is that the study of financial crises and other aspects of financial economics requires the use of multiple theories and approaches. No one approach will be sufficient. Research limitations/implications There are great opportunities for further research in financial economics making use of these new approaches. Practical implications These recent contributions can be quite useful for improved analysis by researchers, private participants in the financial sector and macroeconomic and regulatory officials. Originality/value Provides an introduction to these new approaches and highlights fruitful areas for their extensions and applications.

Agent-Based Modeling Bridges Theory of Behavioral Finance and Financial Markets

2010 ◽  
pp. 134-155
Author(s):  
Hiroshi Takahashi ◽  
Takao Terano
Keyword(s):  
Decision Making ◽  
Asset Pricing ◽  
Financial Market ◽  
Behavioral Finance ◽  
Investment Strategy ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based ◽  
Micro Level ◽  
Fundamental Values

This chapter describes advances of agent-based models to financial market analyses based on our recent research. We have developed several agent-based models to analyze microscopic and macroscopic links between investor behaviors and price fluctuations in a financial market. The models are characterized by the methodology that analyzes the relations among micro-level decision making rules of the agents and macro-level social behaviors via computer simulations. In this chapter, we report the outline of recent results of our analysis. From the extensive analyses, we have found that (1) investors’ overconfidence behaviors plays various roles in a financial market, (2) overconfident investors emerge in a bottom-up fashion in the market, (3) they contribute to the efficient trades in the market, which adequately reflects fundamental values, (4) the 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 (5) under certain assumptions, the passive investment strategy and active investment strategy could coexist in a financial market.

Unreal Models of Real Behavior: The Agent-Based Modeling Experience

2012 ◽  
Vol 35 (1) ◽  
pp. 19-23 ◽  
Author(s):  
Lee Hoffer
Keyword(s):  
Drug Users ◽  
Personal Narratives ◽  
Agent Based Modeling ◽  
Treatment Programs ◽  
Social Environments ◽  
Fair Share ◽  
P Values ◽  
Agent Based ◽  
Fieldwork Experience ◽  
Real Behavior

Like many applied anthropologists who have worked for 20 or so years in public health and drug addiction research, I have sat in more than my fair share of overly quantitative presentations on the "behaviors" of drug users that dominate conferences in the field. Like my peers in these circumstances, I am often frustrated not because the models, tables, graphs, figures, correlations, p-values, or odds ratios presented are difficult to understand or because I have an aversion to basic science, epidemiology, or survey research. Rather, it is because the numbers rarely reflect the histories, concepts, relationships, interactions, and other nuances that have shaped my fieldwork experience. The highly personal narratives of participants framed by equally complex social environments are not visible in the numbers. To epidemiologists and other like-minded health researchers, the numbers are the narrative and all that is required for informing and evaluating theories, models, interventions, treatment programs, or policy.

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