- Original authors / creators
- Rongtian Zhou; Xiong Xiong; Bàrbara Llacay; Gilbert Peffer
- Original publication
- Entropy · Multidisciplinary Digital Publishing Institute (MDPI) · 2023-11-29
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- CC BY 4.0
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Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Article-specific license URI: https://creativecommons.org/licenses/by/4.0/ - Edition changes
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- Adjacent research, not a directly sampled family-office study. Applicability to a family office requires separate assessment. Agent-based modeling is a scientific research method based on computer models that simulate the behavior and interactions of autonomous agents in a complex system [16]. Agent-based models represent the system as a collection of individual agents, each with its own unique characteristics, rules, and behaviors. These agents interact with one another and with their environment, often resulting in emergent patterns and behaviors that can be difficult to predict using traditional mathematical models. Agent-based models can test and compare multiple variables in simulated financial markets, thereby more accurately predicting and evaluating changes and risks in financial markets. However, agent-based modeling may also have some limitations such as data requirements and validation challenges, as accurate agent-based modeling often demands detailed data on individual agents and their interactions, which may not always be readily available or feasible to collect, and researchers should ensure that the model’s behavior aligns with real-world observations and is a faithful representation of the system under study, which may be difficult. The limitations of this study lie in two aspects: firstly, the current model is only applicable to the Chinese A-share market, and secondly, the impact of unforeseen events is only manifested as a disruption to the fundamental value of stocks. Therefore, future research could be divided into two main directions. Firstly, the agent-based model can be extended to the financial literacy applications in other global markets. Secondly, there is a need to refine the types of unforeseen events and their impacts, in order to investigate how financial literacy plays a role in market quality and investor performance when dealing with different events.
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