Agent-based simulation of herding in financial markets

There are several models of financial markets which look at the herding effect. This is a situation where many market traders act as a herd in that they all behave in a similar way with their trading. This type of behaviour may explain certain observed characteristics (or ‘stylised facts’) in real markets. However, the various models have different herding mechanisms and market settings This paper sets out the rationale of our approach and our initial work in trying to get a better understanding of herding in financial markets. Our research, though, is at an early stage. The basic methodology is to reproduce and compare some of the existing models, hopefully leading to a more general understanding and measure of herding and the relationship with market behaviour. One model has been investigated so far and this is described. A more general issue is the research importance of reproducing previous studies

Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability

The integrated - environmental, economic and social - analysis of climate change calls for a paradigm shift as it is fundamentally a problem of complex, bottom-up and multi-agent human behaviour. There is a growing awareness that global environmental change dynamics and the related socio-economic implications involve a degree of complexity that requires an innovative modelling of combined social and ecological systems. Climate change policy can no longer be addressed separately from a broader context of adaptation and sustainability strategies. A vast body of literature on agent-based modelling (ABM) shows its potential to couple social and environmental models, to incorporate the influence of micro-level decision making in the system dynamics and to study the emergence of collective responses to policies. However, there are few publications which concretely apply this methodology to the study of climate change related issues. The analysis of the state of the art reported in this paper supports the idea that today ABM is an appropriate methodology for the bottom-up exploration of climate policies, especially because it can take into account adaptive behaviour and heterogeneity of the system's components.Review, Agent-Based Modelling, Socio-Ecosystems, Climate Change, Adaptation, Complexity.

Agent-based Modeling And Market Microstructure

In most modern financial markets, traders express their preferences for assets by making orders. These orders are either executed if a counterparty is willing to match them or collected in a priority queue, called a limit order book. Such markets are said to adopt an order-driven trading mechanism. A key question in this domain is to model and analyze the strategic behavior of market participants, in response to different definitions of the trading mechanism (e.g., the priority queue changed from the continuous double auctions to the frequent call market). The objective is to design financial markets where pernicious behavior is minimized.The complex dynamics of market activities are typically studied via agent-based modeling (ABM) methods, enriched by Empirical Game-Theoretic Analysis (EGTA) to compute equilibria amongst market players and highlight the market behavior (also known as market microstructure) at equilibrium. This thesis contributes to this research area by evaluating the robustness of this approach and providing results to compare existing trading mechanisms and propose more advanced designs.In Chapter 4, we investigate the equilibrium strategy profiles, including their induced market performance, and their robustness to different simulation parameters. For two mainstream trading mechanisms, continuous double auctions (CDAs) and frequent call markets (FCMs), we find that EGTA is needed for solving the game as pure strategies are not a good approximation of the equilibrium. Moreover, EGTA gives generally sound and robust solutions regarding different market and model setups, with the notable exception of agents’ risk attitudes. We also consider heterogeneous EGTA, a more realistic generalization of EGTA whereby traders can modify their strategies during the simulation, and show that fixed strategies lead to sufficiently good analyses, especially taking the computation cost into consideration.After verifying the reliability of the ABM and EGTA methods, we follow this research methodology to study the impact of two widely adopted and potentially malicious trading strategies: spoofing and submission of iceberg orders. In Chapter 5, we study the effects of spoofing attacks on CDA and FCM markets. We let one spoofer (agent playing the spoofing strategy) play with other strategic agents and demonstrate that while spoofing may be profitable in both market models, it has less impact on FCMs than on CDAs. We also explore several FCM mechanism designs to help curb this type of market manipulation even further. In Chapter 6, we study the impact of iceberg orders on the price and order flow dynamics in financial markets. We find that the volume of submitted orders significantly affects the strategy choice of the other agents and the market performance. In general, when agents observe a large volume order, they tend to speculate instead of providing liquidity. In terms of market performance, both efficiency and liquidity will be harmed. We show that while playing the iceberg-order strategy can alleviate the problem caused by the high-volume orders, submitting a large enough order and attracting speculators is better than taking the risk of having fewer trades executed with iceberg orders.We conclude from Chapters 5 and 6 that FCMs have some exciting features when compared with CDAs and focus on the design of trading mechanisms in Chapter 7. We verify that CDAs constitute fertile soil for predatory behavior and toxic order flows and that FCMs address the latency arbitrage opportunities built in those markets. This chapter studies the extent to which adaptive rules to define the length of the clearing intervals — that might move in sync with the market fundamentals — affect the performance of frequent call markets. We show that matching orders in accordance with these rules can increase efficiency and selfish traders’ surplus in a variety of market conditions. In so doing, our work paves the way for a deeper understanding of the flexibility granted by adaptive call markets

Replicating agent-based simulation models of herding in financial markets

Agent-based simulation of herding in financial markets varies in the herding and market mechanism. Replication studies are a cornerstone of the scientific method although it is not applied very often. The research aims to obtain a greater understanding of herding and the related stylised facts, assess the herding models’ reproducibility, and find ways to improve reproducibility by replicating two herding models. The first herding mechanism factor (Tedeschi et al., 2012) controls the extent of the neighbour’s influence on the expected returns and hence on the trading decisions. The market mechanism is an artificial market where agents submit either ask or bid orders into the order book, and they trade between themselves. The second replicating herding mechanism (Lux and Marchesi, 2000) is based on transition probabilities to decide whether agents are fundamentalists, optimistic or pessimistic chartists. The market mechanism is demand and supply. The first replicating study fails to produce the original results, whereas the second does have similar findings to the original paper. The second model’s description is done by following the recent STRESS guidelines for specifying models. The guidelines help to cover everything needed for describing the second model. Then features from the STRESS and ODD guidelines are combined to give a slightly revised guideline with a defined structure. This is considered to give an improvement. Both models have fat tails, and only the second model has volatility clustering. The behaviour of the second model that gives the volatility clustering is called on-off intermittency. This is analysed in detail to understand how the model enters and leaves periods of high volatility. The conclusions are that randomness causes the model to move into high volatility, which happens when the percentage of noise traders is high, and the price effect in the model soon returns the model back to low volatility

A theoretical and practical approach to a persuasive agent model for change behaviour in oral care and hygiene

There is an increased use of the persuasive agent in behaviour change interventions due to the agent‘s features of sociable, reactive, autonomy, and proactive. However, many interventions have been unsuccessful, particularly in the domain of oral care. The psychological reactance has been identified as one of the major reasons for these unsuccessful behaviour change interventions. This study proposes a formal persuasive agent model that leads to psychological reactance reduction in order to achieve an improved behaviour change intervention in oral care and hygiene. Agent-based simulation methodology is adopted for the development of the proposed model. Evaluation of the model was conducted in two phases that include verification and validation. The verification process involves simulation trace and stability analysis. On the other hand, the validation was carried out using user-centred approach by developing an agent-based application based on belief-desire-intention architecture. This study contributes an agent model which is made up of interrelated cognitive and behavioural factors. Furthermore, the simulation traces provide some insights on the interactions among the identified factors in order to comprehend their roles in behaviour change intervention. The simulation result showed that as time increases, the psychological reactance decreases towards zero. Similarly, the model validation result showed that the percentage of respondents‘ who experienced psychological reactance towards behaviour change in oral care and hygiene was reduced from 100 percent to 3 percent. The contribution made in this thesis would enable agent application and behaviour change intervention designers to make scientific reasoning and predictions. Likewise, it provides a guideline for software designers on the development of agent-based applications that may not have psychological reactance

Contributions of Computational Cognitive Modeling to the Understanding of the Financial Markets

Tese de doutoramento em Ciências e Tecnologias da Informação, apresentada ao Departamento de Engenharia Informática da Faculdade de Ciências e Tecnologia da Universidade de CoimbraOs mercados financeiros são sistemas socioeconómicos complexos, dinâmicos e estratégicos nos quais um grande número de heterogéneos participantes interagem por meio da compra e venda de diferentes tipos de ativos. Os mercados financeiros tais como os mercados de ações (e.g., S&P500) possuem múltiplas funções. Por exemplo, os mercados financeiros propiciam meios para que os participantes e as companhias façam um melhor processo de alocação de capital. Para além disso, o comportamento dos mercados financeiros são geralmente considerados como importantes medidas/sinais para a compreensão do estado atual e futuro das companhias e, em última análise, de todo o sistema económico e financeiro. Entretanto, a importância dos mercados financeiros poderá ser melhor compreendida quando os mercados não cumprem suas funções primordias, mais especificamente e de forma dramática quando ocorre um crash nos mercados financeiros. Em Setembro de 2008, por exemplo, uma série de eventos ameaçou a estabilidade do sistema financeiro mundial. Gigantescas empresas dos mercados financeiros inesperadamente falharam e tiveram de ser resgatadas pelos seus respectivos governos, enquanto que outras simplesmente entraram com pedido de falência. O sistema financeiro mundial esteve próximo do colapso. Embora o desastre tenha, de certa forma, sido evitado, o Crash de 2008 teve consequências imediatas, profundas, e duradouras para a economia mundial. A verdadeira compreensão dos mercados financeiros é de fato difícil. Diferentes hipóteses têm sido propostas para explicar o comportamento dos participantes dos mercados financeiros de forma individual bem como o comportamento dos mercados de forma global. Por um lado, teorias económicas tradicionais como, por exemplo, a Hipótese do Mercado Eficiente, tendem a considerar que os participantes dos mercados financeiros são racionais e que os mercados são eficientes. Entretanto, pesquisas na área de economia comportamental têm fornecido extensa e vasta evidência que demonstra que os participantes dos mercados financeiros possuem desvios comportamentais do chamado comportamento racional. Para além das evidências da economia comportamental, disciplinas tais como a neurociência cognitiva e a neuroeconomia têm clarificado a função das emoções (e.g., felicidade, tristeza, surpresa) no processo de raciocínio e tomada de decisão, aprendizado, bem como a importância das emoções no âmbito da memória humana, particularmente para os processos de codificação e recuperação de memórias. Por outro lado, teorias recentes como a Hipótese do Mercado Adaptativo tentam reconciliar a ideia de mercados eficientes com a economia comportamental, ao reconhecer a importância das emoções, a existência de desvios comportamentais, e a ocorrência de fenómenos e anomalias como as bolhas. O Crash de 2008 conjuntamente com novas evidências fornecidas por diferentes áreas têm salientado a necessidade de novas e interdisciplinares abordagens para o estudo de sistemas e problemas económicos e financeiros. Uma destas abordagens é o uso de Agent-based Financial Markets. Esta abordagem permite aos investigadores se distanciarem das tradicionais crenças a fim de testar novas hipóteses, conceitos, ideias, etc, tornando possível o projeto e realização de experimentos mais realistas e mais plausíveis em termos comportamentais. Esta tese está em linha com este contexto. Neste trabalho exploratório, nosso objetivo é investigar quais contribuições a aplicação de uma abordagem de modelação cognitiva pode trazer para a compreensão dos mercados financeiros, especificamente para o comportamento dos participantes dos mercados financeiros (individualmente) e dos mercados financeiros (globalmente). O ponto de partida é a criação de agentes artificiais com mecanismos similares aos ou inspirados nos usados pelos seres humanos de modo que seja possível conceber agentes artificiais cognitivos, i.e., agentes artificiais com diferentes sistemas de memórias e processos, com a capacidade de reconhecer, simular, e expressar emoções, diferentes processos de tomada de decisão, com a habilidade de receber e processar diferentes tipos de informação, e com a habilidade de aprender. Para este fim, nós primeiro concebemos um modelo cognitivo genérico individual dos participantes dos mercados financeiros (agentes humanos) intitulado TribeCA (Trading and investing with behavioral-emotional Cognitive Agents). O modelo cognitivo proposto é baseado na Belief-Desire Theory of Emotions (BDTE), no modelo cognitive-psychoevolutionary de surpresa proposto por Myer e colegas, e no modelo de surpresa artificial proposto por Macedo e Cardoso. De seguida nós fornecemos uma implementação do modelo proposto a qual foi posteriormente integrada a duas ferramentas utilizadas no contexto dos agent-based financial markets. A plataforma resultante permite o projeto e realização de uma variedade de experimentos económicos e financeiros com agentes artificiais cognitivos. Nós realizamos neste trabalho três experimentos com agentes e multi-agentes a fim de endereçar alguns aspectos fundamentais dos mercados financeiros tais como eficiência e racionalidade. Adicionalmente, nós realizamos dois estudos de casos a fim de comparar a perspectiva tradicional (económica e financeira) com a perspectiva da ciência cognitiva na modelação e computação da surpresa na economia e finança. Esta tese fornece contribuições para o avanço no projeto e realização de abordagens interdisciplinares para o estudo de sistemas ou problemas económicos e financeiros. Nosso modelo cognitivo genérico e sua implementação podem ser utilizados a fim de que sejam explorados outros aspectos dos mercados financeiros, para além dos que foram endereçados nest trabalho, e em outros modelos baseados em agentes. Nós consideramos que este trabalho abre um novo conjunto de possibilidades para investigações quer na academia quer na indústria. Ao final, nós poderemos obter uma melhor compreensão e entendimento sobre o comportamento dos participantes dos mercados financeiros (individualmente) bem como dos mercados financeiros (globalmente). Estas investigações poderão resultar, por exemplo, no desenvolvimento de novos (potencialmente melhores e altamente lucrativos) serviços financeiros para suportar o processo de tomada de decisão dos participantes dos mercados financeiros baseados nas suas emoções, comportamentos, etc...........................................................The financial markets are complex, dynamic, and strategic socio-economic systems in which a great number of heterogeneous market participants interact by essentially buying and selling assets of di ff erent types. The financial markets such as stock markets (e.g., S&P500) serve many functions. For instance, the financial markets help market participants and companies in improving the capital allocation process. Additionally, the behavior of the financial markets is assumed to be an important gauge for helping the understanding of the current and future state of companies and, ultimately, of the whole economic and financial system. However, the importance of the financial markets might be better noticed when they do not fulfill their primary functions, specifically and most dramatically when the financial markets crash. On September 2008, for example, a series of events threated the stability of the world’s financial system. Some gigantic financial services companies had unexpectedly failed and had to be rescued by governments while others simply filled for bankruptcy. The world’s financial system came close to a meltdown. Although disaster had somehow been averted due to a series of actions, the Crash of 2008 had immediate, profound, vast and long-lasting consequences for the world economy. The true understanding of the financial markets are indeed quite di ffi cult. Several hy- potheses have been proposed to try to explain the behavior of the market participants individually as well as the behavior of markets as a whole. On the one hand, tradi- tional economic theories such as the E ffi cient Market Hypothesis tend to assume that market participants are rational as well as that markets are e ffi cient. However, beha- vioral economics research has been providing extensive and vast evidence that market participants have what is known as behavioral biases, i.e., deviations from the so-called rational behavior. In addition to the behavioral economics evidence, disciplines such as cognitive neuroscience and neuroeconomics have been clarifying the role of emotions (e.g., happiness, unhappiness, surprise) for the human reasoning, memory system and processes, and decision-making process. For instance, emotions play a very important role in the memory processes of encoding and retrieving as well as are the basis of a sort of learning system. On the other hand, recent theories such as the Adaptive Market Hypothesis tries to reconciliate market e ffi ciency with behavioral economics by acknowledging the importance of emotions, the existence of behavioral biases, and the occurrence of interesting phenomena and anomalies such as bubbles. The Crash of 2008 together with new evidence provided by di ff erent research areas have been stressing the need for novel and interdisciplinary approaches for the study of economic and financial systems and problems. One of these approaches is the use of Agent-based Financial Markets. Agent-based Financial Markets allows researchers to depart from classical assumptions in order to test di ff erent hypotheses, concepts, ideas, etc, making it possible the design and realization of more realistic and behavioral plausible experiments. This thesis is in line with this context. In this exploratory work we aim to investigate which contributions the application of a cognitive modeling approach might bring to the understanding of the financial markets, specifically to the behavior of both market participants (individually) and the financial markets (globally). The starting point is to empower artificial agents with mechanisms similar to or inspired in those used by humans so that we have artificial cognitive agents, i.e., artificial agents with di ff erent memory systems and processes, the capacity of recognizing, simulating and expressing emotions, decision-making processes, the ability to receive and process di ff erent kinds of information, and the ability to learn. To this end, we first conceive a generic novel cognitive model of individual market participants (human agents) named TribeCA (Trading and investing with behavioral- emotional Cognitive Agents). TribeCA is based on the Belief-Desire Theory of Emo- tions (BDTE), on the cognitive-psychoevolutionary model of surprise proposed by Meyer and colleagues, and on the artificial surprise model proposed by Macedo and Cardoso. Then we provide an implementation of the proposed model which is later integrated into two tools used in the context of agent-based financial markets. The re- sulting platform allows the design and realization of a variety of economic and financial experiments with artificial cognitive agents. We carried out three agent and multi- agent based experiments to address some fundamental aspects regarding the financial markets such as e ffi ciency and rationality. Additionally, we carried out two case studies on comparing the traditional (economics and finance) perspective with the cognitive science perspective on modeling and computing surprise in economics and finance. This thesis provides contributions to the advance in the design and realization of in- terdisciplinary approaches to the study of economic and financial systems or problems. Our generic conceptual cognitive model and implementation might be used both to explore other aspects of the financial markets in addition to those addressed in this work and to other agent-based models. We consider this work opens up a set of novel possibilities for investigations in the academia and in the industry. In the end, we may have a better understanding of the behavior of market participants individually as well as of the financial markets globally. It has the potential to result in, for instance, the development of novel (potentially better and highly lucrative) financial services to support the market participants’ decision-making process based on his/her emotions, behavior, etc.FEDER - project TribeCA (Trading and investing with behavioralemotional Cognitive Agents

Architecting system of systems: artificial life analysis of financial market behavior

This research study focuses on developing a framework that can be utilized by system architects to understand the emergent behavior of system architectures. The objective is to design a framework that is modular and flexible in providing different ways of modeling sub-systems of System of Systems. At the same time, the framework should capture the adaptive behavior of the system since evolution is one of the key characteristics of System of Systems. Another objective is to design the framework so that humans can be incorporated into the analysis. The framework should help system architects understand the behavior as well as promoters or inhibitors of change in human systems. Computational intelligence tools have been successfully used in analysis of Complex Adaptive Systems. Since a System of Systems is a collection of Complex Adaptive Systems, a framework utilizing combination of these tools can be developed. Financial markets are selected to demonstrate the various architectures developed from the analysis framework --Introduction, page 3

The solar energy consumer agent decision (SECAD) model : addressing complexity through GIS-integrated agent-based modeling

textThis thesis presents a step-by-step implementation of the Solar Energy Consumer Agent Decision (SECAD) model: an empirically-grounded multi-agent model of residential solar photovoltaic (PV) adoption with an integrated geospatial topology. Solar PV diffusion is a complex system with geographic heterogeneity, uncertain information, high financial risk, and important social interaction and feedback effects between consumers. A key limitation for agentbased models in human socio-technical systems is the integration of empirical patterns in the model structure, initialization, and validation efforts. This limitation is addressed though highly granular and interlocking data-streams from the geographic, social network, financial, demographic, and decision-making process of real households in the study. The fitted and validation model is used to simulate implementation of potential policies to inform decision-makers: i) Targeted informational dissemination campaigns, ii) Tiered rebates, iii) Locational pricing, and iv) Alternative rebate schedules. Informational campaigns can increase cumulative installations by as much as 12%, but vary greatly in their effectiveness based on which agents are targeted. Simulations suggest that by lowering the cost barrier to lower wealth households through a slightly higher rebate (+$0.25/Watt), the mean difference in wealth between solar adopters and non adopters could be reduced by 22.6$0.25 higher offering increased the percentage of adopters in the target area from less than 1% to over 10%. Relative to flatter rebate schedules, sharply decreasing schedules are effective in terms of motivating adoption but inefficient in small markets. It is our hope that this work will provide a working example for other agent-based models of human socio-technical systems as well as provide insight into the likely outcomes of novel policy-levers such as those described above.Energy and Earth ResourcesPublic Affair