Higher-Order Simulations: Strategic Investment Under Model-Induced Price Patterns

The trading and investment decision processes in financial markets become ever more dependent on the use of valuation and risk models. In the case of risk management for instance, modelling practice has become quite homogeneous and the question arises as to the effect this has on the price formation process. Furthermore, sophisticated investors who have private information about the use and characteristics of these models might be able to make superior gains in such an environment. The aim of this article is to test this hypothesis in a stylised market, where a strategic investor trades on information about the valuation and risk management models used by other market participants. Simulation results show that under certain market conditions, such a \'higher-order\' strategy generates higher profits than standard fundamental and momentum strategies that do not draw on information about model use.Financial Markets, Multi-Agent Simulation, Performativity, Higher-Order Strategies

Higher-order simulations: Strategic investment under model-induced market structures

The trading and investment decision processes in financial markets becomes ever more dependent on the use of valuation and risk models. In certain, cases such as risk management, modelling practice has become so homogeneous that one is led to ask about the effect this has on the price formation process. Furthermore, should stable price patterns emerge from this, can sophisticated investors who have private information about the use and characteristics of these models make superior gains? The aim of this article is to test this hypothesis in a stylised market environment, where a strategic trader who trades on information about the valuation and risk management models used by competitors. Results show that for our particular market setting, such a strategy has an advantage over those that do not use this information

Using realistic trading strategies in an agent-based stock market model

The use of agent-based models (ABMs) has increased in the last years to simulate social systems and, in particular, financial markets. ABMs of financial markets are usually validated by checking the ability of the model to reproduce a set of empirical stylised facts. However, other common-sense evidence is available which is often not taken into account, ending with models which are valid but not sensible. In this paper we present an ABM of a stock market which incorporates this type of common-sense evidence and implements realistic trading strategies based on practitioners literature. We next validate the model using a comprehensive approach consisting of four steps: assessment of face validity, sensitivity analysis, calibration and validation of model outputs

Simulación realista de los mercados financieros con sistemas multi-agentes

Els mercats financers són un exemple paradigmàtic de sistemes complexos adaptatius. Els paradigmes tradicionals de modelització no permeten reflexar aquesta realitat i hem de recórrer a noves eines. Presentem la simulació basada en sistemes multi-agents com el paradigma idoni per a analitzar els mercats financers en tota la seva complexitat. És un mètode que permet estudiar el comportament global del mercat a partir de la seva microestructura, i ja ha suggerit explicacions per a algunes de les regularitats estadístiques observades en una gran varietat de mercats. Exposem, a més amés, els primers passos en la construcció d'una simulació realista del mercat de bons.Financial markets are a paradigmatic case of complex adaptive systems. However, their complexity cannot be captured by traditional modelisation paradigms and we thus need to turn to new modelling tools. We present agent-based simulation as the suitable paradigm to analyse financial markets: this method allows to study the market macro behaviour on the basis of its microstructure, and some advances have already been done in the analysis and explanation of the stylised facts observed in a range of markets. We moreover describe the first steps we have undertaken to build a realistic simulation of a bond market

Higher-order simulations: Strategic investment under model-induced price patterns

The trading and investment decision processes in financial markets become ever more dependent on the use of valuation and risk models. In the case of risk management for instance, modelling practice has become quite homogeneous and the question arises as to the effect this has on the price formation process. Furthermore, sophisticated investors who have private information about the use and characteristics of these models might be able to make superior gains in such an environment. The aim of this article is to test this hypothesis in a stylised market, where a strategic investor trades on information about the valuation and risk management models used by other market participants. Simulation results show that under certain market conditions, such a'higher-order'strategy generates higher profits than standard fundamental and momentum strategies that do not draw on information about model use

Impact of value-at-risk models on market stability

Financial institutions around the world use value-at-risk (VaR) models to manage their market risk and calculate their capital requirements under Basel Accords. VaR models, as any other risk management system, are meant to keep financial institutions out of trouble by, among other things, guiding investment decisions within established risk limits so that the viability of a business is not put unduly at risk in a sharp market downturn. However, some researchers have warned that the widespread use of VaR models creates negative externalities in financial markets, as it can feed market instability and result in what has been called endogenous risk, that is, risk caused and amplified by the system itself, rather than being the result of an exogenous shock. This paper aims at analyzing the potential of VaR systems to amplify market disturbances with an agent-based model of fundamentalist and technical traders which manage their risk with a simple VaR model and must reduce their positions when the risk of their portfolio goes above a given threshold. We analyse the impact of the widespread use of VaR systems on different financial instability indicators and confirm that VaR models may induce a particular price dynamics that rises market volatility. These dynamics, which we have called `VaR cycles', take place when a sufficient number of traders reach their VaR limit and are forced to simultaneously reduce their portfolio; the reductions cause a sudden price movement, raise volatility and force even more traders to liquidate part of their positions. The model shows that market is more prone to suffer VaR cycles when investors use a short-term horizon to calculate asset volatility or a not-too-extreme value for their risk threshold

Simulación basada en agentes del efecto inestabilizador de las técnicas VaR

En los últimos años ha habido un gran número de crisis financieras con graves efectos en la economía de los países afectados. Para evitar o minimizar estos efectos negativos es necesario entender qué factores pueden desencadenar una crisis financiera. Sin embargo, la literatura sólo ofrece explicaciones de tipo cualitativo o modelos analíticos muy estilizados que resultan poco operativos. Proponemos en este artículo un modelo basado en agentes que permite estudiar, mediante simulación, los efectos agregados que emergen de la interacción de los inversores de un mercado financiero. Nuestro objetivo es analizar con este modelo la influencia en la dinámica de un mercado del uso generalizado de modelos basados en técnicas VaR de gestión de riesgo. Los resultados de la simulación corroboran la tesis que señala el uso homogéneo de estos modelos como uno de los factores que pueden inducir episodios de inestabilidad financiera

Impact of Basel III Countercyclical Measures on Financial Stability: An Agent-Based Model

The financial system is inherently procyclical, as it amplifies the course of economic cycles, and precisely one of the factors that has been suggested to exacerbate this procyclicality is the Basel regulation on capital requirements. After the recent credit crisis, international regulators have turned their eyes to countercyclical regulation as a solution to avoid similar episodes in the future. Countercyclical regulation aims at preventing excessive risk taking during booms to reduce the impact of losses suffered during recessions, for example increasing the capital requirements during the good times to improve the resilience of financial institutions at the downturn. The Basel Committee has already moved forward towards the adoption of countercyclical measures on a global scale: the Basel III Accord, published in December 2010, revises considerably the capital requirement rules to reduce their procyclicality. These new countercyclical measures will not be completely implemented until 2019, so their impact cannot be evaluated yet, and it is a crucial question whether they will be effective in reducing procyclicality and the appearance of crisis episodes such as the one experienced in 2007-08. For this reason, we present in this article an agent-based model aimed at analysing the effect of two countercyclical mechanisms introduced in Basel III: the countercyclical buffer and the stressed VaR. In particular, we focus on the impact of these mechanisms on the procyclicality induced by market risk requirements and, more specifically, by value-at-risk models, as it is a issue of crucial importance that has received scant attention in the modeling literature. The simulation results suggest that the adoption of both of these countercyclical measures improves market stability and reduces the emergence of crisis episodes

A game based approach to improve traders' decision-making

Purpose: The development of a game based approach to improving the decision-making capabilities of financial traders through attention to improving the regulation of emotions during trading. Design/methodology/approach: The project used a design-based research approach to integrate the contributions of a highly inter-disciplinary team. The approach was underpinned by considerable stakeholder engagement to understand the ‘ecology of practices’ in which this learning approach should be embedded. Findings: Taken together, our 35 laboratory, field and evaluation studies provide much support for the validity of our game based learning approach, the learning elements which make it up, and the value of designing game-based learning to fit within an ecology of existing practices. Originality/value: The novelty of the work described in the paper comes from the focus in this research project of combining knowledge and skills from multiple disciplines informed by a deep understanding of the context of application to achieve the successful development of a Learning Pathway, which addresses the transfer of learning to the practice environment Key words: Design-based research, emotion-regulation, disposition–effect, financial traders, serious games, sensor-based game

A learning design to support the emotion regulation of investors

We describe an approach to reducing systematic bias in financial decision-making. We explicitly reject an approach based on the transmission of propositional knowledge. Rather we develop a learning design which is founded in recent research on the role of emotions and their regulation in financial decision-making. We describe a pedagogic approach which supports the development of emotion-regulation skills in a serious game environment and supports their translation into ‘real-world’ tradin