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A reexamination of modern finance issues using Artificial Market Frameworks
No full textAgent-based mo deling (ABM) is widely used to study economic systems under a complex paradigm framework. Within this research stream, financial markets have received a lot of interest from academics and practitioners these last years, notably in offering an alternative to mathematical finance and financial econometrics. The traditional approach to analyzing such systems uses analytical models. The latter make simplifying assumptions, for example about perfect rationality homogeneity of market participants. These limitations motivate the use of alternative tools. Thus, disciplines such as Computational Economics and Computational Finance have gained attention and earned their place in the scientific arena. In this thesis we present an artificial stock market, called ATOM, and contribute to the understanding of some important issues regarding the construction of an abstract model of stock markets as well as a series of technical issues. In ATOM, we model a wide variety of trading strategies and market rules, that allows us to reexamine several traditional questions in finance within a totally different framework. Firstly, we investigate different conditions under which the statistical properties of an artificial stock market resemble those of real financial markets. To the best of our knowledge, this research is the first to clearly reproduce set of price dynamics at different granularities (intraday and extraday over several simulated years). We argue that generating realistic financial dynamics that reproduce quantitative financial distribution is out-of-reach within the pure zero-intelligent traders framework. Secondly, we increase agents' intelligence to address the problem of portfolio optimization at the level of individual strategies. We show that the higher relative risk aversion helps the agents earn higher Sharp e ratio and final wealth in the long-range. We also investigate the relation between rebalancing frequency and portfolio performance in low- and high-volatility market regimes with different transaction costs. Thirdly, we renew the analysis of classical questions in finance, namely, the relative performance of various investment strategies. For that purpose we compare rational mean-variance portfolio optimization versus "naive diversification". We test the investors' performance, each of them following a specific strategy, scrutinizing their behavior in ecological competitions where populations of artificial investors co evolve. Some investment strategies, followed by artificial traders, are based on different variations of canonical modern Markowitz portfolio theory, others on the "Naive" diversification principles, and others on combinations of sophisticated rational and naive strategies. Finally, we develop a new method for the determination of the upper bound in terms of maximum profit for any investment strategy applied in a given time window. We first describe this problem using a linear programming framework. Thereafter, we propose to embed this question in a graph theory framework as an optimal path problem in an oriented, weighted, bipartite network or in a weighted directed acyclic graph.Cette thèse apporte une contribution à la compréhension des dynamiques de marché et à la prise de décision des traders à l'aide d'une plateforme de simulation de marchés multi-agents. La modélisation multi-agents permet notamment d'étudier le système boursier comme un système complexe évolutif dans lequel chaque trader artificiel possède son propre comportement possède son propre comportement et qui, par ses prises de décision, influence l'ensemble des autres acteurs du système. Dans une première partie, nous mettons en évidence à l'aide de "traders à intelligence zéro" (ZIT), le rôle de la microstructure pour comprendre la nature des principaux faits stylisés de l'évolution des prix. Les résultats issus de nombreuses simulations, indiquent que l'usage des ZIT n'est pas suffisant pour reproduire de façon convaincante les évolutions de prix réels, car ceux-ci doivent être appréhendés à la fois de manière qualitative mais aussi quantitative. Nous montrons que seuls des éléments de stratégies de trading et une forte calibration peuvent améliorer cette réplication par simulation, suggérant que les aspects comportementaux importent tout autant que les aspects micro structurels. Dans une seconde partie, nous concentrons notre recherche sur la problématique de la rationalité dans le corpus de la théorie moderne du portefeuille. Le marché artificiel nous permet de tester si des stratégies naïves peuvent surpasser, en terme de performance, des modèles plus complexes. Diverses stratégies d'investissement sont implémentées dans le système artificiel et mises en interaction afin d'observer leur survie dans des compétitions écologiques basées sur leurs performances relatives. Certaines de ces stratégies d'investissements sont fondées sur des variations du modèle canonique de la théorie de portefeuilles de Markowitz, d'autres suivent des principes de diversification naïfs, d'autres encore obéissent à des combinaisons de stratégies rationnelles sophistiquées et de stratégies naïves. Enfin, de manière à mieux saisir les facteurs qui influent sur la performance du portefeuille, nous montrons les effets de la fréquence de pondération et des préférences pour le risque des investisseurs sur l'issue de ces compétitions. Pour finir, afin de fournir une mesure de performance absolue orientée vers l'évaluation ex-post d'un large éventail de stratégies de trading des investisseurs (agents dans notre cas) nous proposons un nouvel algorithme de complexité polynomiale permettant de déterminer la borne supérieure absolue des profits atteignables pour n'importe quelle stratégie sur une période de temps donnée. Cet algorithme met en contact deux champs a priori éloignés: la théorie des graphes d'une part et la finance computationnelle d'autre part
A reexamination of modern finance issues using Artificial Market Frameworks
No full textAgent-based mo deling (ABM) is widely used to study economic systems under a complex paradigm framework. Within this research stream, financial markets have received a lot of interest from academics and practitioners these last years, notably in offering an alternative to mathematical finance and financial econometrics. The traditional approach to analyzing such systems uses analytical models. The latter make simplifying assumptions, for example about perfect rationality homogeneity of market participants. These limitations motivate the use of alternative tools. Thus, disciplines such as Computational Economics and Computational Finance have gained attention and earned their place in the scientific arena. In this thesis we present an artificial stock market, called ATOM, and contribute to the understanding of some important issues regarding the construction of an abstract model of stock markets as well as a series of technical issues. In ATOM, we model a wide variety of trading strategies and market rules, that allows us to reexamine several traditional questions in finance within a totally different framework. Firstly, we investigate different conditions under which the statistical properties of an artificial stock market resemble those of real financial markets. To the best of our knowledge, this research is the first to clearly reproduce set of price dynamics at different granularities (intraday and extraday over several simulated years). We argue that generating realistic financial dynamics that reproduce quantitative financial distribution is out-of-reach within the pure zero-intelligent traders framework. Secondly, we increase agents' intelligence to address the problem of portfolio optimization at the level of individual strategies. We show that the higher relative risk aversion helps the agents earn higher Sharp e ratio and final wealth in the long-range. We also investigate the relation between rebalancing frequency and portfolio performance in low- and high-volatility market regimes with different transaction costs. Thirdly, we renew the analysis of classical questions in finance, namely, the relative performance of various investment strategies. For that purpose we compare rational mean-variance portfolio optimization versus "naive diversification". We test the investors' performance, each of them following a specific strategy, scrutinizing their behavior in ecological competitions where populations of artificial investors co evolve. Some investment strategies, followed by artificial traders, are based on different variations of canonical modern Markowitz portfolio theory, others on the "Naive" diversification principles, and others on combinations of sophisticated rational and naive strategies. Finally, we develop a new method for the determination of the upper bound in terms of maximum profit for any investment strategy applied in a given time window. We first describe this problem using a linear programming framework. Thereafter, we propose to embed this question in a graph theory framework as an optimal path problem in an oriented, weighted, bipartite network or in a weighted directed acyclic graph.Cette thèse apporte une contribution à la compréhension des dynamiques de marché et à la prise de décision des traders à l'aide d'une plateforme de simulation de marchés multi-agents. La modélisation multi-agents permet notamment d'étudier le système boursier comme un système complexe évolutif dans lequel chaque trader artificiel possède son propre comportement possède son propre comportement et qui, par ses prises de décision, influence l'ensemble des autres acteurs du système. Dans une première partie, nous mettons en évidence à l'aide de "traders à intelligence zéro" (ZIT), le rôle de la microstructure pour comprendre la nature des principaux faits stylisés de l'évolution des prix. Les résultats issus de nombreuses simulations, indiquent que l'usage des ZIT n'est pas suffisant pour reproduire de façon convaincante les évolutions de prix réels, car ceux-ci doivent être appréhendés à la fois de manière qualitative mais aussi quantitative. Nous montrons que seuls des éléments de stratégies de trading et une forte calibration peuvent améliorer cette réplication par simulation, suggérant que les aspects comportementaux importent tout autant que les aspects micro structurels. Dans une seconde partie, nous concentrons notre recherche sur la problématique de la rationalité dans le corpus de la théorie moderne du portefeuille. Le marché artificiel nous permet de tester si des stratégies naïves peuvent surpasser, en terme de performance, des modèles plus complexes. Diverses stratégies d'investissement sont implémentées dans le système artificiel et mises en interaction afin d'observer leur survie dans des compétitions écologiques basées sur leurs performances relatives. Certaines de ces stratégies d'investissements sont fondées sur des variations du modèle canonique de la théorie de portefeuilles de Markowitz, d'autres suivent des principes de diversification naïfs, d'autres encore obéissent à des combinaisons de stratégies rationnelles sophistiquées et de stratégies naïves. Enfin, de manière à mieux saisir les facteurs qui influent sur la performance du portefeuille, nous montrons les effets de la fréquence de pondération et des préférences pour le risque des investisseurs sur l'issue de ces compétitions. Pour finir, afin de fournir une mesure de performance absolue orientée vers l'évaluation ex-post d'un large éventail de stratégies de trading des investisseurs (agents dans notre cas) nous proposons un nouvel algorithme de complexité polynomiale permettant de déterminer la borne supérieure absolue des profits atteignables pour n'importe quelle stratégie sur une période de temps donnée. Cet algorithme met en contact deux champs a priori éloignés: la théorie des graphes d'une part et la finance computationnelle d'autre part
Market structure or traders’ behavior? An assessment of flash crash phenomena and their regulation based on a multi-agent simulation
No full textThis paper aims at studying the flash crash caused by an operational shock with different market participants. We reproduce this shock in artificial market framework to study market quality in different scenarios, with or without strategic traders. We show that traders’ srategies influence the magnitude of the collapse.But, with the help of zero-intelligence traders framework, we show that despite theabsence of market makers, the order-driven market is resilient and favors a price recovery. We find that a short-sales ban imposed by regulator reduces short-term volatility
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Who gains and who loses on stock markets? Risk preferences and timing matter
No full textInternational audienceThis paper uses an agent-based multi-asset model to examine the effect of risk preferences and optimal rebalancing frequency on performance measures while tracking profit and risk-adjusted return. We focus on the evolution of portfolios managed by heterogeneous mean-variance optimizers with a quadratic utility function under different market conditions. We show that patient and risk-averse agents are able to outperform aggressive risk-takers in the long-run. Our findings also suggest that the trading frequency determined by the optimal tolerance for the deviation from portfolio targets should be derived from a tradeoff between rebalancing benefits and rebalancing costs. In a relatively calm market, the absolute range of 6% to 8% and the complete-way back rebalancing technique outperforms others. During particular turbulent periods, however, none of the existing rebalancing techniques improves tax-adjusted profits and risk-adjusted returns simultaneously
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