Results 1 - 10 of 42

Agent-based computational economics: Growing economies from the bottom-up

by Leigh Tesfatsion - Artificial Life , 2002
"... Abstract: Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization to economics of the basic complex adaptive systems paradigm. This study outlines the main objectives and defining ch ..."
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Abstract: Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization to economics of the basic complex adaptive systems paradigm. This study outlines the main objectives and defining characteristics of the ACE methodology, and discusses similarities and distinctions between ACE and artificial life research. Eight ACE research areas are identified, and a number of publications in each area are highlighted for concrete illustration. Open questions and directions for future ACE research are also considered. The study concludes with a discussion of the potential benefits associated with ACE modeling, as well some potential difficulties. Keywords: Agent-based computational economics; artificial life; learning; evolution of norms; markets; networks; parallel experiments with humans and computational agents; computational laboratories. 1
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Agent-Based Computational Economics: Modeling Economies as Complex Adaptive Systems

by Leigh Tesfatsion - Information Sciences , 2003
"... Agent-based computational economics (ACE) is the computational study of economies modelled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization to economics of the basic complex adaptive systems paradigm. This paper outlines the main objectives and defining characteris ..."
Abstract - Cited by 35 (0 self) - Add to MetaCart
Agent-based computational economics (ACE) is the computational study of economies modelled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization to economics of the basic complex adaptive systems paradigm. This paper outlines the main objectives and defining characteristics of the ACE methodology, and discusses several active research areas. Key words: Agent-based computational economics, complex adaptive systems 1
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Agent-based models of financial markets

by E. Samanidou, E. Zschischang, D. Stauffer, T. Lux - REPORTS ON PROGRESS IN PHYSICS 70 , 2007
"... This review deals with several microscopic (“agent-based”) models of financial markets which have been studied by economists and physicists over the last decade: Kim-Markowitz, Levy-Levy-Solomon, Cont-Bouchaud, Solomon-Weisbuch, Lux-Marchesi, Donangelo-Sneppen and Solomon-Levy-Huang. After an overvi ..."
Abstract - Cited by 27 (0 self) - Add to MetaCart
This review deals with several microscopic (“agent-based”) models of financial markets which have been studied by economists and physicists over the last decade: Kim-Markowitz, Levy-Levy-Solomon, Cont-Bouchaud, Solomon-Weisbuch, Lux-Marchesi, Donangelo-Sneppen and Solomon-Levy-Huang. After an overview of simulation approaches in financial economics, we first give a summary of the Donangelo-Sneppen model of monetary exchange and compare it with related models in economics literature. Our selective review then outlines the main ingredients of some influential early models of multi-agent dynamics in financial markets (Kim-Markowitz, Levy-Levy-Solomon). As will be seen, these contributions draw their inspiration from the complex appearance of investors ’ interactions in real-life markets. Their main aim is to reproduce (and, thereby, provide possible explanations) for the spectacular bubbles and crashes seen in certain historical
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Modeling Chaotic Behavior of Stock Indices Using Intelligent Paradigms

by Ajith Abraham, Ninan Sajith Philip, P. Saratchandran - International Journal of Neural, Parallel & Scientific Computations, USA, Volume 11, Issue , 2003
"... The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using several connectionist paradigms and soft computing techniques. To demonstrate the different techn ..."
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The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using several connectionist paradigms and soft computing techniques. To demonstrate the different techniques, we considered Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index. We analyzed 7 year's Nasdaq 100 main index values and 4 year's NIFTY index values. This paper investigates the development of a reliable and efficient technique to model the seemingly chaotic behavior of stock markets. We considered an artificial neural network trained using Levenberg-Marquardt algorithm, Support Vector Machine (SVM), Takagi-Sugeno neuro- fuzzy model and a Difference Boosting Neural Network (DBNN). This paper briefly explains how the different connectionist paradigms could be formulated using different learning methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the connectionist paradigms considered could represent the stock indices behavior very accurately.
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Agent-Based Computational Economics

by Leigh Tesfatsion - ISU Economics Working Paper Number 1 , 2002
"... Abstract: Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly inter ..."
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Abstract: Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This chapter discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research. Keywords: Agent-based computational economics; autonomous agents; interaction networks; learning; evolution; mechanism design; computational experiments; object-oriented programming. 1
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The Virtues and Vices of Equilibrium and the future of financial economics

by J. Doyne Farmer, John Geanakoplos , 2009
"... The use of equilibrium models in economics springs from the desire for parsimonious models of economic phenomena that take human reasoning into account. This approach has been the cornerstone of modern economic theory. We explain why this is so, extolling the virtues of equilibrium theory; then we p ..."
Abstract - Cited by 24 (1 self) - Add to MetaCart
The use of equilibrium models in economics springs from the desire for parsimonious models of economic phenomena that take human reasoning into account. This approach has been the cornerstone of modern economic theory. We explain why this is so, extolling the virtues of equilibrium theory; then we present a critique and describe why this approach is inherently limited, and why economics needs to move in new directions if it is to continue to make progress. We stress that this shouldn’t be a question of dogma, and should be resolved empirically. There are situations where equilibrium models provide useful predictions and there are situations where they can never provide useful predictions. There are also many situations where the jury is still out,i.e.,where so far they fail to provide a good description of the world, but where proper extensions might change this. Our goal is to convince the skeptics that equilibrium models can be useful, but also to make traditional economists more aware of the limitations of equilibrium models.We sketch some alternative approaches and discuss why they should play an important role in
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Building the santa fe artificial stock market. Working Paper, Graduate

by Blake Lebaron - School of International Economics and Finance, Brandeis , 2002
"... This short summary presents an insider’s look at the construction of the Santa Fe artificial stock market. The perspective considers the many design questions that went into building the market from the perspective of a decade of experience with agent-based financial markets. The market is assessed ..."
Abstract - Cited by 24 (0 self) - Add to MetaCart
This short summary presents an insider’s look at the construction of the Santa Fe artificial stock market. The perspective considers the many design questions that went into building the market from the perspective of a decade of experience with agent-based financial markets. The market is assessed based on its overall strengths and weaknesses.

A Heterogeneous, Endogenous and Co-evolutionary GP-based Financial Market

by Serafin Martinez-Jaramillo, Edward P. K. Tsang
"... Stock markets are very important in modern societies and their behaviour have serious implications in a wide spectrum of the world’s population. Investors, governing bodies and the society as a whole could benefit from better understanding of the behavior of stock markets. The traditional approach t ..."
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Stock markets are very important in modern societies and their behaviour have serious implications in a wide spectrum of the world’s population. Investors, governing bodies and the society as a whole could benefit from better understanding of the behavior of stock markets. The traditional approach to analyze such systems is the use of analytical models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions, such as perfect rationality and homogeneous investors, which threaten the validity of analytical results. This motivates alternative methods. In this work, we developed an artificial financial market and used it to study the behavior of stock markets. In this market, we model technical, fundamental and noise traders. The technical traders are sophisticated genetic programming based agents that co-evolve (by means of their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. With this endogenous artificial market, we identified conditions under which the statistical properties of price series in the artificial market resembles those of the real financial markets. Additionally, we modeled the pressure to beat the market by a behavioral constraint imposed on the agents reflecting the Red Queen principle in evolution. We have demonstrated how evolutionary computation could play a key role in studying stock markets.

Agent-based Financial Markets: Matching Stylized Facts with Style

by Blake Lebaron , 2004
"... Empirical facts from financial data pose some of the most difficult puzzles for equilibrium macroeco-nomic modeling. Features such as volatility, excess kurtosis, and conditional heteroscedasticity are not easily replicated by any single representative agent model. Most agent-based financial markets ..."
Abstract - Cited by 10 (2 self) - Add to MetaCart
Empirical facts from financial data pose some of the most difficult puzzles for equilibrium macroeco-nomic modeling. Features such as volatility, excess kurtosis, and conditional heteroscedasticity are not easily replicated by any single representative agent model. Most agent-based financial markets are able to match a good subset of these features quite easily. This paper will summarize some of the results from an agent-based model. It will be argued that agent-based approaches also make more sense economically then their representative agent competition. They will also be compared and contrasted with approaches coming from the behavioral finance perspective as well.

AUTOMATED TRADING WITH BOOSTING AND EXPERT WEIGHTING

by Germán Creamer
"... We propose a multi-stock automated trading system that relies on a layered structure consisting of a machine learning algorithm, an online learning utility, and a risk management overlay. Alternating decision tree (ADT), which is implemented with Logitboost, was chosen as the underlying algorithm. O ..."
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We propose a multi-stock automated trading system that relies on a layered structure consisting of a machine learning algorithm, an online learning utility, and a risk management overlay. Alternating decision tree (ADT), which is implemented with Logitboost, was chosen as the underlying algorithm. One of the strengths of our approach is that the algorithm is able to select the best combination of rules derived from well-known technical analysis indicators and is also able to select the best parameters of the technical indicators. Additionally, the online learning layer combines the output of several ADTs and suggests a short or long position. Finally, the risk management layer can validate the trading signal when it exceeds a specified non-zero threshold and limit the application of our trading strategy when it is not profitable. We test the expert weighting algorithm with data of 100 randomly selected companies of the S&P 500 index during the period 2003-2005. We find that this algorithm generates abnormal returns during the test period. Our experiments show that the boosting approach is able to improve the predictive capacity when indicators are combined and aggregated as a single predictor. Even more, the combination of indicators of different stocks demonstrated to be adequate in order to reduce the use of computational resources, and still maintain an adequate predictive capacity. KEY WORDS Automated trading, machine learning, algorithmic trading, boosting. 1
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