19,297 research outputs found
Economics: The next physical science?
We review an emerging body of work by physicists addressing questions of
economic organization and function. We suggest that, beyond simply employing
models familiar from physics to economic observables, remarkable regularities
in economic data may suggest parts of social order that can usefully be
incorporated into, and in turn can broaden, the conceptual structure of
physics.Comment: 9 pages, 6 figures, to appear in Physics Toda
Building an Artificial Stock Market Populated by Reinforcement-Learning Agents
In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward-looking behaviour is driven by the reinforcement learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self-regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision making, application of the Q-learning algorithm for driving individual behaviour, and rich market setup.agent-based financial modelling, artificial stock market, complex dynamical system, emergent properties, market efficiency, agent heterogeneity, reinforcement learning
Introduction to the special issue on neural networks in financial engineering
There are several phases that an emerging field goes through before it reaches maturity, and computational finance is no exception. There is usually a trigger for the birth of the field. In our case, new techniques such as neural networks, significant progress in computing technology, and the need for results that rely on more realistic assumptions inspired new researchers to revisit the traditional problems of finance, problems that have often been tackled by introducing simplifying assumptions in the past. The result has been a wealth of new approaches to these time-honored problems, with significant improvements in many cases
Artificial Agents and Speculative Bubbles
Pertaining to Agent-based Computational Economics (ACE), this work presents
two models for the rise and downfall of speculative bubbles through an exchange
price fixing based on double auction mechanisms. The first model is based on a
finite time horizon context, where the expected dividends decrease along time.
The second model follows the {\em greater fool} hypothesis; the agent behaviour
depends on the comparison of the estimated risk with the greater fool's.
Simulations shed some light on the influent parameters and the necessary
conditions for the apparition of speculative bubbles in an asset market within
the considered framework
The Opinion Game: Stock price evolution from microscopic market modelling
We propose a class of Markovian agent based models for the time evolution of
a share price in an interactive market. The models rely on a microscopic
description of a market of buyers and sellers who change their opinion about
the stock value in a stochastic way. The actual price is determined in
realistic way by matching (clearing) offers until no further transactions can
be performed. Some analytic results for a non-interacting model are presented.
We also propose basic interaction mechanisms and show in simulations that these
already reproduce certain particular features of prices in real stock markets.Comment: 14 pages, 5 figure
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