8,794 research outputs found
How to Identify Investor's types in real financial markets by means of agent based simulation
The paper proposes a computational adaptation of the principles underlying
principal component analysis with agent based simulation in order to produce a
novel modeling methodology for financial time series and financial markets.
Goal of the proposed methodology is to find a reduced set of investor s models
(agents) which is able to approximate or explain a target financial time
series. As computational testbed for the study, we choose the learning system L
FABS which combines simulated annealing with agent based simulation for
approximating financial time series. We will also comment on how L FABS s
architecture could exploit parallel computation to scale when dealing with
massive agent simulations. Two experimental case studies showing the efficacy
of the proposed methodology are reported.Comment: 18 pages, in pres
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
Finance is a particularly difficult playground for deep reinforcement
learning. However, establishing high-quality market environments and benchmarks
for financial reinforcement learning is challenging due to three major factors,
namely, low signal-to-noise ratio of financial data, survivorship bias of
historical data, and model overfitting in the backtesting stage. In this paper,
we present an openly accessible FinRL-Meta library that has been actively
maintained by the AI4Finance community. First, following a DataOps paradigm, we
will provide hundreds of market environments through an automatic pipeline that
collects dynamic datasets from real-world markets and processes them into
gym-style market environments. Second, we reproduce popular papers as stepping
stones for users to design new trading strategies. We also deploy the library
on cloud platforms so that users can visualize their own results and assess the
relative performance via community-wise competitions. Third, FinRL-Meta
provides tens of Jupyter/Python demos organized into a curriculum and a
documentation website to serve the rapidly growing community. FinRL-Meta is
available at: https://github.com/AI4Finance-Foundation/FinRL-MetaComment: NeurIPS 2022 Datasets and Benchmarks. 36th Conference on Neural
Information Processing Systems Datasets and Benchmarks Trac
Agent-based Computational Economics: a Methodological Appraisal
This paper is an overview of "Agent-based Computational Economics (ACE)", an emerging approach to the study of decentralized market economies, in methodological perspective. It summarizes similarities and differences with respect to conventional economic models, outlines the unique methodological characteristics of this approach, and discusses its implications for economic methodology as a whole. While ACE rejoins the reflection on the unintended social consequences of purposeful individual action which is constitutive of economics as a discipline, the paper shows that it complements state-of the-art research in experimental and behavioral economics. In particular, the methods and techniques of ACE have reinforced the laboratory finding that fundamental economic results rely less on rational choice theory than is usually assumed, and have provided insight into the importance of market structures and rules in addition to individual choice. In addition, ACE has enlarged the range of inter-individual interactions that are of interest for economists. In this perspective, ACE provides the economist‘s toolbox with valuable supplements to existing economic techniques rather than proposing a radical alternative. Despite some open methodological questions, it has potential for better integration into economics in the future.Agent-based Computational Economics, Economic Methodology, Experimental Economics.
Dynamic Datasets and Market Environments for Financial Reinforcement Learning
The financial market is a particularly challenging playground for deep
reinforcement learning due to its unique feature of dynamic datasets. Building
high-quality market environments for training financial reinforcement learning
(FinRL) agents is difficult due to major factors such as the low
signal-to-noise ratio of financial data, survivorship bias of historical data,
and model overfitting. In this paper, we present FinRL-Meta, a data-centric and
openly accessible library that processes dynamic datasets from real-world
markets into gym-style market environments and has been actively maintained by
the AI4Finance community. First, following a DataOps paradigm, we provide
hundreds of market environments through an automatic data curation pipeline.
Second, we provide homegrown examples and reproduce popular research papers as
stepping stones for users to design new trading strategies. We also deploy the
library on cloud platforms so that users can visualize their own results and
assess the relative performance via community-wise competitions. Third, we
provide dozens of Jupyter/Python demos organized into a curriculum and a
documentation website to serve the rapidly growing community. The open-source
codes for the data curation pipeline are available at
https://github.com/AI4Finance-Foundation/FinRL-MetaComment: 49 pages, 15 figures. arXiv admin note: substantial text overlap with
arXiv:2211.0310
Computational Explorations of Information and Mechanism Design in Markets
Markets or platforms assemble multiple selfishly-motivated and strategic agents. The outcomes of such agent interactions depend heavily on the rules, regulations, and norms of the platform, as well as the information available to agents. This thesis investigates the design and analysis of mechanisms and information structures through the ``computational lens\u27\u27 in both static and dynamic settings. It both addresses the outcome of single platforms and fills a gap in the study of the dynamics of multiple platform interactions.
In static market settings, we are particularly interested in the role of information, because mechanisms are harder to change than the information available to participants. We approach information design through specific examples, i.e., matching markets and auction markets. First, in matching markets, we study the situation where the matching is preceded by a costly interviewing stage in which firms acquire information about the qualities of candidates. We focus on the impact of the signals of quality available prior to the interviewing stage. We show that more ``commonality\u27\u27 in the quality of information can be harmful, yielding fewer matches. Second, in auction markets, we design an information environment for revenue enhancement in a sealed-bid second price auction. Much of the previous literature has focused on signal design in settings where bidders are symmetrically informed, or on the design of optimal mechanisms under fixed information structures. Here, we provide new theoretical insights for complex situations like corporate mergers, where the sender of the signal has the opportunity to communicate in different ways to different receivers.
Next, in dynamic markets, we focus on two dimensions: (1) the effects of different market-clearing rules on market outcomes and (2) the dynamics of multiple platform interactions. Considering both dimensions, we investigate two important real-world dynamic markets: kidney exchange and financial markets. Specifically, in kidney exchange, we analyze the performance of different market-clearing algorithms and design a competing-market model to quantify the social welfare loss caused by market competition and exchange fragmentation. Here, we present the first analysis of equilibrium behavior in these dynamic competing matching market systems, from the viewpoints of both agents and markets. To improve the performance of kidney exchange in terms of both social welfare and individual utility, we analyze the benefit of convincing directed donation pairs to participate in paired kidney exchange, measured in terms of long-term graft survival. We provide the first empirical evidence that including compatible pairs dramatically benefits both social welfare and individual outcomes.
For financial markets, in the debate over high frequency trading, the frequent call (Call) mechanism has recently received considerable attention as a proposal for replacing the continuous double auction (CDA) mechanisms that currently run most financial markets. We examine agents\u27 profit under CDA and frequent call auctions in a dynamic environment. We design an agent-based model to study the competition between these two market policies and show that CALL markets can drive trade away from CDAs. The results help to inform this very important debate
Informational Substitutes
We propose definitions of substitutes and complements for pieces of
information ("signals") in the context of a decision or optimization problem,
with game-theoretic and algorithmic applications. In a game-theoretic context,
substitutes capture diminishing marginal value of information to a rational
decision maker. We use the definitions to address the question of how and when
information is aggregated in prediction markets. Substitutes characterize
"best-possible" equilibria with immediate information aggregation, while
complements characterize "worst-possible", delayed aggregation. Game-theoretic
applications also include settings such as crowdsourcing contests and Q\&A
forums. In an algorithmic context, where substitutes capture diminishing
marginal improvement of information to an optimization problem, substitutes
imply efficient approximation algorithms for a very general class of (adaptive)
information acquisition problems.
In tandem with these broad applications, we examine the structure and design
of informational substitutes and complements. They have equivalent, intuitive
definitions from disparate perspectives: submodularity, geometry, and
information theory. We also consider the design of scoring rules or
optimization problems so as to encourage substitutability or complementarity,
with positive and negative results. Taken as a whole, the results give some
evidence that, in parallel with substitutable items, informational substitutes
play a natural conceptual and formal role in game theory and algorithms.Comment: Full version of FOCS 2016 paper. Single-column, 61 pages (48 main
text, 13 references and appendix
Agent-Based Models and Human Subject Experiments
This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms
The virtues and vices of equilibrium and the future of financial economics
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, but 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 future
research in economics.Comment: 68 pages, one figur
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