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Information
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Agent-Based Artificial Markets
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Agent-Based Artificial Markets

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (31 May 2018) | Viewed by 16171

Special Issue Editor

Prof. Dr. Philippe Mathieu

E-Mail Website
Guest Editor
Department of Computer Science, Lille University, Cité Scientifique, 59650 Villeneuve-d’Ascq, France
Interests: artificial intelligence; multi-agent systems; individual based simulation; agent based computational economics; game theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, economics and finance have a real advantage from a tremendous stream of innovations, notably coming from the computer science community. Recent advantages in information technologies, employed in the stock market, allow traders to analyze fundamental information, make trading decisions, and submit orders in fractions of seconds. This phenomenon impacts market quality, increases message traffic, makes market data extremely difficult to analyze, and requires effective regulatory design. Smart-grid, agent-based modeling, technical methods and smart order routing help academy, industry, government and authorities to reach a deeper understanding of financial markets as a complex system.

This Special Issue of the journal Information focuses on the application of agents and multi-agent systems, as well as all techniques in artificial intelligence applied to financial market issues. The aim is to explore the intersection of two research domains, financial economics and computer sciences. Areas of special interest include, but are not limited to, simulation, ACE, algorithmic trading, high-frequency trading, agent-based artificial markets, high performance trading, smart grids, design of artificial traders, market and policy design, auctions, matching mechanism designs, and economics education with ABM.

Prof. Philippe Mathieu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Agent-based Computational Economics
  • Financial Economics
  • Mechanism Design
  • Algorithmic Trading
  • High-frequency Trading
  • Behavioral Finance
  • Experimental Finance
  • Agent-based Computational Modeling
  • Market Microstructure
  • Algorithmic Finance
  • Smart Markets
  • Smart Grid Markets

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Published Papers (3 papers)

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Editorial

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2 pages, 137 KB  
Editorial
Editorial for the Special Issue on ‘Agent-Based Artificial Markets’
by Philippe Mathieu
Information 2018, 9(9), 225; https://doi.org/10.3390/info9090225 - 3 Sep 2018
Viewed by 2575
Abstract
n/a Full article
(This article belongs to the Special Issue Agent-Based Artificial Markets)

Research

Jump to: Editorial

18 pages, 876 KB  
Article
An Agent-Based Approach to Interbank Market Lending Decisions and Risk Implications
by Anqi Liu, Cheuk Yin Jeffrey Mo, Mark E. Paddrik and Steve Y. Yang
Information 2018, 9(6), 132; https://doi.org/10.3390/info9060132 - 29 May 2018
Cited by 12 | Viewed by 7341
Abstract
In this study, we examine the relationship of bank level lending and borrowing decisions and the risk preferences on the dynamics of the interbank lending market. We develop an agent-based model that incorporates individual bank decisions using the temporal difference reinforcement learning algorithm [...] Read more.
In this study, we examine the relationship of bank level lending and borrowing decisions and the risk preferences on the dynamics of the interbank lending market. We develop an agent-based model that incorporates individual bank decisions using the temporal difference reinforcement learning algorithm with empirical data of 6600 U.S. banks. The model can successfully replicate the key characteristics of interbank lending and borrowing relationships documented in the recent literature. A key finding of this study is that risk preferences at the individual bank level can lead to unique interbank market structures that are suggestive of the capacity with which the market responds to surprising shocks. Full article
(This article belongs to the Special Issue Agent-Based Artificial Markets)
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24 pages, 2753 KB  
Article
A Market-Based Optimization Approach for Domestic Thermal and Electricity Energy Management System: Formulation and Assessment
by Baptiste Feron and Antonello Monti
Information 2018, 9(5), 120; https://doi.org/10.3390/info9050120 - 15 May 2018
Cited by 3 | Viewed by 5543
Abstract
The increase of domestic electrical and thermal controllable devices and the emergence of dynamic electrical pricing leads to the opportunity to integrate and optimize electrical and thermal energy at a house level using a home energy management system (HEMS) in order to minimize [...] Read more.
The increase of domestic electrical and thermal controllable devices and the emergence of dynamic electrical pricing leads to the opportunity to integrate and optimize electrical and thermal energy at a house level using a home energy management system (HEMS) in order to minimize the energy costs. In the literature, optimization-based algorithms yielding 24-h schedules are used in spite of their growing complexity with the number of controllable devices and their sensitivity to forecast errors which leads, in most of the cases, to suboptimal schedules. To overcome this weakness, this paper introduces a domestic thermal and electrical control based on a market approach. In contrast with the optimization-based HEMS, the proposed market-based approach targets a scalable and reactive optimal control. This paper first formulates the market-based optimization problem with generality and discusses its optimality conditions with regards to the microeconomic theory. Secondly, this paper compares its optimality to an optimization-based approach and a rule-based approach under forecast errors using Monte Carlo simulations. Finally, this paper quantifies and identifies the effectiveness boundaries of the different approaches. Full article
(This article belongs to the Special Issue Agent-Based Artificial Markets)
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