Special Issue "AI and Financial Markets II"

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Technology and Innovation".

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Prof. Dr. Shigeyuki Hamori
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Guest Editor
Graduate School of Economics, Kobe University, Rokkodai, Nada-Ku, Kobe 657-8504, Japan
Interests: applied time series analysis; empirical finance; data science; international financeapplied time series analysis; empirical finance; data science; international finance
Special Issues and Collections in MDPI journals
Prof. Dr. Tetsuya Takiguchi
Website
Guest Editor
Graduate School of System Informatics, Kobe University 1-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan
Interests: signal processing, machine learning, pattern recognition, statistical modeling
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine—particularly, an intelligent computer program. Machine learning is an approach to realize AI and is a collection of statistical algorithms. Because of the rapid development of computer technology, machine learning has been actively explored for a variety of academic and practical purposes in financial markets. This Special Issue focuses on the broad topic of “AI and Financial Markets” and includes novel research associated with this topic. Articles on the application of AI to financial markets are welcome.

The Special Issue could include contributions on the application of AI to asset return forecasting, volatility forecasting, portfolio allocation, market risk, credit analysis, and so on.

Prof. Dr. Shigeyuki Hamori
Prof. Dr. Tetsuya Takiguchi
Guest Editors

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 papers will be 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. Journal of Risk and Financial Management 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 1200 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

  • Artificial intelligence (AI)
  • Machine learning
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Regression
  • Classification
  • Deep learning
  • Asset return forecasting
  • Volatility forecasting
  • Portfolio allocation
  • Market risk
  • High-frequency trading

Published Papers (1 paper)

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Research

Open AccessArticle
Latent Segmentation of Stock Trading Strategies Using Multi-Modal Imitation Learning
J. Risk Financial Manag. 2020, 13(11), 250; https://doi.org/10.3390/jrfm13110250 - 23 Oct 2020
Abstract
While exchanges and regulators are able to observe and analyze the individual behavior of financial market participants through access to labeled data, this information is not accessible by other market participants nor by the general public. A key question, then, is whether it [...] Read more.
While exchanges and regulators are able to observe and analyze the individual behavior of financial market participants through access to labeled data, this information is not accessible by other market participants nor by the general public. A key question, then, is whether it is possible to model individual market participants’ behaviors through observation of publicly available unlabeled market data alone. Several methods have been suggested in the literature using classification methods based on summary trading statistics, as well as using inverse reinforcement learning methods to infer the reward function underlying trader behavior. Our primary contribution is to propose an alternative neural network based multi-modal imitation learning model which performs latent segmentation of stock trading strategies. As a result that the segmentation in the latent space is optimized according to individual reward functions underlying the order submission behaviors across each segment, our results provide interpretable classifications and accurate predictions that outperform other methods in major classification indicators as verified on historical orderbook data from January 2018 to August 2019 obtained from the Tokyo Stock Exchange. By further analyzing the behavior of various trader segments, we confirmed that our proposed segments behaves in line with real-market investor sentiments. Full article
(This article belongs to the Special Issue AI and Financial Markets II)
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