Neural Networks and Learning Systems for Financial Risk Management
A special issue of FinTech (ISSN 2674-1032).
Deadline for manuscript submissions: closed (10 February 2023) | Viewed by 3335
Special Issue Editor
Interests: financial mathematics; artificial intelligence; neural networks for options; financial risk management; financial computing; financial data science; Markovian regime switching; high frequency trading; modeling of financial price; granular dynamics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Financial risk management is a process of identifying, evaluating, and controlling the risk in an investment. Financial risks can be broadly classified into three subclasses: credit risk, liquidity risk, and market risk. However, financial risk is such a complex and extensive concept that financial risk management practitioners often need to specialize only in a certain aspect of financial risk management. Notably, forecasting financial risk has become one of the main areas of probability and statistical modeling. In recent decades, artificial intelligence, including neural networks, deep learning, and machine learning, has seen significant progress and offered new opportunities for research in financial risk management. Many scholars have applied artificial natural networks and learning systems to construct financial risk prediction models with better forecast ability. The main goal of this Special Issue is to collect papers on the state of the art and the latest studies on neural networks and learning systems for financial risks and summarize different applications of artificial intelligence technologies in the relevant domains of financial risks and their management. Moreover, this issue is an opportunity to provide a forum where researchers will be able to share and exchange their ideas in the fields of financial risks. The area of interest is wide and includes several categories, such as neural networks and learning systems for financial derivatives, credit risk, liquidity risk, market risk, novel learning algorithms, the exploration of financial risk prediction, and so on.
Dr. David Liu
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. FinTech is an international peer-reviewed open access quarterly 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 1000 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
- finance
- credit risk
- liquid risk
- market risk
- investment
- neural network
- learning system
- risk management
- risk model
- financial derivatives
- artificial intelligence
- learning algorithms
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