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Neural Networks for Financial Derivatives
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Neural Networks for Financial Derivatives

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: closed (1 August 2023) | Viewed by 9072

Special Issue Editor

Dr. David Liu

E-Mail Website
Guest Editor
Department of Financial and Actuarial Mathematics, School of Science, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
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 the process of identifying, evaluating, and controlling the risk of 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 on financial risk management. Many scholars have applied artificial natural networks and learning systems to construct financial risk prediction models with better forecasting abilities. The main goal of this Special Issue is to collect papers on the state of the art, in addition to the latest studies on neural networks as well as 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 Special 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. 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 1400 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

Published Papers (4 papers)

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Research

19 pages, 3224 KiB  
Article
Enhancing Financial Market Analysis and Prediction with Emotion Corpora and News Co-Occurrence Network
by Shawn McCarthy and Gita Alaghband
J. Risk Financial Manag. 2023, 16(4), 226; https://doi.org/10.3390/jrfm16040226 - 04 Apr 2023
Cited by 3 | Viewed by 2659
Abstract
This study employs an improved natural language processing algorithm to analyze over 500,000 financial news articles from sixteen major sources across 12 sectors, with the top 10 companies in each sector. The analysis identifies shifting economic activity based on emotional news sentiment and [...] Read more.
This study employs an improved natural language processing algorithm to analyze over 500,000 financial news articles from sixteen major sources across 12 sectors, with the top 10 companies in each sector. The analysis identifies shifting economic activity based on emotional news sentiment and develops a news co-occurrence network to show relationships between companies even across sectors. This study created an improved corpus and algorithm to identify emotions in financial news. The improved method identified 18 additional emotions beyond what was previously analyzed. The researchers labeled financial terms from Investopedia to validate the categorization performance of the new method. Using the improved algorithm, we analyzed how emotions in financial news relate to market movement of pairs of companies. We found a moderate correlation (above 60%) between emotion sentiment and market movement. To validate this finding, we further checked the correlation coefficients between sentiment alone, and found that consumer discretionary, consumer staples, financials, industrials, and technology sectors showed similar trends. Our findings suggest that emotional sentiment analysis provide valuable insights for financial market analysis and prediction. The technical analysis framework developed in this study can be integrated into a larger investment strategy, enabling organizations to identify potential opportunities and develop informed strategies. The insights derived from the co-occurrence model may be leveraged by companies to strengthen their risk management functions, making it an asset within a comprehensive investment strategy. Full article
(This article belongs to the Special Issue Neural Networks for Financial Derivatives)
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14 pages, 887 KiB  
Article
Stock Portfolio Management by Using Fuzzy Ensemble Deep Reinforcement Learning Algorithm
by Zheng Hao, Haowei Zhang and Yipu Zhang
J. Risk Financial Manag. 2023, 16(3), 201; https://doi.org/10.3390/jrfm16030201 - 15 Mar 2023
Cited by 3 | Viewed by 2387
Abstract
The research objective of this article is to train a computer (agent) with market information data so it can learn trading strategies and beat the market index in stock trading without having to make any prediction on market moves. The approach assumes no [...] Read more.
The research objective of this article is to train a computer (agent) with market information data so it can learn trading strategies and beat the market index in stock trading without having to make any prediction on market moves. The approach assumes no trading knowledge, so the agent will only learn from conducting trading with historical data. In this work, we address this task by considering Reinforcement Learning (RL) algorithms for stock portfolio management. We first generate a three-dimension fuzzy vector to describe the current trend for each stock. Then the fuzzy terms, along with other stock market features, such as prices, volumes, and technical indicators, were used as the input for five algorithms, including Advantage Actor-Critic, Trust Region Policy Optimization, Proximal Policy Optimization, Actor-Critic Using Kronecker Factored Trust Region, and Deep Deterministic Policy Gradient. An average ensemble method was applied to obtain trading actions. We set SP100 component stocks as the portfolio pool and used 11 years of daily data to train the model and simulate the trading. Our method demonstrated better performance than the two benchmark methods and each individual algorithm without fuzzy extension. In practice, real market traders could use the trained model to make inferences and conduct trading, then retrain the model once in a while since training such models is time0consuming but making inferences is nearly simultaneous. Full article
(This article belongs to the Special Issue Neural Networks for Financial Derivatives)
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16 pages, 1517 KiB  
Article
Deconstructing Risk Factors for Predicting Risk Assessment in Supply Chains Using Machine Learning
by Guy Burstein and Inon Zuckerman
J. Risk Financial Manag. 2023, 16(2), 97; https://doi.org/10.3390/jrfm16020097 - 06 Feb 2023
Cited by 3 | Viewed by 2244
Abstract
Risk management is an ongoing process that includes several stages of mapping and identification, analysis, and evaluation, planning, and implementation to reduce risks and ensure ongoing control. Risk management along the supply chains has become more significant in recent years due to an [...] Read more.
Risk management is an ongoing process that includes several stages of mapping and identification, analysis, and evaluation, planning, and implementation to reduce risks and ensure ongoing control. Risk management along the supply chains has become more significant in recent years due to an increased complexity of the relationships between components in the chain as well as various disruptions such as climate change, COVID-19, or geo-political scenarios. The current literature alongside the increase in complexity and frequency of risk events, leads us to the single, most prominent challenge in risk management today: the auditor’s subjectivity in determining the risk levels. Simply stated, two different auditors may assess a given situation differently due to their specific history and experience. Specifically, it seems to be extremely difficult to find cases in which different auditors, working on the same organization, made the same risk assessment. With that in mind, this research aims to reduce the human subjectivity bias and reach a risk evaluation that is as objective as possible, by using the machine learning approach. For this aim the paper introduces a new risk assessment framework based on factors analysis and artificial neural network as the predictive model. We first introduced a new approach of deconstructing the risk factors into their basic elements and analyzing them as a feature vector. Next, we collected unique, real-world data of risk surveys and audit reports from 60 industrial companies of various industries (from plastic and metal factories to logistic and medical devices companies). Lastly, we constructed a neural network to predict the risk levels of operational processes in the industry. We trained our model on 42 samples and managed to achieve a R2 score of 0.9 on the test set of 18 samples. Our model was validated and managed to predict the risk accuracy with R = 0.95 in accordance with the human auditor results. Full article
(This article belongs to the Special Issue Neural Networks for Financial Derivatives)
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8 pages, 359 KiB  
Article
Newton–Raphson Emulation Network for Highly Efficient Computation of Numerous Implied Volatilities
by Geon Lee, Tae-Kyoung Kim, Hyun-Gyoon Kim and Jeonggyu Huh
J. Risk Financial Manag. 2022, 15(12), 616; https://doi.org/10.3390/jrfm15120616 - 18 Dec 2022
Viewed by 1355
Abstract
In finance, implied volatility is an important indicator that reflects the market situation immediately. Many practitioners estimate volatility by using iteration methods, such as the Newton–Raphson (NR) method. However, if numerous implied volatilities must be computed frequently, the iteration methods easily reach the [...] Read more.
In finance, implied volatility is an important indicator that reflects the market situation immediately. Many practitioners estimate volatility by using iteration methods, such as the Newton–Raphson (NR) method. However, if numerous implied volatilities must be computed frequently, the iteration methods easily reach the processing speed limit. Therefore, we emulate the NR method as a network by using PyTorch, a well-known deep learning package, and optimize the network further by using TensorRT, a package for optimizing deep learning models. Comparing the optimized emulation method with the benchmarks, implemented in two popular Python packages, we demonstrate that the emulation network is up to 1000 times faster than the benchmark functions. Full article
(This article belongs to the Special Issue Neural Networks for Financial Derivatives)
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