Special Issue "Machine Learning Applications in Finance"

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 May 2020.

Special Issue Editor

Prof. Dr. Jong-Min Kim
E-Mail Website
Guest Editor
Statistics Discipline, Division of Science and Mathematics, University of Minnesota at Morris, Morris, MN 56267, USA
Interests: artificial intelligence; blockchain; big data; cryptocurrencies; cyber security; data analytics; data mining; deep learning; electronic data interchange (EDI); e-learning; Internet security; Internet of things; mobile applications; mobile learning; neural networks; fuzzy logic; expert systems; security; sentiment analysis; support vector machines; web services and performance
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

FinTech is a mainstream research topic in finance. To promote breakthrough research in finance technology, diverse machine learning and artificial intelligent techniques for large and complex finance data have been developed.

To present the modern machine learning data analysis methods in economics and finance, a Special Issue of the Journal of Risk and Financial Management, the Emerging Science Citation Index Expanded (Emerging SCI) Journal, will be devoted to “Machine Learning Applications in Finance”.

The guest editor for this Special Issue is Prof. Dr. Jong‐Min Kim.

Prof. Dr. Jong-Min Kim
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 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 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

  • Artificial intelligence
  • Blockchain
  • Big data
  • Cryptocurrencies
  • Cyber security
  • Data analytics
  • Data mining
  • Deep learning
  • Electronic data interchange (EDI)
  • e-Learning
  • Internet security
  • Internet of things
  • Mobile applications
  • Mobile learning
  • Neural networks
  • Fuzzy logic
  • Expert systems
  • Security
  • Sentiment analysis
  • Support vector machines
  • Web services and performance

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
A Gated Recurrent Unit Approach to Bitcoin Price Prediction
J. Risk Financial Manag. 2020, 13(2), 23; https://doi.org/10.3390/jrfm13020023 - 03 Feb 2020
Abstract
In today’s era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network [...] Read more.
In today’s era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. In this study, we investigate a framework with a set of advanced machine learning forecasting methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that the gated recurring unit (GRU) model with recurrent dropout performs better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance)
Show Figures

Figure 1

Open AccessArticle
What Coins Lead in the Cryptocurrency Market: Using Copula and Neural Networks Models
J. Risk Financial Manag. 2019, 12(3), 132; https://doi.org/10.3390/jrfm12030132 - 08 Aug 2019
Cited by 1
Abstract
Exploring dependence structures between financial time series has been important within a wide range of applications. The main aim of this paper is to examine dependence relationships among five well-known cryptocurrencies—Bitcoin, Ethereum, Litecoin, Ripple, and Stella—by a copula directional dependence (CDD). By employing [...] Read more.
Exploring dependence structures between financial time series has been important within a wide range of applications. The main aim of this paper is to examine dependence relationships among five well-known cryptocurrencies—Bitcoin, Ethereum, Litecoin, Ripple, and Stella—by a copula directional dependence (CDD). By employing a neural network autoregression model to avoid the serial dependence in each individual cryptocurrency, we generate residuals of the fitted models with time series of daily log-returns in percentage of the five cryptocurrencies and then we apply a Gaussian copula marginal beta regression model to the residuals to explore the CDD. The results show that the CDD from Bitcoin to Litecoin is highest among all ordered directional dependencies and the CDDs from Ethereum to the other four cryptocurrencies are relatively higher than the CDDs to Ethereum from those cryptocurrencies. This finding implies that the return shocks of Bitcoin have the most effect on Litecoin and the return shocks of Ethereum relatively influence the shocks on the other four cryptocurrencies instead of being affected by them. This allows investors to build the market-timing strategies by observing the directional flow of return shocks among cryptocurrencies. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance)
Show Figures

Figure 1

Back to TopTop