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 July 2021.

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 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
  • 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 (12 papers)

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Research

Open AccessArticle
Predicting Gold and Silver Price Direction Using Tree-Based Classifiers
J. Risk Financial Manag. 2021, 14(5), 198; https://doi.org/10.3390/jrfm14050198 - 29 Apr 2021
Viewed by 234
Abstract
Gold is often used by investors as a hedge against inflation or adverse economic times. Consequently, it is important for investors to have accurate forecasts of gold prices. This paper uses several machine learning tree-based classifiers (bagging, stochastic gradient boosting, random forests) to [...] Read more.
Gold is often used by investors as a hedge against inflation or adverse economic times. Consequently, it is important for investors to have accurate forecasts of gold prices. This paper uses several machine learning tree-based classifiers (bagging, stochastic gradient boosting, random forests) to predict the price direction of gold and silver exchange traded funds. Decision tree bagging, stochastic gradient boosting, and random forests predictions of gold and silver price direction are much more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging, stochastic gradient boosting, and random forests produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. Stochastic gradient boosting accuracy is a few percentage points less than that of random forests for forecast horizons over 10 days. For those looking to forecast the direction of gold and silver prices, tree bagging and random forests offer an attractive combination of accuracy and ease of estimation. For each of gold and silver, a portfolio based on the random forests price direction forecasts outperformed a buy and hold portfolio. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance)
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Open AccessArticle
Movie Title Keywords: A Text Mining and Exploratory Factor Analysis of Popular Movies in the United States and China
J. Risk Financial Manag. 2021, 14(2), 68; https://doi.org/10.3390/jrfm14020068 - 06 Feb 2021
Viewed by 865
Abstract
Unprecedented opportunities have been brought by advancements in machine learning in the prediction of the economic success of movies. The analysis of movie title keywords is one promising but rarely investigated direction of study. To address this gap, we performed a text mining [...] Read more.
Unprecedented opportunities have been brought by advancements in machine learning in the prediction of the economic success of movies. The analysis of movie title keywords is one promising but rarely investigated direction of study. To address this gap, we performed a text mining and exploratory factor analysis (EFA) of the relationships between movie titles and their corresponding movies’ levels of success. Specifically, intragroup and intergroup analyses of 217 top hit movies in the United States and 245 top hit movies in China showed that successful movies in these two major movie markets with outstanding total lifetime grosses featured titles with similar and different patterns of most frequently used words, revealing useful information about viewers’ preferences in these countries. The findings of this study will serve to better inform the movie industry in giving more economically promising names to their products from a machine-learning perspective and inspire further studies. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance)
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Open AccessArticle
Observation Time Effects in Reinforcement Learning on Contracts for Difference
J. Risk Financial Manag. 2021, 14(2), 54; https://doi.org/10.3390/jrfm14020054 - 27 Jan 2021
Viewed by 504
Abstract
In this paper, we present a study on Reinforcement Learning optimization models for automatic trading, in which we focus on the effects of varying the observation time. Our Reinforcement Learning agents feature a Convolutional Neural Network (CNN) together with Long Short-Term Memory (LSTM) [...] Read more.
In this paper, we present a study on Reinforcement Learning optimization models for automatic trading, in which we focus on the effects of varying the observation time. Our Reinforcement Learning agents feature a Convolutional Neural Network (CNN) together with Long Short-Term Memory (LSTM) and act on the basis of different observation time spans. Each agent tries to maximize trading profit by buying or selling one of a number of contracts in a simulated market environment for Contracts for Difference (CfD), considering correlations between individual assets by architecture. To decide which action to take on a specific contract, an agent develops a policy which relies on an observation of the whole market for a certain period of time. We investigate whether or not there exists an optimal observation sequence length, and conclude that such a value depends on market dynamics. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance)
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Open AccessArticle
Know Your Clients’ Behaviours: A Cluster Analysis of Financial Transactions
J. Risk Financial Manag. 2021, 14(2), 50; https://doi.org/10.3390/jrfm14020050 - 25 Jan 2021
Viewed by 733
Abstract
In Canada, financial advisors and dealers are required by provincial securities commissions and self-regulatory organizations—charged with direct regulation over investment dealers and mutual fund dealers—to respectively collect and maintain know your client (KYC) information, such as their age or risk tolerance, for investor [...] Read more.
In Canada, financial advisors and dealers are required by provincial securities commissions and self-regulatory organizations—charged with direct regulation over investment dealers and mutual fund dealers—to respectively collect and maintain know your client (KYC) information, such as their age or risk tolerance, for investor accounts. With this information, investors, under their advisor’s guidance, make decisions on their investments that are presumed to be beneficial to their investment goals. Our unique dataset is provided by a financial investment dealer with over 50,000 accounts for over 23,000 clients covering the period from January 1st to August 12th 2019. We use a modified behavioral finance recency, frequency, monetary model for engineering features that quantify investor behaviours, and unsupervised machine learning clustering algorithms to find groups of investors that behave similarly. We show that the KYC information—such as gender, residence region, and marital status—does not explain client behaviours, whereas eight variables for trade and transaction frequency and volume are most informative. Hence, our results should encourage financial regulators and advisors to use more advanced metrics to better understand and predict investor behaviours. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance)
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Open AccessArticle
A Random Forests Approach to Predicting Clean Energy Stock Prices
J. Risk Financial Manag. 2021, 14(2), 48; https://doi.org/10.3390/jrfm14020048 - 24 Jan 2021
Cited by 2 | Viewed by 788
Abstract
Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest towards investing in companies that specialize in clean energy. Well informed investors need reliable methods for predicting the stock prices of clean energy companies. [...] Read more.
Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest towards investing in companies that specialize in clean energy. Well informed investors need reliable methods for predicting the stock prices of clean energy companies. While the existing literature on forecasting stock prices shows how difficult it is to predict stock prices, there is evidence that predicting stock price direction is more successful than predicting actual stock prices. This paper uses the machine learning method of random forests to predict the stock price direction of clean energy exchange traded funds. Some well-known technical indicators are used as features. Decision tree bagging and random forests predictions of stock price direction are more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging and random forests methods produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. Tree bagging and random forests are easy to understand and estimate and are useful methods for forecasting the stock price direction of clean energy stocks. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance)
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Open AccessArticle
The Changing Dynamics of Board Independence: A Copula Based Quantile Regression Approach
J. Risk Financial Manag. 2020, 13(11), 254; https://doi.org/10.3390/jrfm13110254 - 28 Oct 2020
Viewed by 474
Abstract
This paper examines the effect of board characteristics, especially board independence, on firm performance from a dynamic perspective through copula-based quantile regression approaches, which allow us to focus on changes at different points in the distribution of board characteristics. We find that the [...] Read more.
This paper examines the effect of board characteristics, especially board independence, on firm performance from a dynamic perspective through copula-based quantile regression approaches, which allow us to focus on changes at different points in the distribution of board characteristics. We find that the effect of board independence on Tobin’s Q, a proxy of firm value, is negatively associated with firm value, using ordinary least squares (OLS) regression. This negative effect using the conditional mean of the firm value does not hold across the conditional quantiles of the distribution of Tobin’s Q, and this finding is still held under both the linear and the nonlinear quantile regressions. We even lessen the assumption of distributions of multivariate board variables by employing parametric copula-based quantile regressions as well as nonparametric ones. The results support our findings. Our results suggest that estimating the quantile effect of board variables on firm value can provide more meaningful insight than just examining the conditional mean effect. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance)
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Open AccessArticle
Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans
J. Risk Financial Manag. 2020, 13(8), 180; https://doi.org/10.3390/jrfm13080180 - 13 Aug 2020
Cited by 1 | Viewed by 1507
Abstract
Airtime lending default rates are typically lower than those experienced by banks and microfinance institutions (MFIs) but are likely to grow as the service is offered more widely. In this paper, credit scoring techniques are reviewed, and that knowledge is built upon to [...] Read more.
Airtime lending default rates are typically lower than those experienced by banks and microfinance institutions (MFIs) but are likely to grow as the service is offered more widely. In this paper, credit scoring techniques are reviewed, and that knowledge is built upon to create an appropriate machine learning model for airtime lending. Over three million loans belonging to more than 41 thousand customers with a repayment period of three months are analysed. Logistic Regression, Decision Trees and Random Forest are evaluated for their ability to classify defaulters using several cross-validation approaches and the latter model performed best. When the default rate is below 2%, it is better to offer everyone a loan. For higher default rates, the model substantially enhances profitability. The model quadruples the tolerable level of default rate for breaking even from 8% to 32%. Nonlinear classification models offer considerable potential for credit scoring, coping with higher levels of default and therefore allowing for larger volumes of customers. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance)
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Open AccessCommunication
Cryptocurrency Trading Using Machine Learning
J. Risk Financial Manag. 2020, 13(8), 178; https://doi.org/10.3390/jrfm13080178 - 10 Aug 2020
Cited by 1 | Viewed by 1859
Abstract
We present a model for active trading based on reinforcement machine learning and apply this to five major cryptocurrencies in circulation. In relation to a buy-and-hold approach, we demonstrate how this model yields enhanced risk-adjusted returns and serves to reduce downside risk. These [...] Read more.
We present a model for active trading based on reinforcement machine learning and apply this to five major cryptocurrencies in circulation. In relation to a buy-and-hold approach, we demonstrate how this model yields enhanced risk-adjusted returns and serves to reduce downside risk. These findings hold when accounting for actual transaction costs. We conclude that real-world portfolio management application of the model is viable, yet, performance can vary based on how it is calibrated in test samples. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance)
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Open AccessArticle
A Machine Learning Integrated Portfolio Rebalance Framework with Risk-Aversion Adjustment
J. Risk Financial Manag. 2020, 13(7), 155; https://doi.org/10.3390/jrfm13070155 - 16 Jul 2020
Cited by 2 | Viewed by 1188
Abstract
We propose a portfolio rebalance framework that integrates machine learning models into the mean-risk portfolios in multi-period settings with risk-aversion adjustment. In each period, the risk-aversion coefficient is adjusted automatically according to market trend movements predicted by machine learning models. We employ Gini’s [...] Read more.
We propose a portfolio rebalance framework that integrates machine learning models into the mean-risk portfolios in multi-period settings with risk-aversion adjustment. In each period, the risk-aversion coefficient is adjusted automatically according to market trend movements predicted by machine learning models. We employ Gini’s Mean Difference (GMD) to specify the risk of a portfolio and use a set of technical indicators generated from a market index (e.g., S&P 500 index) to feed the machine learning models to predict market movements. Using a rolling-horizon approach, we conduct a series of computational tests with real financial data to evaluate the performance of the machine learning integrated portfolio rebalance framework. The empirical results show that the XGBoost model provides the best prediction of market movement, while the proposed portfolio rebalance strategy generates portfolios with superior out-of-sample performances in terms of average returns, time-series cumulative returns, and annualized returns compared to the benchmarks. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance)
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Open AccessArticle
Finding Nemo: Predicting Movie Performances by Machine Learning Methods
J. Risk Financial Manag. 2020, 13(5), 93; https://doi.org/10.3390/jrfm13050093 - 09 May 2020
Viewed by 1161
Abstract
Analyzing the success of movies has always been a popular research topic in the film industry. Artificial intelligence and machine learning methods in the movie industry have been applied to modeling the financial success of the movie industry. The new contribution of this [...] Read more.
Analyzing the success of movies has always been a popular research topic in the film industry. Artificial intelligence and machine learning methods in the movie industry have been applied to modeling the financial success of the movie industry. The new contribution of this research combined Bayesian variable selection and machine learning methods for forecasting the return on investment (ROI). We also attempt to compare machine learning methods including the quantile regression model with movie performance data in terms of in-sample and out of sample forecasting. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance)
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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
Cited by 10 | Viewed by 2059
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)
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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 5 | Viewed by 1580
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)
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