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Machine Learning Applications in Finance, 2nd Edition
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Machine Learning Applications in Finance, 2nd Edition

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 December 2024 | Viewed by 7151

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: probability and stochastic processes; Functional Data Analysis; financial time series
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

FinTech is a mainstream research topic in the field of finance. To promote emerging research focusing on finance technology, diverse machine learning and artificial intelligence techniques for large and complex finance data have been developed.

To present modern machine learning data analysis methods in economics and finance, a Special Issue of the Journal of Risk and Financial Management, will be devoted to “Machine Learning Applications in Finance, 2nd Edition”.

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 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

  • 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

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Related Special Issue

Published Papers (6 papers)

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Research

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18 pages, 786 KiB  
Article
Forecasting Orange Juice Futures: LSTM, ConvLSTM, and Traditional Models Across Trading Horizons
by Apostolos Ampountolas
J. Risk Financial Manag. 2024, 17(11), 475; https://doi.org/10.3390/jrfm17110475 - 22 Oct 2024
Viewed by 699
Abstract
This study evaluated the forecasting accuracy of various models over 5-day and 10-day trading horizons to predict the prices of orange juice futures (OJ = F). The analysis included traditional models like Autoregressive Integrated Moving Average (ARIMA) and advanced neural network models such [...] Read more.
This study evaluated the forecasting accuracy of various models over 5-day and 10-day trading horizons to predict the prices of orange juice futures (OJ = F). The analysis included traditional models like Autoregressive Integrated Moving Average (ARIMA) and advanced neural network models such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), and Convolutional Long Short-Term Memory (ConvLSTM), incorporating factors like the Commodities Index and the S&P500 Index. We employed loss function metrics and various tests to assess model performance. The results indicated that for the 5-day horizon, the LSTM and ConvLSTM consistently outperformed the other models. LSTM achieved the lowest error rates and demonstrated superior capability in capturing temporal dependencies, especially in single-factor and S&P500 Index predictions. ConvLSTM also performed strongly, effectively modeling spatial and temporal data patterns. In the 10-day horizon, similar trends were observed. LSTM and ConvLSTM models had significantly lower errors and better alignment with actual values. The BPNN model performed well when all factors were included, and the SVR model maintained consistent accuracy, particularly for single-factor predictions. The Diebold–Mariano (DM) test indicated significant differences in forecasting accuracy, favoring advanced neural network models. In addition, incorporating multiple influencing factors further improved predictive performance, enhancing investment outcomes and reducing risk. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)
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23 pages, 2242 KiB  
Article
Financial Distress Prediction in the Nordics: Early Warnings from Machine Learning Models
by Nils-Gunnar Birkeland Abrahamsen, Emil Nylén-Forthun, Mats Møller, Petter Eilif de Lange and Morten Risstad
J. Risk Financial Manag. 2024, 17(10), 432; https://doi.org/10.3390/jrfm17100432 - 27 Sep 2024
Viewed by 1129
Abstract
This paper proposes an explicable early warning machine learning model for predicting financial distress, which generalizes across listed Nordic corporations. We develop a novel dataset, covering the period from Q1 2001 to Q2 2022, in which we combine idiosyncratic quarterly financial statement data, [...] Read more.
This paper proposes an explicable early warning machine learning model for predicting financial distress, which generalizes across listed Nordic corporations. We develop a novel dataset, covering the period from Q1 2001 to Q2 2022, in which we combine idiosyncratic quarterly financial statement data, information from financial markets, and indicators of macroeconomic trends. The preferred LightGBM model, whose features are selected by applying explainable artificial intelligence, outperforms the benchmark models by a notable margin across evaluation metrics. We find that features related to liquidity, solvency, and size are highly important indicators of financial health and thus crucial variables for forecasting financial distress. Furthermore, we show that explicitly accounting for seasonality, in combination with entity, market, and macro information, improves model performance. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)
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21 pages, 843 KiB  
Article
Does ICT Investment Affect Market Share and Customer Acquisition Cost? A Comparative Analysis of Domestic and Foreign Banks Operating in India
by Gulam Goush Ansari and Rajorshi Sen Gupta
J. Risk Financial Manag. 2024, 17(9), 421; https://doi.org/10.3390/jrfm17090421 - 22 Sep 2024
Viewed by 1058
Abstract
Competitive banks aggressively invest in information and communication technologies (ICT) to enhance their market share and reduce Customer Acquisition Costs (CAC). This study examines the impact of cumulative stock of ICT investment on (a) deposit and loan market share and (b) CAC of [...] Read more.
Competitive banks aggressively invest in information and communication technologies (ICT) to enhance their market share and reduce Customer Acquisition Costs (CAC). This study examines the impact of cumulative stock of ICT investment on (a) deposit and loan market share and (b) CAC of banks operating in India. The analysis uses a longitudinal dataset of 84 domestic and 70 foreign banks from 2000 to 2020, employing a two-step system Generalized Method of Moment (GMM). It is found that ICT investment adversely affects the market share of domestic banks, indicating a need for these banks to strategically invest more in CAC. Conversely, foreign banks are able to increase their market share through ICT investment and reduced CAC, thereby demonstrating greater efficiency in utilizing ICT. The study underscores the strategic importance of cumulative stock of ICT investment for banks. Nonetheless, it is emphasized that ICT investment must be complemented with innovative marketing strategies to enhance customer experience, reduce CAC, and increase market share. Overall, while foreign banks are able to leverage ICT to boost efficiency, domestic banks must leverage ICT to implement targeted marketing strategies and strive to enhance their customer service. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)
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22 pages, 1177 KiB  
Article
Exploring Calendar Anomalies and Volatility Dynamics in Cryptocurrencies: A Comparative Analysis of Day-of-the-Week Effects before and during the COVID-19 Pandemic
by Sonal Sahu, Alejandro Fonseca Ramírez and Jong-Min Kim
J. Risk Financial Manag. 2024, 17(8), 351; https://doi.org/10.3390/jrfm17080351 - 12 Aug 2024
Viewed by 1238
Abstract
This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, [...] Read more.
This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the market’s continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)
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16 pages, 3729 KiB  
Article
Prediction of Currency Exchange Rate Based on Transformers
by Lu Zhao and Wei Qi Yan
J. Risk Financial Manag. 2024, 17(8), 332; https://doi.org/10.3390/jrfm17080332 - 1 Aug 2024
Viewed by 925
Abstract
The currency exchange rate is a crucial link between all countries related to economic and trade activities. With increasing volatility, exchange rate fluctuations have become frequent under the combined effects of global economic uncertainty and political risks. Consequently, accurate exchange rate prediction is [...] Read more.
The currency exchange rate is a crucial link between all countries related to economic and trade activities. With increasing volatility, exchange rate fluctuations have become frequent under the combined effects of global economic uncertainty and political risks. Consequently, accurate exchange rate prediction is significant in managing financial risks and economic instability. In recent years, the Transformer models have attracted attention in the field of time series analysis. Transformer models, such as Informer and TFT (Temporal Fusion Transformer), have also been extensively studied. In this paper, we evaluate the performance of the Transformer, Informer, and TFT models based on four exchange rate datasets: NZD/USD, NZD/CNY, NZD/GBP, and NZD/AUD. The results indicate that the TFT model has achieved the highest accuracy in exchange rate prediction, with an R2 value of up to 0.94 and the lowest RMSE and MAE errors. However, the Informer model offers faster training and convergence speeds than the TFT and Transformer, making it more efficient. Furthermore, our experiments on the TFT model demonstrate that integrating the VIX index can enhance the accuracy of exchange rate predictions. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)
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23 pages, 5586 KiB  
Systematic Review
Bibliometric Analysis of the Machine Learning Applications in Fraud Detection on Crowdfunding Platforms
by Luis F. Cardona, Jaime A. Guzmán-Luna and Jaime A. Restrepo-Carmona
J. Risk Financial Manag. 2024, 17(8), 352; https://doi.org/10.3390/jrfm17080352 - 13 Aug 2024
Viewed by 1511
Abstract
Crowdfunding platforms are important for startups, since they offer diverse financing options, market validation, and promotional opportunities through an investor community. These platforms provide detailed company information, aiding informed investment decisions within a regulated and secure environment. Machine learning (ML) techniques are important [...] Read more.
Crowdfunding platforms are important for startups, since they offer diverse financing options, market validation, and promotional opportunities through an investor community. These platforms provide detailed company information, aiding informed investment decisions within a regulated and secure environment. Machine learning (ML) techniques are important in analyzing large data sets, detecting anomalies and fraud, and enhancing decision-making and business strategies. A systematic review employed PRISMA guidelines, which studied how ML improves fraud detection on digital crowdfunding platforms. The analysis includes English-language studies from peer-reviewed journals published between 2018 and 2023 to analyze the pre- and post-COVID-19 pandemic. The findings indicate that ML techniques such as Random Forest, Support Vector Machine, and Artificial Neural Networks significantly enhance the predictive accuracy and utility of tax planning for startups considering equity crowdfunding. The United States, Germany, Canada, Italy, and Turkey do not present statistically significant differences at the 95% confidence level, standing out for their notable academic visibility. Florida Atlantic and Cornell Universities, Springer and John Wiley & Sons Ltd. publishing houses, and the Journal of Business Ethics and Management Science magazines present the highest citations without statistical differences at the 95% confidence level. Full article
(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)
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