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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 August 2025 | Viewed by 21076

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Prof. Dr. Jong-Min Kim

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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

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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

Machine Learning Applications in Finance in Journal of Risk and Financial Management (19 articles)

Published Papers (10 papers)

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31 pages, 1781 KiB

Open AccessArticle

A Majority Voting Mechanism-Based Ensemble Learning Approach for Financial Distress Prediction in Indian Automobile Industry

by Manoranjitham Muniappan and Nithya Darisini Paruvachi Subramanian

J. Risk Financial Manag. 2025, 18(4), 197; https://doi.org/10.3390/jrfm18040197 - 4 Apr 2025

Viewed by 400

Abstract

Financial distress poses a significant risk to companies worldwide, irrespective of their nature or size. It refers to a situation where a company is unable to meet its financial obligations on time, potentially leading to bankruptcy and liquidation. Predicting distress has become a crucial application in business classification, employing both Statistical approaches and Artificial Intelligence techniques. Researchers often compare the prediction performance of different techniques on specific datasets, but no consistent results exist to establish one model as superior to others. Each technique has its own advantages and drawbacks, depending on the dataset. Recent studies suggest that combining multiple classifiers can significantly enhance prediction performance. However, such ensemble methods inherit both the strengths and weaknesses of the constituent classifiers. This study focuses on analyzing and comparing the financial status of Indian automobile manufacturing companies. Data from a sample of 100 automobile companies between 2013 and 2019 were used. A novel Firm-Feature-Wise three-step missing value imputation algorithm was implemented to handle missing financial data effectively. This study evaluates the performance of 11 individual baseline classifiers and all the 11 baseline algorithm’s combinations by using ensemble method. A manual ranking-based approach was used to evaluate the performance of 2047 models. The results of each combination are inputted to hard majority voting mechanism algorithm for predicting a company’s financial distress. Eleven baseline models are trained and assessed, with Gradient Boosting exhibiting the highest accuracy. Hyperparameter tuning is then applied to enhance individual baseline classifier performance. The majority voting mechanism with hyperparameter-tuned baseline classifiers achieve high accuracy. The robustness of the model is tested through k-fold Cross-Validation, demonstrating its generalizability. After fine-tuning the hyperparameters, the experimental investigation yielded an accuracy of 99.52%, surpassing the performance of previous studies. Furthermore, it results in the absence of Type-I errors. Full article

(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)

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24 pages, 3801 KiB

Open AccessArticle

Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading

by Akash Deep, Abootaleb Shirvani, Chris Monico, Svetlozar Rachev and Frank Fabozzi

J. Risk Financial Manag. 2025, 18(3), 142; https://doi.org/10.3390/jrfm18030142 - 9 Mar 2025

Viewed by 1694

Abstract

Because of the theoretical challenges posed by the Efficient Market Hypothesis with respect to technical analysis, the effectiveness of technical indicators in high-frequency trading remains inadequately explored, particularly at the minute-level frequency, where the effects of the microstructure of the market dominate. This study evaluates the integration of traditional technical indicators with Random Forest regression models using minute-level SPY data, analyzing 13 distinct model configurations. Our empirical results reveal a stark contrast between in-sample and out-of-sample performance, with

R^{2}

values deteriorating from 0.749–0.812 during training to negative values in testing. A feature importance analysis demonstrates that primary price-based features dominate the predictions made by the model, accounting for over 60% of the importance, while established technical indicators, such as RSI and Bollinger Bands, account for only 14–15%. Although the indicator-enhanced models achieved superior risk-adjusted metrics, with Rachev ratios between 0.919 and 0.961, they consistently underperformed a simple buy-and-hold strategy, generating returns ranging from −2.4% to −3.9%. These findings challenge conventional assumptions about the usefulness of technical indicators in algorithmic trading, suggesting that in high-frequency contexts, they may be more relevant to risk management rather than to predicting returns. For practitioners and researchers, our findings indicate that successful high-frequency trading strategies should focus on adaptive feature selection and regime-specific modeling rather than relying on traditional technical indicators, as well as indicating the critical importance of robust out-of-sample testing in the development of a model. Full article

(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)

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16 pages, 2404 KiB

Open AccessArticle

Using Machine Learning to Understand the Dynamics Between the Stock Market and US Presidential Election Outcomes

by Avi Thaker, Daniel Sonner and Leo H. Chan

J. Risk Financial Manag. 2025, 18(3), 109; https://doi.org/10.3390/jrfm18030109 - 21 Feb 2025

Viewed by 534

Abstract

In this paper, we applied an explainable AI model (SHAP feature importance measures) to study the dynamic relationship between stock market returns and the US presidential election outcomes. More specifically, we wanted to study how the market would react the day after the election. AI models have been criticized as black-box models and lack the clarity needed for decision-making by different stakeholders. The explainable AI model we utilized in this model provides more clarity for the outcomes of the model. Using features commonly used by previous studies related to this topic, we find that the previous market direction leading up to the election and the incumbency information combined with the political affiliation are larger drivers for a 1-day post-election market return than sentiment and which party wins the election. Full article

(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)

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22 pages, 7903 KiB

Open AccessArticle

Forecasting Forex Market Volatility Using Deep Learning Models and Complexity Measures

by Pavlos I. Zitis, Stelios M. Potirakis and Alex Alexandridis

J. Risk Financial Manag. 2024, 17(12), 557; https://doi.org/10.3390/jrfm17120557 - 13 Dec 2024

Cited by 1 | Viewed by 3117

Abstract

In this article, we examine whether incorporating complexity measures as features in deep learning (DL) algorithms enhances their accuracy in predicting forex market volatility. Our approach involved the gradual integration of complexity measures alongside traditional features to determine whether their inclusion would provide additional information that improved the model’s predictive accuracy. For our analyses, we employed recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) as DL model architectures, while using the Hurst exponent and fuzzy entropy as complexity measures. All analyses were conducted on intraday data from four highly liquid currency pairs, with volatility estimated using the Range-Based estimator. Our findings indicated that the inclusion of complexity measures as features significantly enhanced the accuracy of DL models in predicting volatility. In achieving this, we contribute to a relatively unexplored area of research, as this is the first instance of such an approach being applied to the prediction of forex market volatility. Additionally, we conducted a comparative analysis of the three models’ performance, revealing that the LSTM and GRU models consistently demonstrated a superior accuracy. Finally, our findings also have practical implications, as they may assist risk managers and policymakers in forecasting volatility in the forex market. Full article

(This article belongs to the Special Issue Machine Learning Applications in Finance, 2nd Edition)

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18 pages, 786 KiB

Open AccessArticle

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

Cited by 3 | Viewed by 1863

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 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

Open AccessArticle

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 2189

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, 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

Open AccessArticle

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 1755

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 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

Open AccessArticle

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 3525

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, 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

Open AccessArticle

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 2133

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 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

Open AccessSystematic 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 2763

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 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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