Journal of Risk and Financial Management

14 pages, 494 KiB

Open AccessArticle

Evaluating the Financial Performance of Colombian Companies: A Data Envelopment Analysis Without Explicit Inputs and Technique for Order Preference by Similarity to the Ideal Solution Approach

by Adel Mendoza-Mendoza, Daniel Mendoza Casseres and Enrique De La Hoz-Domínguez

J. Risk Financial Manag. 2024, 17(12), 568; https://doi.org/10.3390/jrfm17120568 - 18 Dec 2024

Abstract

The evaluation and ranking of companies in any sector are generally based on a single measure of financial success, so the results obtained vary according to the classification criteria used. This study applies a multi-criteria approach to develop a classification of the largest [...] Read more.

The evaluation and ranking of companies in any sector are generally based on a single measure of financial success, so the results obtained vary according to the classification criteria used. This study applies a multi-criteria approach to develop a classification of the largest companies in Colombia based on their financial results for the period 2022–2023. An analysis of 100 companies was conducted, utilizing four critical criteria: operating income, net profit, total assets, and equity. The evaluation followed a two-stage process. In the first stage, the weights or importance of each selected criterion were objectively established using data envelopment analysis without explicit inputs (DEA-WEIs). This approach reveals that operating income (35.23%) and total assets (28.57%) are the most influential criteria, while net profit is the least influential (13.51%). In the second stage, companies are ranked using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), with the results highlighting Refinería de Cartagena, Empresas Públicas de Medellín, and Terpel S.A. as the top-performing companies. The classification shows clear differentiation, forming two statistically distinct groups validated through discriminant analysis, achieving a 100% correct classification rate. These findings provide actionable insights for benchmarking and improving financial performance in the corporate sector. Full article

(This article belongs to the Section Mathematics and Finance)

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15 pages, 627 KiB

Open AccessArticle

Analysis of Financial Contagion and Prediction of Dynamic Correlations During the COVID-19 Pandemic: A Combined DCC-GARCH and Deep Learning Approach

by Victor Chung, Jenny Espinoza and Alan Mansilla

J. Risk Financial Manag. 2024, 17(12), 567; https://doi.org/10.3390/jrfm17120567 - 18 Dec 2024

Abstract

This study aims to combine the use of dynamic conditional correlation multiple generalized autoregressive conditional heteroskedasticity (DCC-GARCH) models and deep learning techniques in analyzing the dynamic correlation between stock markets. First, we examine the contagion effect of the high-risk financial crisis during COVID-19 [...] Read more.

This study aims to combine the use of dynamic conditional correlation multiple generalized autoregressive conditional heteroskedasticity (DCC-GARCH) models and deep learning techniques in analyzing the dynamic correlation between stock markets. First, we examine the contagion effect of the high-risk financial crisis during COVID-19 in the United States on the Latin American stock market using a dynamic conditional correlation approach. The study covers the period from 2014 to 2020, divided into the pre-COVID-19 period (January 2014–February 2020) and the COVID-19 period (March 2020–November 2020), to examine the sudden change in average conditional correlation from one period to the next and identify the contagion effect. The contagion test showed significant contagion between the S&P 500 and Latin American indices, except for Argentina’s MERVAL. Additionally, we applied deep learning models, specifically LSTM, to predict market dynamics and changes in volatility as an early warning system. The results indicate that incorporating LSTM improved the accuracy of predicting dynamic correlations and provided early risk signals during the crisis. This suggests that combining DCC-GARCH with deep learning techniques is a powerful tool for predicting and managing financial risk in highly uncertain markets. Full article

(This article belongs to the Section Financial Technology and Innovation)

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17 pages, 2489 KiB

Open AccessReview

Higher Education Loan Schemes Across the Globe: A Systematic Review on the Utility Derived and Burden Associated with Educational Debt

by Daniel Frank, Rakshith Bhandary and Sudhir K. Prabhu

J. Risk Financial Manag. 2024, 17(12), 566; https://doi.org/10.3390/jrfm17120566 - 18 Dec 2024

Abstract

Education is considered an investment in human capital that is gained at the cost of knowledge acquisition. This cost is borne by the beneficiary along with subsidy provided by the government, if any, that is mainly collected through tax revenues. This article aims [...] Read more.

Education is considered an investment in human capital that is gained at the cost of knowledge acquisition. This cost is borne by the beneficiary along with subsidy provided by the government, if any, that is mainly collected through tax revenues. This article aims to systematically review the utility derived and the burden experienced with educational debt borrowers across the globe as per the three types of educational loan schemes present across the globe. This study follows the PRISMA guidelines for review selection, and 47 articles published between 1994 and 2024 were included for the final review. The study results reveal that education improves the quality of life; an educational debt servicing to income ratio above 8% is considered as a financial burden. Also, the results reveal that material benefits are high after education along with an increase in the psychological burden because of repayment concerns. This study highlights the need to move towards designing a flexible repayment system in the education loan scheme based on the income contingent schemes adopted in many countries. Income contingent schemes reduce the repayment burden of the borrowers but the return to the lender is limited to the income of the borrower, and mortgage-based schemes are associated with high repayment burden. Therefore, a dynamic scheme will fix the problems associated with the repayment burden by creating a dynamic link between the benefits received and the contributions made by the borrower. Full article

(This article belongs to the Special Issue Recent Development and Challenges in the Banking and Financial Sector: Toward a More Sustainable Financial System)

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18 pages, 897 KiB

Open AccessArticle

A Non-Linear Approach to Current Account Sustainability—The Cases of Germany, China, and the USA

by Thanos Poulakis and Dimitrios Kyrkilis

J. Risk Financial Manag. 2024, 17(12), 565; https://doi.org/10.3390/jrfm17120565 - 17 Dec 2024

Abstract

This paper examines the sustainability of the current account balances for three leading economies that play significant roles in global financial and geopolitical developments: Using the concept of sustainability as the ability of an economy to meet its long-term intertemporal budget constraint, the [...] Read more.

This paper examines the sustainability of the current account balances for three leading economies that play significant roles in global financial and geopolitical developments: Using the concept of sustainability as the ability of an economy to meet its long-term intertemporal budget constraint, the analysis evaluates whether the current accounts of these three economies can maintain this condition without requiring substantial policy interventions or significant changes in private sector behavior. The stationarity of the current account is considered a sufficient condition for testing the sustainability of the current account balance. However, it is argued that the common assumption of a linear process for the current account under the alternative hypothesis of stationarity may not accurately reflect reality, as the current account may exhibit non-linear behavior. Therefore, both linear and non-linear unit root tests are employed to investigate current account sustainability. The results of the unit root tests indicate that the current account balances of Germany, China, and the United States are unsustainable. These findings have significant implications for the economic stability and policy direction of these major economies. Full article

(This article belongs to the Special Issue Contemporary Trends in International Finance: Navigating Global Dynamics)

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13 pages, 252 KiB

Open AccessArticle

Dynamics of Dividend Payout in Korean Corporations: A Comprehensive Panel Analysis Across Economic Cycles

by SungSup Brian Choi and Kudzai Sauka

J. Risk Financial Manag. 2024, 17(12), 564; https://doi.org/10.3390/jrfm17120564 - 17 Dec 2024

Abstract

This research conducts a meticulous examination of the determinants influencing dividend payout dynamics among firms listed on the Korean Stock Exchange (KSE) from 1995 to 2021, a period characterized by profound economic fluctuations. By leveraging a dynamic panel data model and the Generalized [...] Read more.

This research conducts a meticulous examination of the determinants influencing dividend payout dynamics among firms listed on the Korean Stock Exchange (KSE) from 1995 to 2021, a period characterized by profound economic fluctuations. By leveraging a dynamic panel data model and the Generalized Method of Moments (GMM) for estimation, the study addresses endogeneity concerns while exploring the effects of firm-specific and macroeconomic variables on dividend yields. The investigation delineates three distinct economic phases: normal conditions, financial crises, and the aggregate study period, facilitating a granular understanding of firms’ dividend payout adaptability under varying economic landscapes. Empirical findings underscore the persistence of dividend payments, revealing a variable adjustment speed toward target dividend yields contingent upon the economic context, with an expedited adjustment observed during crises. Crucially, firm profitability emerges as a consistent determinant of dividend yields across all examined periods, whereas the influence of macroeconomic variables is notably more pronounced during periods of economic normalcy. This research elucidates the complex interplay between internal corporate strategies and external economic pressures in shaping dividend policies, thereby enriching the discourse on dividend payout behavior in the context of Korea’s economic evolution from an emerging to a developed market. Full article

(This article belongs to the Special Issue Advances in Macroeconomics and Financial Markets)

19 pages, 308 KiB

Open AccessArticle

Does Profitability Moderate the Relationship Between the Leverage and Dividend Policy of Manufacturing Firms in Nigeria and South Africa?

by Ovbe Simon Akpadaka, Musa Adeiza Farouk, Dagwom Yohanna Dang and Musa Inuwa Fodio

J. Risk Financial Manag. 2024, 17(12), 563; https://doi.org/10.3390/jrfm17120563 - 16 Dec 2024

Abstract

This study examines the moderating role of profitability in the relationship between leverage and dividend policy in listed manufacturing firms in Nigeria and South Africa. Using a sample of 915 firm-year observations from 2013 to 2022, the analysis employs panel Tobit regression to [...] Read more.

This study examines the moderating role of profitability in the relationship between leverage and dividend policy in listed manufacturing firms in Nigeria and South Africa. Using a sample of 915 firm-year observations from 2013 to 2022, the analysis employs panel Tobit regression to manage the censored nature of dividend data, with logistic regression applied as a robustness check. The findings reveal a negative association between leverage and dividend payout ratio for Nigerian firms, while this association is less pronounced and statistically insignificant in South Africa, reflecting a more flexible financial environment. Profitability strengthens the leverage–dividend policy relationship in Nigeria, enabling firms to maintain dividends despite high leverage; however, this moderating effect is weaker in South Africa. These results underscore the importance of context-specific financial strategies, recommending that Nigerian policymakers improve access to affordable credit, while South African policymakers focus on sustaining market stability. This study advances the understanding of dividend policy in emerging markets by clarifying how leverage and profitability interact to shape dividend practices. Full article

(This article belongs to the Special Issue Featured Papers in Corporate Finance and Governance)

15 pages, 512 KiB

Open AccessArticle

Assessment of Factors Affecting Tax Revenues: The Case of the Simplified Taxation System in the Russian Federation

by Kristina Alekseyevna Zakharova, Danil Anatolyevich Muravyev, Egine Araratovna Karagulian, Natalia Alekseyevna Baburina and Ekaterina Vladimirovna Degtyaryova

J. Risk Financial Manag. 2024, 17(12), 562; https://doi.org/10.3390/jrfm17120562 - 16 Dec 2024

Abstract

The simplified tax system is the most common special tax regime in the Russian Federation in terms of the number of taxpayers. Tax revenues from the simplified tax system account for 6% of the structure of tax revenues of the consolidated budgets of [...] Read more.

The simplified tax system is the most common special tax regime in the Russian Federation in terms of the number of taxpayers. Tax revenues from the simplified tax system account for 6% of the structure of tax revenues of the consolidated budgets of the constituent entities of the Russian Federation and more than 93% of the structure of tax revenues from special tax regimes. The purpose of this study is to identify and assess the factors influencing tax revenues from the tax levied in connection with applying the simplified system of taxation (taxable object—income reduced by the amount of expenses). The objective of this study is to determine a set of factors used by economists to model the level of tax revenues and to conduct a corresponding econometric analysis of the influence of the selected factors on the dependent variable to identify characteristics of the simplified taxation system functioning in the Russian Federation. The object of this study is the per capita tax revenue from the tax levied in connection with applying the simplified system of taxation (the object of taxation is income reduced by expenses) in the Russian Federation. The subject of the research is a set of economic relations, which arise because of tax-legal relations between tax authorities and taxpayers in relation to the calculation of the tax levied in connection with the application of the simplified taxation system. This study’s hypothesis is that the amount of tax revenues is influenced by factors characterizing the economic situation and development of small and medium businesses in the constituent territories of the Russian Federation. This study was conducted in 83 constituent territories of the Russian Federation in 2020–2022. The research methods are statistical analysis and econometric modeling on panel data. During this study, six econometric models were constructed. Based on the results of specification tests, the least squares dummy variables model was selected. The results of the modeling show that the tax rate, the number of taxpayers, and the real average per capita monetary income of the population have a statistically significant impact on the per capita tax revenue under the simplified tax system (the object of taxation is income reduced by the number of expenses). As a result, the focus of economic policy at both macro and meso levels should be on the support of small and medium-sized enterprises in the early stages of their life cycle, as well as on the increase of the purchasing power of the population. Based on the results obtained, it is possible to forecast the revenue side of the budgets of the constituent entities of the Russian Federation. Full article

(This article belongs to the Special Issue Financial Econometrics with Panel Data)

12 pages, 613 KiB

Open AccessArticle

Sustainability, Risk Management, and Innovation: Enhancing Performance in Indonesian Social Enterprises

by Azhar Maksum, Munawarah, Yuni Lestari Br Sitepu and Fauziah Kumalasari

J. Risk Financial Manag. 2024, 17(12), 561; https://doi.org/10.3390/jrfm17120561 - 16 Dec 2024

Abstract

This study investigates the integration of sustainability practices and risk management in Indonesian social enterprises, emphasizing the role of innovation as a mediator and operational type as a moderator. Social enterprises face unique challenges in balancing economic sustainability with social impact, especially in [...] Read more.

This study investigates the integration of sustainability practices and risk management in Indonesian social enterprises, emphasizing the role of innovation as a mediator and operational type as a moderator. Social enterprises face unique challenges in balancing economic sustainability with social impact, especially in emerging markets like Indonesia. A structured survey was conducted with 118 social enterprises to assess their sustainable practices, risk management procedures, innovation scores, and operational models (permanent vs. project-based). Using Structural Equation Modeling (SEM) and Partial Least Squares (PLS) analysis, the results show that sustainability practices positively influence innovation, while both innovation and risk management significantly improve sustainable performance. Additionally, innovation mediates the relationship between sustainability practices, risk management, and performance. The operational type moderates the link between risk management and sustainable performance but does not influence the connection between sustainability practices and performance. These findings suggest that innovation is crucial for improving the sustainability of social enterprises and that risk management strategies should be tailored to the operational model. Social enterprises in Indonesia should prioritize innovative approaches and effective risk management to enhance their long-term sustainability and social impact. Full article

(This article belongs to the Special Issue Business, Finance and Economic Development—On Globalisation, Entrepreneurship and Emerging Markets)

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25 pages, 458 KiB

Open AccessArticle

Managerial Social Capital and Dividends: Evidence from the UK

by Omar Al-Bataineh, Abdullah Iqbal and Timothy King

J. Risk Financial Manag. 2024, 17(12), 560; https://doi.org/10.3390/jrfm17120560 - 15 Dec 2024

Abstract

We examine the relationship between managerial social capital (MSC) and firms’ dividend policies. For an unbalanced panel of publicly listed UK FTSE 350 firms from 2006 to 2017, we find that MSC has a negative impact on a firm’s dividend policy. Firms pay [...] Read more.

We examine the relationship between managerial social capital (MSC) and firms’ dividend policies. For an unbalanced panel of publicly listed UK FTSE 350 firms from 2006 to 2017, we find that MSC has a negative impact on a firm’s dividend policy. Firms pay lower dividends when a higher proportion of well-connected directors join corporate boards. Our main result is consistent with the notion that a high degree of social capital leads to better monitoring and control; therefore, social capital works under the substitution effect between governance quality and dividend payouts. In further analyses, we explore potential differences in this relationship between financial and non-financial firms and show that the association between MSC and dividend policy is weaker in financial firms than in non-financial firms. Taken together, our findings infer that investors should consider the social capital status of firms when they make investment decisions. Our results proved to be robust when subjected to a battery of tests, including alternative model specifications and definitions of MSC. Full article

(This article belongs to the Special Issue Corporate Finance: Financial Management of the Firm)

16 pages, 375 KiB

Open AccessArticle

The Impact of Shipping Connectivity on Environmental Quality, Financial Development, and Economic Growth in Regional Comprehensive Economic Partnership Countries

by Xhelil Bekteshi, Sevdie Alshiqi, Bartosz Jóźwik, Fatma Gul Altin, Mesut Dogan and Tatyana Petrossyants

J. Risk Financial Manag. 2024, 17(12), 559; https://doi.org/10.3390/jrfm17120559 - 15 Dec 2024

Abstract

This study investigates the relationship between shipping connectivity, environmental quality, financial development, and economic growth among 14 countries in the Regional Comprehensive Economic Partnership (RCEP) from 2006 to 2019. Using panel-corrected standard error, Dynamic Seemingly Unrelated Regression, and Driscoll–Kraay estimation methods, the analysis [...] Read more.

This study investigates the relationship between shipping connectivity, environmental quality, financial development, and economic growth among 14 countries in the Regional Comprehensive Economic Partnership (RCEP) from 2006 to 2019. Using panel-corrected standard error, Dynamic Seemingly Unrelated Regression, and Driscoll–Kraay estimation methods, the analysis reveals that shipping connectivity significantly contributes to financial development and economic growth, while also exerting a negative impact on environmental quality. These findings suggest that the maritime sector can have significant impacts not only on economic growth and financial development but also on environmental sustainability. In countries where maritime shipping has increased, particularly with the growth of trade, positive outcomes are observed in terms of financial development and economic growth, while negative impacts on environmental quality are also evident. This study provides insights for policymakers to develop strategies that maximize economic benefits while reducing environmental harm in order to achieve sustainable development in the maritime sector. Full article

(This article belongs to the Special Issue Macroeconomic Policies and Economic Growth)

20 pages, 1490 KiB

Open AccessArticle

The Predictive Grey Forecasting Approach for Measuring Tax Collection

by Pitresh Kaushik, Mohsen Brahmi, Shubham Kakran and Pooja Kansra

J. Risk Financial Manag. 2024, 17(12), 558; https://doi.org/10.3390/jrfm17120558 - 13 Dec 2024

Abstract

Taxation serves as a vital lifeline for government revenue, directly contributing to national development and the welfare of its citizens. Ensuring the efficiency and effectiveness of the tax collection process is essential for maintaining a sustainable economic framework. This study investigates (a) trends [...] Read more.

Taxation serves as a vital lifeline for government revenue, directly contributing to national development and the welfare of its citizens. Ensuring the efficiency and effectiveness of the tax collection process is essential for maintaining a sustainable economic framework. This study investigates (a) trends and patterns of direct tax collection, (b) the cost of tax collection, (c) the proportion of direct tax in total tax collection, and (d) the tax-to-GDP ratio in India. By utilizing a novel grey forecasting model (GM (1,1)), this study attempted to predict the future trends of India’s direct tax collections, through which it aims to provide a concurrent and accurate future outlook on tax revenue, ensuring resources are optimally allocated for the country’s growth. Results revealed that direct tax collection has consistently increased in the past two decades, and the proportion of direct tax in total tax has also improved significantly. On the contrary, the cost of tax collection has decreased regularly, indicating the efficiency of tax collection. Forecasting shows that the collection from direct tax is expected to reach INR 30.67 trillion in 2029–30, constituting around 54.41% of the total tax, leaving behind collections from indirect tax at a total of INR 25.70 trillion. Such findings offer insights that could enhance revenue management strategies with policy decisions relevant to economists, government, and other stakeholders to understand trends and the efficiency of direct tax collection in India. Full article

(This article belongs to the Special Issue Sustainable Tax and Accounting Reporting in Building a New Tax Culture)

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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

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 [...] Read more.

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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14 pages, 1123 KiB

Open AccessArticle

Incorporating Artificial Intelligence into Finance: A Bibliometric Analysis

by Antonio Carlos Alcázar-Blanco, José Francisco Rangel-Preciado and Fiama Portillo-Santos

J. Risk Financial Manag. 2024, 17(12), 556; https://doi.org/10.3390/jrfm17120556 - 11 Dec 2024

Abstract

The aim of this study is to carry out an analysis of the intellectual structure of the introduction of AI into finance, in the period from 1995 to 2023, using SciMAT v.1.1.04 software. The results indicate how research on the incorporation of AI [...] Read more.

The aim of this study is to carry out an analysis of the intellectual structure of the introduction of AI into finance, in the period from 1995 to 2023, using SciMAT v.1.1.04 software. The results indicate how research on the incorporation of AI in finance has grown significantly, which shows the evolution and importance of this area of research. Eight main topics were obtained in this area: bank, prediction, impact, decision, valuesstock, genetic algorithm, big data analysis, and social data analysis. This study shows us how the incorporation of AI can strongly support the analysis of different financial situations such as decision making or the prediction of movements. Full article

(This article belongs to the Section Financial Technology and Innovation)

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19 pages, 977 KiB

Open AccessArticle

Reinforcement Learning Pair Trading: A Dynamic Scaling Approach

by Hongshen Yang and Avinash Malik

J. Risk Financial Manag. 2024, 17(12), 555; https://doi.org/10.3390/jrfm17120555 - 11 Dec 2024

Abstract

Cryptocurrency is a cryptography-based digital asset with extremely volatile prices. Around USD 70 billion worth of cryptocurrency is traded daily on exchanges. Trading cryptocurrency is difficult due to the inherent volatility of the crypto market. This study investigates whether Reinforcement Learning (RL) can [...] Read more.

Cryptocurrency is a cryptography-based digital asset with extremely volatile prices. Around USD 70 billion worth of cryptocurrency is traded daily on exchanges. Trading cryptocurrency is difficult due to the inherent volatility of the crypto market. This study investigates whether Reinforcement Learning (RL) can enhance decision-making in cryptocurrency algorithmic trading compared to traditional methods. In order to address this question, we combined reinforcement learning with a statistical arbitrage trading technique, pair trading, which exploits the price difference between statistically correlated assets. We constructed RL environments and trained RL agents to determine when and how to trade pairs of cryptocurrencies. We developed new reward shaping and observation/action spaces for reinforcement learning. We performed experiments with the developed reinforcement learner on pairs of BTC-GBP and BTC-EUR data separated by 1 min intervals (n = 263,520). The traditional non-RL pair trading technique achieved an annualized profit of 8.33%, while the proposed RL-based pair trading technique achieved annualized profits from 9.94% to 31.53%, depending upon the RL learner. Our results show that RL can significantly outperform manual and traditional pair trading techniques when applied to volatile markets such as cryptocurrencies. Full article

(This article belongs to the Special Issue Financial Technologies (Fintech) in Finance and Economics)

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Stretched pair trading view of price distance between <math display="inline"><semantics> <msub> <mi>p</mi> <mi>i</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>p</mi> <mi>j</mi> </msub> </semantics></math>. Figure (b), which shares the same time axis with (a), is a stretched view of (a). It presents the corresponding same actions with the crossing of Spread (S) and zones in two different views. Full article ">Figure 2
Architecture of trading strategies. Full article ">Figure 3
Window-size cut for correlation and co-integration testing. Full article ">Figure 4
The value of position observation based on investment. Full article ">Figure 5
Prices of BTCEUR and BTCGBP. Full article ">Figure 6
Comparison of portfolio value trends for RL1 and RL2 Pair Trading (A2C). Full article ">Figure A1
Comparison of Pair Trading strategies from (a) <a href="#B11-jrfm-17-00555" class="html-bibr">Gatev et al.</a> (<a href="#B11-jrfm-17-00555" class="html-bibr">2006</a>) and (b) <a href="#B17-jrfm-17-00555" class="html-bibr">Kim and Kim</a> (<a href="#B17-jrfm-17-00555" class="html-bibr">2019</a>). Full article ">

19 pages, 2836 KiB

Open AccessArticle

The Application of Machine Learning Techniques to Predict Stock Market Crises in Africa

by Muhammad Naeem, Hothefa Shaker Jassim and David Korsah

J. Risk Financial Manag. 2024, 17(12), 554; https://doi.org/10.3390/jrfm17120554 - 10 Dec 2024

Abstract

This study sought to ascertain a machine learning algorithm capable of predicting crises in the African stock market with the highest accuracy. Seven different machine-learning algorithms were employed on historical stock prices of the eight stock markets, three main sentiment indicators, and the [...] Read more.

This study sought to ascertain a machine learning algorithm capable of predicting crises in the African stock market with the highest accuracy. Seven different machine-learning algorithms were employed on historical stock prices of the eight stock markets, three main sentiment indicators, and the exchange rate of the respective countries’ currencies against the US dollar, each spanning from 1 May 2007 to 1 April 2023. It was revealed that extreme gradient boosting (XGBoost) emerged as the most effective way of predicting crises. Historical stock prices and exchange rates were found to be the most important features, exerting strong influences on stock market crises. Regarding the sentiment front, investors’ perceptions of possible volatility on the S&P 500 (Chicago Board Options Exchange (CBOE) VIX) and the Daily News Sentiment Index were identified as influential predictors. The study advances an understanding of market sentiment and emphasizes the importance of employing advanced computational techniques for risk management and market stability. Full article

(This article belongs to the Special Issue Investment Management in the Age of AI)

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23 pages, 867 KiB

Open AccessArticle

Bachelier’s Market Model for ESG Asset Pricing

by Svetlozar Rachev, Nancy Asare Nyarko, Blessing Omotade and Peter Yegon

J. Risk Financial Manag. 2024, 17(12), 553; https://doi.org/10.3390/jrfm17120553 - 10 Dec 2024

Abstract

Environmental, Social, and Governance (ESG) finance is a cornerstone of modern finance and investment, as it changes the classical return-risk view of investment by incorporating an additional dimension to investment performance: the ESG score of the investment. We define the ESG price process [...] Read more.

Environmental, Social, and Governance (ESG) finance is a cornerstone of modern finance and investment, as it changes the classical return-risk view of investment by incorporating an additional dimension to investment performance: the ESG score of the investment. We define the ESG price process and include it in an extension of Bachelier’s market model in both discrete and continuous time, enabling option pricing valuation. Full article

(This article belongs to the Section Economics and Finance)

29 pages, 1079 KiB

Open AccessArticle

Large Drawdowns and Long-Term Asset Management

by Eric Jondeau and Alexandre Pauli

J. Risk Financial Manag. 2024, 17(12), 552; https://doi.org/10.3390/jrfm17120552 - 10 Dec 2024

Abstract

Long-term investors are often hesitant to invest in assets or strategies prone to significant drawdowns, primarily due to the challenge of predicting these drawdowns. This study presents a multivariate Markov-switching model for small- and large-cap returns in the U.S. equity markets, demonstrating that [...] Read more.

Long-term investors are often hesitant to invest in assets or strategies prone to significant drawdowns, primarily due to the challenge of predicting these drawdowns. This study presents a multivariate Markov-switching model for small- and large-cap returns in the U.S. equity markets, demonstrating that three distinct regimes are necessary to capture the negative trends in expected returns during financial crises. Our findings indicate that this framework enhances the prediction of conditional drawdowns compared to standard alternative models of financial returns. Furthermore, out-of-sample analysis shows that investment strategies based on these predictions outperform those relying on models with one or two regimes. Full article

(This article belongs to the Special Issue Featured Papers in Mathematics and Finance)

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Evolution of ADD, CDD, and MDD over non-overlapping subsamples. The figure displays the evolution in percentage of ADD, <math display="inline"><semantics> <mrow> <mn>20</mn> <mo>%</mo> </mrow> </semantics></math> CDD, and MDD over various non-overlapping subsamples (from one to four quarters) between 1926 and 2020. The straight line on right plots corresponds to <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math> CED. The black lines correspond to the small caps, the red dashed lines to the large caps. CED is computed with 376, 188, and 94 observations for the one-quarter, two-quarter, and four-quarter horizons, respectively. Full article ">Figure 2
Filtered Probability of Being in the Bear State. The figure displays the filtered probability <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mrow> <mi>d</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </semantics></math> of being in the low expected return regime (bear state), for the two-regime and three-regime models. The horizontal blue line corresponds to the stationary probability of being in the bear state <math display="inline"><semantics> <mrow> <msub> <mi>π</mi> <mrow> <mi>b</mi> <mo>,</mo> <mo>∞</mo> </mrow> </msub> <mo>=</mo> <mo form="prefix">Pr</mo> <mrow> <mo>[</mo> <msub> <mi>S</mi> <mrow> <mi>d</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <msub> <mi>k</mi> <mi>b</mi> </msub> <mo>]</mo> </mrow> </mrow> </semantics></math>, where <math display="inline"><semantics> <msub> <mi>k</mi> <mi>b</mi> </msub> </semantics></math> denotes the bear state. Full article ">Figure 3
Out-of-Sample Optimal Weights—Two-quarter Horizon (1990–2020). The figure displays the temporal evolution of the optimal weight of small caps for the <math display="inline"><semantics> <mrow> <mn>20</mn> <mo>%</mo> </mrow> </semantics></math> CDD, MDD, <math display="inline"><semantics> <mrow> <mn>10</mn> <mo>%</mo> </mrow> </semantics></math> CED, and MV portfolios over the two-quarter horizon, when predictions are based on the one-regime, two-regime, and three-regime models. Full article ">Figure A1
Model Parameters: One-regime Models. Full article ">Figure A2
Model Parameters: Two-regime Models. Full article ">Figure A3
Model Parameters: Three-regime Models. Full article ">

33 pages, 3351 KiB

Open AccessArticle

Risk-Averse, Integrated Contract, and Open Market Procurement with Quantity Adjustment Costs

by Santosh Mahapatra, Santosh Kar and Shlomo Levental

J. Risk Financial Manag. 2024, 17(12), 551; https://doi.org/10.3390/jrfm17120551 - 9 Dec 2024

Abstract

This paper examines the issue cost-effective procurement of a commodity product when its spot (open) market prices are stochastic, contract prices are previously determined, and there are costs associated with adjusting (i.e., switching) the procurement quantities from an alternative. Spot (open) market and [...] Read more.

This paper examines the issue cost-effective procurement of a commodity product when its spot (open) market prices are stochastic, contract prices are previously determined, and there are costs associated with adjusting (i.e., switching) the procurement quantities from an alternative. Spot (open) market and contract as sole modes of procurement could present risks of high magnitude and uncertainty of expenses for the buyer. To address these risks, a risk-averse buyer may consider simultaneous use of both alternatives with adjustment of the purchase quantities from both the alternatives over time. Scenarios when the switching costs depend on the relative prices of the two alternatives are considered. The problem being analytically intractable, a mixed method decision model combining analytical and computational techniques to analyze the problem is proposed. The model helps identify expected optimal contract and spot market procurement quantities with respect to unknown spot prices and known contract prices over the planned procurement horizon when procurement quantity adjustment costs are influenced by the spends. The analysis reveals that it is cost-effective to continue purchasing with an existing pattern of procurement from the two alternatives until the contract to spot market price ratio reaches a threshold level and then to change the proportion of quantity purchased from the two alternatives. Using numerical analysis, we illustrate the theoretical and managerial significance of this stickiness to continue with an existing pattern until an adjustment. Full article

(This article belongs to the Collection Business Performance)

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31 pages, 1616 KiB

Open AccessArticle

Conceptualizing an Institutional Framework to Mitigate Crypto-Assets’ Operational Risk

by Deepankar Roy, Ashutosh Dubey and Daitri Tiwary

J. Risk Financial Manag. 2024, 17(12), 550; https://doi.org/10.3390/jrfm17120550 - 9 Dec 2024

Abstract

Extent ecosystems of crypto financial assets (crypto-assets) lack parity and coherence across the globe. This asymmetry is further heightened with a knowledge gap in operational risk management, wherein the global landscape of crypto-assets is characterized by unprecedented external risks and internal vulnerabilities. In [...] Read more.

Extent ecosystems of crypto financial assets (crypto-assets) lack parity and coherence across the globe. This asymmetry is further heightened with a knowledge gap in operational risk management, wherein the global landscape of crypto-assets is characterized by unprecedented external risks and internal vulnerabilities. In this study, we present a critical examination and comprehensive analysis of current crypto-asset operational guidelines across geographies. We benchmark these guidelines to the Basel Committee for Banking Supervision (BCBS) risk classification framework for crypto-assets, identifying gaps in the operations across organizations. We, hence, conceptualize a novel institutional framework which may help in understanding and mitigating the gaps in operational risks’ regulation of crypto-assets. Our proposed Crypto-asset Operational Risk Management (CORM) framework determines how operational risk associated with crypto-assets of financial institutions can be mitigated to respond to the increasing demand for crypto-assets, cross border payments, electronic money, and cryptocurrencies, across countries. Applicable to firms irrespective of their size and scale of operations, CORM aligns with global regulatory initiatives, facilitating compliance and fostering trust among stakeholders. Strengthening our argument of CORM’s applicability, we present its efficacy in the form of alternate hypothetical outcomes in two distinct real-life cases wherein crypto-asset exchanges succumbed to either external risks, such as hacking, or internal vulnerabilities. It paves the way for future regulatory response with a structured approach to addressing the unique operational risks associated with crypto-assets. The framework advocates for collaborative efforts among industry stakeholders, ensuring its adaptability to the rapidly evolving crypto landscape. It further contributes to the establishment of a more resilient and regulated financial ecosystem, inclusive of crypto-assets. By implementing CORM, institutions can navigate the complexities of crypto-assets while safeguarding their interests and promoting sustainable growth in the digital asset market. Full article

(This article belongs to the Special Issue The Future of Money: Central Bank Digital Currencies, Cryptocurrencies and Stablecoins)

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