Risks

30 pages, 525 KiB

Open AccessArticle

Navigating Dividend Decisions: The Impact of Outsider CEOs in Imputation Tax Environments

by Ariful Hoque, Md Rayhan Islam and Shahadat Hossain

Risks 2024, 12(11), 182; https://doi.org/10.3390/risks12110182 - 19 Nov 2024

This study examines the influence of outsider CEOs on corporate dividend policies, specifically within the framework of an imputation tax environment, like in Australia. The study analyses 9826 firm-year observations from Australian firms listed on the ASX from 2012 to 2021. The study [...] Read more.

This study examines the influence of outsider CEOs on corporate dividend policies, specifically within the framework of an imputation tax environment, like in Australia. The study analyses 9826 firm-year observations from Australian firms listed on the ASX from 2012 to 2021. The study reveals that outsider CEOs tend to distribute lower dividend payouts; a trend observed even after robustness tests. Methodologically, the study addresses potential endogeneity issues through sophisticated methods like propensity score matching, difference-in-difference approach, and two-stage system generalized method of moments. The findings indicate that firms led by outsider CEOs, particularly those with specialized industry knowledge, are more inclined to invest undistributed profits in capital expenditure projects, reflecting an investment-oriented strategy. This research contributes significantly to understanding the strategic decision-making of outsider CEOs and their impact on dividend policies in specific tax environments. Full article

19 pages, 692 KiB

Open AccessArticle

Climate-Related Default Probabilities

by Augusto Blanc-Blocquel, Luis Ortiz-Gracia and Simona Sanfelici

Risks 2024, 12(11), 181; https://doi.org/10.3390/risks12110181 - 14 Nov 2024

Abstract

Climate risk refers to the risks associated with climate change and has already started to impact various sectors of the economy. In this work, we focus on the impact of physical risk on the probability of default for a firm in the agribusiness [...] Read more.

Climate risk refers to the risks associated with climate change and has already started to impact various sectors of the economy. In this work, we focus on the impact of physical risk on the probability of default for a firm in the agribusiness sector. The probability of default is estimated based on the Merton model, where the firm defaults when its asset value falls below the threshold defined by its liabilities. We study the relationship between the stock value of the firm and global surface temperature anomalies, observing that an increase in temperature negatively affects the stock value and, consequently, the asset value of the firm. A decrease in the asset value of the firm translates into an increase in its probability of default. We also propose a model to assess the exposure of the firm to transition risk. Full article

(This article belongs to the Special Issue Integrating New Risks into Traditional Risk Management)

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21 pages, 2179 KiB

Open AccessArticle

Market Predictability Before the Closing Bell Rings

by Lu Zhang and Lei Hua

Risks 2024, 12(11), 180; https://doi.org/10.3390/risks12110180 - 13 Nov 2024

Abstract

This study examines the predictability of the last 30 min of intraday stock price movements within the US financial market. The analysis encompasses several potential explanatory variables, including returns from each 30 min intraday trading session, overnight returns, the federal reserve fund rate [...] Read more.

This study examines the predictability of the last 30 min of intraday stock price movements within the US financial market. The analysis encompasses several potential explanatory variables, including returns from each 30 min intraday trading session, overnight returns, the federal reserve fund rate decision days and the subsequent three days, the US dollar index, month effects, weekday effects, and market volatilities. Market-adaptive trading strategies are developed and backtested on the basis of the study’s insights. Unlike the commonly employed multiple linear regression methods with Gaussian errors, this research utilizes a Bayesian linear regression model with Student-t error terms to more accurately capture the heavy tails characteristic of financial returns. A comparative analysis of these two approaches is conducted and the limitations inherent in the traditionally used method are discussed. Our main findings are based on data from 2007 to 2018. We observed that well-studied factors such as overnight effects and intraday momentum have diminished over time. Some other new factors were significant, such as lunchtime returns during boring days and the tug-of-war effect over the days after a federal fund rate change decision. Ultimately, we incorporate findings derived from data spanning 2022 to 2024 to provide a contemporary perspective on the examined components, followed by a discussion of the study’s limitations. Full article

(This article belongs to the Special Issue Modern Statistical and Machine Learning Techniques for Financial Data)

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17 pages, 7514 KiB

Open AccessArticle

Predicting Mutual Fund Stress Levels Utilizing SEBI’s Stress Test Parameters in MidCap and SmallCap Funds Using Deep Learning Models

by Suneel Maheshwari and Deepak Raghava Naik

Risks 2024, 12(11), 179; https://doi.org/10.3390/risks12110179 - 13 Nov 2024

Abstract

Abstract: The Association of Mutual Funds of India (AMFI), under the direction of the Securities and Exchange Board of India (SEBI), provided open access to various risk parameters with respect to MidCap and SmallCap funds for the first time from February 2024. Our [...] Read more.

Abstract: The Association of Mutual Funds of India (AMFI), under the direction of the Securities and Exchange Board of India (SEBI), provided open access to various risk parameters with respect to MidCap and SmallCap funds for the first time from February 2024. Our study utilizes AMFI datasets from February 2024 to September 2024 which consisted of 14 variables. Among these, the primary variable identified in grading mutual funds is the stress test parameter, expressed as number of days required to liquidate between 50% and 25% of the portfolio, respectively, on a pro-rata basis under stress conditions as a response variable. The objective of our paper is to build and test various neural network models which can help in predicting stress levels with the highest accuracy and specificity in MidCap and SmallCap mutual funds based on AMFI’s 14 parameters as predictors. The results suggest that the simpler neural network model architectures show higher accuracy. We used Artificial Neural Networks (ANN) over other machine learning methods due to its ability to analyze the impact of dynamic interrelationships among 14 variables on the dependent variable, independent of the statistical distribution of parameters considered. Predicting stress levels with the highest accuracy in MidCap and SmallCap mutual funds will benefit investors by reducing information asymmetry while allocating investments based on their risk tolerance. It will help policy makers in designing controls to protect smaller investors and provide warnings for funds with unusually high risk. Full article

(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)

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18 pages, 871 KiB

Open AccessArticle

Defeating the Dark Sides of FinTech: A Regression-Based Analysis of Digitalization’s Role in Fostering Consumers’ Financial Inclusion in Central and Eastern Europe

by Mirela Clementina Panait, Simona Andreea Apostu, Iza Gigauri, Maria Giovanna Confetto and Maria Palazzo

Risks 2024, 12(11), 178; https://doi.org/10.3390/risks12110178 - 11 Nov 2024

Abstract

Financial technologies metamorphose economies with customer-focused innovation. In this way, financial inclusion is fostered and economic growth is increased. However, risks, trust issues, and ethical concerns stem from the faster advancement of digital technologies and expanding financial innovation. Thus, this paper aims to [...] Read more.

Financial technologies metamorphose economies with customer-focused innovation. In this way, financial inclusion is fostered and economic growth is increased. However, risks, trust issues, and ethical concerns stem from the faster advancement of digital technologies and expanding financial innovation. Thus, this paper aims to understand the risks and barriers associated with FinTech and consumer adoption, focussing on the impact of digitalization on financial products/services’ acceptance. The research investigates the impact of digitalization on financial services and the recognition of the role played in the global economy by FinTech. For this reason, the regression analysis was used to explore the influence and correlation of various variables on FinTech in Central and Eastern European (CEE) countries, such as Internet usage, online shopping, paying bills via the Internet, and making and receiving digital payments. The results show differences between three clusters of CEEs in terms of FinTech adoption. While several past studies have explored the advantages of FinTech, few studies have investigated the risks associated with its adoption, trust, and barriers to its usage in different country contexts. The present paper fills the gap by analysing the data on Internet usage, online shopping, paying bills via Internet, and sending or receiving digital payments in CEE countries. The study recommends that FinTech companies share information online not only to present their offerings to users, but also to promote financial education through clear and straightforward communication about the features of their services. This approach can indirectly benefit society by contributing to financial development, inclusion, social stability, and, consequently, sustainable development. Full article

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2 pages, 229 KiB

Open AccessEditorial

Special Issue “Interplay Between Financial and Actuarial Mathematics II”

by Corina Constantinescu and Julia Eisenberg

Risks 2024, 12(11), 177; https://doi.org/10.3390/risks12110177 - 8 Nov 2024

Abstract

Dear Reader, [...] Full article

(This article belongs to the Special Issue Interplay between Financial and Actuarial Mathematics II)

23 pages, 2182 KiB

Open AccessArticle

The Role of Personal Remittances in Economic Development: A Comparative Analysis with Foreign Direct Investment in Lebanon

by Samar F. Abou Ltaif, Simona Mihai-Yiannaki and Alkis Thrassou

Risks 2024, 12(11), 176; https://doi.org/10.3390/risks12110176 - 7 Nov 2024

Abstract

Understanding the role of personal remittances in economic development is crucial, particularly for countries like Lebanon, where these inflows play a significant role in economic stability. This study investigates the impact of personal remittances on Lebanon’s economic development over the period from 2002 [...] Read more.

Understanding the role of personal remittances in economic development is crucial, particularly for countries like Lebanon, where these inflows play a significant role in economic stability. This study investigates the impact of personal remittances on Lebanon’s economic development over the period from 2002 to 2022, employing a mixed-methods approach that combines quantitative regression analyses and qualitative data from surveys. The research finds that personal remittances have a more substantial effect on Lebanon’s GDP compared to foreign direct investment (FDI), with positive correlations observed between remittances and key economic indicators such as GDP, public debt, and unemployment rates. Additionally, qualitative findings reveal that remittances are vital for addressing basic living expenses, education, and healthcare needs, illustrating their multifaceted influence on household well-being. This study contributes to the existing literature by providing a nuanced understanding of how remittances impact economic development in Lebanon and highlights the need for policy interventions aimed at enhancing financial literacy and promoting productive investments. The findings offer valuable implications for policymakers and stakeholders, suggesting that improving the management and utilization of remittances could significantly bolster Lebanon’s economic resilience and growth prospects. Full article

(This article belongs to the Special Issue Financial Analysis, Corporate Finance and Risk Management)

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26 pages, 501 KiB

Open AccessArticle

The Relationship Between CEO Power, Labor Productivity, and Company Value in the Iraqi Stock Exchange

by Aqeel kadhim Hamad Hamad, Mahdi Salehi, Jasim Idan Barrak, Anmar Adnan Khudhair and Hussen Amran Naji Al-Refiay

Risks 2024, 12(11), 175; https://doi.org/10.3390/risks12110175 - 5 Nov 2024

Abstract

The current study investigates the relationship between the CEO’s power, the workforce’s productivity, and the company’s value in Iraqi stock exchange companies. A sample of 34 companies listed on the Iraqi Stock Exchange from 2016 to 2021 was tested using a multiple regression [...] Read more.

The current study investigates the relationship between the CEO’s power, the workforce’s productivity, and the company’s value in Iraqi stock exchange companies. A sample of 34 companies listed on the Iraqi Stock Exchange from 2016 to 2021 was tested using a multiple regression model, a panel data approach, and a fixed effects model. CEO power is measured by the busing factor analysis approach, which integrates four indices: CEO salary, CEO ownership, CEO tenure, and CEO control over board members. The findings indicate a positive and significant relationship between CEO power and labor productivity. Also, there is a negative and significant relationship between CEO power and the stickiness of labor costs. On the other hand, we found a positive and significant relationship between the CEO power and firm value. In addition, labor cost stickiness has a positive effect on firm value. By highlighting the CEOs’ power, this research tries to increase companies’ attention to this issue and its effect on improving employment productivity, cost management, and firm value. Full article

33 pages, 9119 KiB

Open AccessArticle

Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers

by Victor Chang, Sharuga Sivakulasingam, Hai Wang, Siu Tung Wong, Meghana Ashok Ganatra and Jiabin Luo

Risks 2024, 12(11), 174; https://doi.org/10.3390/risks12110174 - 4 Nov 2024

Abstract

The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify [...] Read more.

The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to accurately classify credit card customers as “good” or “bad” to minimize capital loss. This research investigates the approaches for predicting the default status of credit card customer via the application of various machine-learning models, including neural networks, logistic regression, AdaBoost, XGBoost, and LightGBM. Performance metrics such as accuracy, precision, recall, F1 score, ROC, and MCC for all these models are employed to compare the efficiency of the algorithms. The results indicate that XGBoost outperforms other models, achieving an accuracy of 99.4%. The outcomes from this study suggest that effective credit risk analysis would aid in informed lending decisions, and the application of machine-learning and deep-learning algorithms has significantly improved predictive accuracy in this domain. Full article

(This article belongs to the Special Issue Volatility Modeling in Financial Market)

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14 pages, 644 KiB

Open AccessArticle

Spread Option Pricing Under Finite Liquidity Framework

by Traian A. Pirvu and Shuming Zhang

Risks 2024, 12(11), 173; https://doi.org/10.3390/risks12110173 - 31 Oct 2024

Abstract

This work explores a finite liquidity model to price spread options and assess the liquidity impact. We employ Kirk approximation for computing the spread option price and its delta. The latter is needed since the liquidity impact is caused by the delta hedging [...] Read more.

This work explores a finite liquidity model to price spread options and assess the liquidity impact. We employ Kirk approximation for computing the spread option price and its delta. The latter is needed since the liquidity impact is caused by the delta hedging of a large investor. Our main contribution is a novel methodology to price spread options in this paradigm. Kirk approximation in conjunction with Monte Carlo simulations yields the spread option prices. Moreover, the antithetic and control variates variance reduction techniques improve the performance of our method. Numerical experiments reveal that the finite liquidity causes a liquidity value adjustment in option prices ranging from 0.53% to 2.81%. The effect of correlation on prices is also explored, and as expected the option price increases due to the diversification effect, but the liquidity impact decreases slightly. Full article

(This article belongs to the Special Issue Mathematical Methods Applied in Pricing and Investment Problems)

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19 pages, 1774 KiB

Open AccessArticle

Effective Machine Learning Techniques for Dealing with Poor Credit Data

by Dumisani Selby Nkambule, Bhekisipho Twala and Jan Harm Christiaan Pretorius

Risks 2024, 12(11), 172; https://doi.org/10.3390/risks12110172 - 30 Oct 2024

Abstract

Credit risk is a crucial component of daily financial services operations; it measures the likelihood that a borrower will default on a loan, incurring an economic loss. By analysing historical data for assessment of the creditworthiness of a borrower, lenders can reduce credit [...] Read more.

Credit risk is a crucial component of daily financial services operations; it measures the likelihood that a borrower will default on a loan, incurring an economic loss. By analysing historical data for assessment of the creditworthiness of a borrower, lenders can reduce credit risk. Data are vital at the core of the credit decision-making processes. Decision-making depends heavily on accurate, complete data, and failure to harness high-quality data would impact credit lenders when assessing the loan applicants’ risk profiles. In this paper, an empirical comparison of the robustness of seven machine learning algorithms to credit risk, namely support vector machines (SVMs), naïve base, decision trees (DT), random forest (RF), gradient boosting (GB), K-nearest neighbour (K-NN), and logistic regression (LR), is carried out using the Lending Club credit data from Kaggle. This task uses seven performance measures, including the F1 Score (recall, accuracy, and precision), ROC-AUC, and HL and MCC metrics. Then, the harnessing of generative adversarial networks (GANs) simulation to enhance the robustness of the single machine learning classifiers for predicting credit risk is proposed. The results show that when GANs imputation is incorporated, the decision tree is the best-performing classifier with an accuracy rate of 93.01%, followed by random forest (92.92%), gradient boosting (92.33%), support vector machine (90.83%), logistic regression (90.76%), and naïve Bayes (89.29%), respectively. The classifier is the worst-performing method with a k-NN (88.68%) accuracy rate. Subsequently, when GANs are optimised, the accuracy rate of the naïve Bayes classifier improves significantly to (90%) accuracy rate. Additionally, the average error rate for these classifiers is over 9%, which implies that the estimates are not far from the actual values. In summary, most individual classifiers are more robust to missing data when GANs are used as an imputation technique. The differences in performance of all seven machine learning algorithms are significant at the 95% level. Full article

(This article belongs to the Special Issue Financial Analysis, Corporate Finance and Risk Management)

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32 pages, 6252 KiB

Open AccessArticle

News Sentiment and Liquidity Risk Forecasting: Insights from Iranian Banks

by Hamed Mirashk, Amir Albadvi, Mehrdad Kargari and Mohammad Ali Rastegar

Risks 2024, 12(11), 171; https://doi.org/10.3390/risks12110171 - 30 Oct 2024

Abstract

This study addresses the critical challenge of predicting liquidity risk in the banking sector, as emphasized by the Basel Committee on Banking Supervision. Liquidity risk serves as a key metric for evaluating a bank’s short-term resilience to liquidity shocks. Despite limited prior research, [...] Read more.

This study addresses the critical challenge of predicting liquidity risk in the banking sector, as emphasized by the Basel Committee on Banking Supervision. Liquidity risk serves as a key metric for evaluating a bank’s short-term resilience to liquidity shocks. Despite limited prior research, particularly in anticipating upcoming positions of bank liquidity risk, especially in Iranian banks with high liquidity risk, this study aimed to develop an AI-based model to predict the liquidity coverage ratio (LCR) under Basel III reforms, focusing on its direction (up, down, stable) rather than on exact values, thus distinguishing itself from previous studies. The research objectively explores the influence of external signals, particularly news sentiment, on liquidity prediction, through novel data augmentation, supported by empirical research, as qualitative factors to build a model predicting LCR positions using AI techniques such as deep and convolutional neural networks. Focused on a semi-private Islamic bank in Iran incorporating 4,288,829 Persian economic news articles from 2004 to 2020, this study compared various AI algorithms. It revealed that real-time news content offers valuable insights into impending changes in LCR, particularly in Islamic banks with elevated liquidity risks, achieving a predictive accuracy of 88.6%. This discovery underscores the importance of complementing traditional qualitative metrics with contemporary news sentiments as a signal, particularly when traditional measures require time-consuming data preparation, offering a promising avenue for risk managers seeking more robust liquidity risk forecasts. Full article

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18 pages, 585 KiB

Open AccessArticle

A Comparison of Financial Risk-Tolerance Assessment Methods in Predicting Subsequent Risk Tolerance and Future Portfolio Choices

by Eun Jin Kwak and John E. Grable

Risks 2024, 12(11), 170; https://doi.org/10.3390/risks12110170 - 24 Oct 2024

Abstract

This study explores the effectiveness of various methods for measuring risk tolerance, with the aim to better understand the risk-taking attitudes and behaviors of financial decision-makers. Using data collected between October 2020 and March 2021, the research investigates three key areas: (a) the [...] Read more.

This study explores the effectiveness of various methods for measuring risk tolerance, with the aim to better understand the risk-taking attitudes and behaviors of financial decision-makers. Using data collected between October 2020 and March 2021, the research investigates three key areas: (a) the stability of risk tolerance over a six-month period, (b) the individual and household characteristics that predict future risk tolerance, and (c) the predictive accuracy of various risk-tolerance assessment methods in relation to portfolio choices made by financial decision-makers. The results show that risk-tolerance scores derived from a psychometrically developed scale provide the most accurate insights into future risk-taking attitudes and portfolio decisions. For those looking for a simple way to assess both current and future risk tolerance and portfolio choices, a stated-preference item can be effective. Although less consistent, a revealed-preference test can also be used to predict risk tolerance and risk-taking behavior. Findings provide guidance for financial decision-makers and financial advisors by comparing the key features of the three primary risk-tolerance assessment methods evaluated in this study. The study also establishes a foundational basis for selecting the most appropriate evaluation approach, based on the variables identified in the findings. Full article

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18 pages, 3948 KiB

Open AccessReview

A Systematic Literature Review of Insurance Claims Risk Measurement Using the Hidden Markov Model

by Hilda Azkiyah Surya, Sukono, Herlina Napitupulu and Noriszura Ismail

Risks 2024, 12(11), 169; https://doi.org/10.3390/risks12110169 - 23 Oct 2024

Abstract

In the rapidly evolving field of insurance, accurate risk measurement is crucial for effective claims management and financial stability. Therefore, this research presented a systematic literature review (SLR) on insurance claims risk measurement using the Hidden Markov Model (HMM). Bibliometric analysis was conducted [...] Read more.

In the rapidly evolving field of insurance, accurate risk measurement is crucial for effective claims management and financial stability. Therefore, this research presented a systematic literature review (SLR) on insurance claims risk measurement using the Hidden Markov Model (HMM). Bibliometric analysis was conducted using VOSviewer 1.6.20 and ResearchRabbit software to map research trends and collaboration networks in this topic. This review explored the implementation of the HMM in predicting the frequency and severity of insurance claims, with a focus on the statistical distribution methods used. In addition, the research emphasized the influence of the number of hidden states in the HMM on claims behavior, both in terms of frequency and magnitude, and provided interpretations of these hidden dynamics. Data sources for this review comprised three databases, namely, Scopus, ScienceDirect, and Dimensions, and additional papers from a website. The article selection process followed updated PRISMA 2020 guidelines, resulting in twelve key papers relevant to the topic. The results offered insights into the application of the HMM for forecasting the frequency and severity of insurance claims and opened avenues for further investigation on distribution models and hidden state modeling. Full article

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68 pages, 1040 KiB

Open AccessArticle

Managing Financial Risks of Global Companies Through Corporate Social Responsibility: The Specifics of Sustainable Employment in Developed and Developing Countries

by Bobir O. Tursunov, Chinara R. Kulueva, Olim K. Abdurakhmanov, Larisa V. Shabaltina and Tatyana I. Bezdenezhnykh

Risks 2024, 12(10), 168; https://doi.org/10.3390/risks12100168 - 21 Oct 2024

Abstract

The motivation for this research was the desire to disclose the social nature of the financial risks of global companies: the authors attempted a scientific explanation of the influence of corporate social responsibility, which is manifested through the preservation and creation of additional [...] Read more.

The motivation for this research was the desire to disclose the social nature of the financial risks of global companies: the authors attempted a scientific explanation of the influence of corporate social responsibility, which is manifested through the preservation and creation of additional jobs, on the financial risks of global companies. The research aims to establish the interdependence between financial risks and sustainable employment in global companies. This goal is achieved using the SEM (structural equation modeling) method based on corporate statistics from the Fortune “Global 500” rankings for 2021–2023. As a result, the consequences of global companies’ CSR (corporate social responsibility) practices in personnel management and financial risk management are modeled and described through quantitative and qualitative patterns. The established regularities proved that for developed and developing countries, the larger the number of employees, the lower the financial risks of global companies—the risk of a decrease in profitability, the risk of loss of profit, and the risk of depreciation of assets. The main conclusion is that there is a close systemic relationship between the financial risks of global companies and their workforce size, suggesting that CSR is key to highly effective financial risk management. A clear distinction between the practices of financial risk management through CSR in developed and developing countries forms the basis of the theoretical significance of the research results. The authors provide recommendations to improve the current practice of financial risk management in global companies by integrating it more closely with personnel management practices, highlighting their managerial relevance. It is proposed that corporate strategies for global companies in developed countries should focus on reducing the risk of declining profitability, as CSR has the most pronounced and consistent impact on this particular financial risk. In developing countries, corporate strategies are recommended to be structured by diversifying the areas of CSR application, with the most promising in financial risk management being the reduction in asset depreciation risk and the reduction in profitability risk. The findings of this research have practical significance because they enhance the predictability of CSR activities of global companies and open up opportunities for highly accurate forecasting of the financial risk implications of ensuring sustainable employment by global companies, considering the specificities of developed and developing countries. Full article

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24 pages, 1278 KiB

Open AccessArticle

Enhancing Portfolio Decarbonization Through SensitivityVaR and Distorted Stochastic Dominance

by Aniq Rohmawati, Oki Neswan, Dila Puspita and Khreshna Syuhada

Risks 2024, 12(10), 167; https://doi.org/10.3390/risks12100167 - 19 Oct 2024

Abstract

Recent trends in portfolio management emphasize the importance of reducing carbon footprints and aligning investments with sustainable practices. This paper introduces Sensitivity Value-at-Risk (SensitivityVaR), an advanced distortion risk measure that combines Value-at-Risk (VaR) and Expected Shortfall (ES) with the Cornish–Fisher expansion. SensitivityVaR provides [...] Read more.

Recent trends in portfolio management emphasize the importance of reducing carbon footprints and aligning investments with sustainable practices. This paper introduces Sensitivity Value-at-Risk (SensitivityVaR), an advanced distortion risk measure that combines Value-at-Risk (VaR) and Expected Shortfall (ES) with the Cornish–Fisher expansion. SensitivityVaR provides a more robust framework for managing risk, particularly under extreme market conditions. By incorporating first- and second-order distorted stochastic dominance criteria, we enhance portfolio decarbonization strategies, aligning financial objectives with environmental targets such as the Paris Agreement’s goal of a 7% annual reduction in carbon intensity from 2019 to 2050. Our empirical analysis evaluates the impact of integrating carbon intensity data—including Scope 1, Scope 2, and Scope 3 emissions—on portfolio optimization, focusing on key sectors like technology, energy, and consumer goods. The results demonstrate the effectiveness of SensitivityVaR in managing both risk and environmental impact. The methodology led to significant reductions in carbon intensity across different portfolio configurations, while preserving competitive risk-adjusted returns. By optimizing tail risks and limiting exposure to carbon-intensive assets, this approach produced more balanced and efficient portfolios that aligned with both financial and sustainability goals. These findings offer valuable insights for institutional investors and asset managers aiming to integrate climate considerations into their investment strategies without compromising financial performance. Full article

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15 pages, 512 KiB

Open AccessArticle

Polynomial Moving Regression Band Stocks Trading System

by Gil Cohen

Risks 2024, 12(10), 166; https://doi.org/10.3390/risks12100166 - 18 Oct 2024

Abstract

In this research, we attempted to fit a trading system based on polynomial moving regression bands (MRB) to Nasdaq100 stocks from 2017 till the end of March 2024. Since stocks movement does not follow a linear behavior, we used multiple degree polynomial regression [...] Read more.

In this research, we attempted to fit a trading system based on polynomial moving regression bands (MRB) to Nasdaq100 stocks from 2017 till the end of March 2024. Since stocks movement does not follow a linear behavior, we used multiple degree polynomial regression models to identify the stocks’ trends and two standard deviations from the regression model to generate the trading signals. This way, the MRB was transformed into a momentum indicator designed to identify strong uptrends that can be used by a fully automated trading system. Our results indicate that the behavior of Nasdaq100 stocks can be tracked using all three examined polynomial models and can be traded profitably using fully automated systems based on those models. The best performing model was the model that used a four-degree polynomial MRB achieving the highest average net profit (USD 162.73). Regarding the risks involved, the third model has the lowest loss in dollar value (USD −95.52), and the highest minimum percent of profitable trades (41.51%) and profit factor (0.55) that indicates that this strategy is relatively less risky than the other two strategies. Full article

(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)

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19 pages, 620 KiB

Open AccessArticle

The Role of Entrepreneur’s Face Disclosure on Crowdfunding Success

by Lenny Phulong Mamaro and Athenia Bongani Sibindi

Risks 2024, 12(10), 165; https://doi.org/10.3390/risks12100165 - 15 Oct 2024

Abstract

The evaluation of crowdfunding campaigns varies from person to person; some investors are more interested in the project’s creativity, and others are more concerned with the profiles of entrepreneurs. The study investigated how entrepreneurs’ face disclosure influenced the success of crowdfunding. Secondary data [...] Read more.

The evaluation of crowdfunding campaigns varies from person to person; some investors are more interested in the project’s creativity, and others are more concerned with the profiles of entrepreneurs. The study investigated how entrepreneurs’ face disclosure influenced the success of crowdfunding. Secondary data were collected from multiple crowdfunding platforms for projects in Africa. That is, cross-country data from 54 African countries, to overcome data limitations from a single country. An econometrics analysis revealed that the facial disclosure of entrepreneurs increases the probability of crowdfunding success by 3%. Images, videos, and backers had a positive influence on the success of crowdfunding. On the contrary, the duration of the crowdfunding campaign was negatively associated with its success. To reduce the knowledge asymmetry between creators and backers, those prepared to start a crowdfunding project must provide as much information as possible to show their abilities. This study contributes to understanding the role of disclosing an entrepreneur’s profile on economic exchanges to the success of online crowdfunding. Full article

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18 pages, 925 KiB

Open AccessArticle

Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence

by M. K. Nallakaruppan, Himakshi Chaturvedi, Veena Grover, Balamurugan Balusamy, Praveen Jaraut, Jitendra Bahadur, V. P. Meena and Ibrahim A. Hameed

Risks 2024, 12(10), 164; https://doi.org/10.3390/risks12100164 - 15 Oct 2024

Abstract

The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are [...] Read more.

The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are more accessible than ever, but it has been challenging to create and implement systems that support real-world financial applications, primarily due to their lack of transparency and explainability—both of which are essential for building trustworthy technology. The novelty of this study lies in the development of an explainable AI (XAI) model that not only addresses these transparency concerns but also serves as a tool for policy development in credit risk management. By offering a clear understanding of the underlying factors influencing AI predictions, the proposed model can assist regulators and financial institutions in shaping data-driven policies, ensuring fairness, and enhancing trust. This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms. The model leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest models achieved the highest accuracy levels of 0.89 and 0.93, respectively. The model’s performance was further tested using a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching accuracies of 0.90 and 0.93, respectively. To ensure reliable explainable AI (XAI) modeling, these models were chosen due to the binary classification nature of the problem. LIME and SHAP were employed to present the XAI models as both local and global surrogates. Full article

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