Risks

11 pages, 1168 KiB

Open AccessArticle

Sustaining Algeria’s Retirement System in the Population Aging Context: Could a Contribution Cap Strategy Work?

by Farid Flici and Inmaculada Dominguez-Fabian

Risks 2024, 12(6), 96; https://doi.org/10.3390/risks12060096 - 14 Jun 2024

Viewed by 257

Previous research predicts an increasing financial deficit in Algeria’s PAYG retirement system, mainly due to rapid population aging, and parametric adjustments will be insufficient to alleviate this imbalance. Mitigating the effects of population aging will necessitate further intervention. In this work, we analyze [...] Read more.

Previous research predicts an increasing financial deficit in Algeria’s PAYG retirement system, mainly due to rapid population aging, and parametric adjustments will be insufficient to alleviate this imbalance. Mitigating the effects of population aging will necessitate further intervention. In this work, we analyze how capping contributed salaries can help to mitigate the effects of population aging on the retirement system. Under generous Pay-As-You-Go schemes, promised pension payouts far exceed contributions. Thus, restricting contributions is expected to reduce the burden of future benefits by accepting lower contributions today, while directing public subsidies to low-income individuals. We simulate the future evolution of the financial balance of Algeria’s retirement system under various contributable salary caps versus various scenarios of environmental evolution and potential parametric reform actions. The results demonstrated that a 40% cap, along with major parametric reforms and an ideal environment, would help achieve a cumulatively balanced system in the long run. Full article

(This article belongs to the Special Issue Life Insurance and Pensions: Latest Advances and Prospects)

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19 pages, 415 KiB

Open AccessArticle

Expected Utility Optimization with Convolutional Stochastically Ordered Returns

by Romain Gauchon and Karim Barigou

Risks 2024, 12(6), 95; https://doi.org/10.3390/risks12060095 - 14 Jun 2024

Viewed by 202

Abstract

Expected utility theory is critical for modeling rational decision making under uncertainty, guiding economic agents as they seek to optimize outcomes. Traditional methods often require restrictive assumptions about underlying stochastic processes, limiting their applicability. This paper expands the theoretical framework by considering investment [...] Read more.

Expected utility theory is critical for modeling rational decision making under uncertainty, guiding economic agents as they seek to optimize outcomes. Traditional methods often require restrictive assumptions about underlying stochastic processes, limiting their applicability. This paper expands the theoretical framework by considering investment returns modeled by a stochastically ordered family of random variables under the convolution order, including Poisson, Gamma, and exponential distributions. Utilizing fractional calculus, we derive explicit, closed-form expressions for the derivatives of expected utility for various utility functions, significantly broadening the potential for analytical and computational applications. We apply these theoretical advancements to a case study involving the optimal production strategies of competitive firms, demonstrating the practical implications of our findings in economic decision making. Full article

39 pages, 4316 KiB

Open AccessReview

Cryptocurrencies’ Impact on Accounting: Bibliometric Review

by Georgiana-Iulia Lazea, Ovidiu-Constantin Bunget and Cristian Lungu

Risks 2024, 12(6), 94; https://doi.org/10.3390/risks12060094 - 11 Jun 2024

Viewed by 376

Abstract

This bibliometric study explores the cryptocurrency accounting (CA) literature and the connections between authors, institutions, and countries where cryptocurrency activity involves transactions that must be legally recognized in accounting, ensure accuracy and reliability for auditing, and adhere to tax compliance. The design involves [...] Read more.

This bibliometric study explores the cryptocurrency accounting (CA) literature and the connections between authors, institutions, and countries where cryptocurrency activity involves transactions that must be legally recognized in accounting, ensure accuracy and reliability for auditing, and adhere to tax compliance. The design involves the selection of data from Web of Science Core Collection (WoS) and Scopus, published between 2007 and 2023. The technique helps identify influential publications, collaboration networks, thematic clusters, and trends in research on CA using tools VOSviewer, Biblioshiny, and MS Excel. The originality of the study lies in its dual role as a support for accounting professionals and academics to develop innovative solutions for the challenges posed by crypto technology across core accounting areas: financial and managerial accounting, taxation, and auditing. The findings offer insights into the themes mentioned, and even if the collaboration between the authors is not very developed, the innovation and public recognition of the subject could raise researchers’ interest. The limitation of the dataset is that it does not cover all relevant publications in a different period from the one in which the data were retrieved, 9–11 May 2024. This review might need periodic updates because the CA landscape is constantly changing. Full article

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17 pages, 1062 KiB

Open AccessArticle

Deep Learning Option Price Movement

by Weiguan Wang and Jia Xu

Risks 2024, 12(6), 93; https://doi.org/10.3390/risks12060093 - 4 Jun 2024

Viewed by 346

Abstract

Understanding how price-volume information determines future price movement is important for market makers who frequently place orders on both buy and sell sides, and for traders to split meta-orders to reduce price impact. Given the complex non-linear nature of the problem, we consider [...] Read more.

Understanding how price-volume information determines future price movement is important for market makers who frequently place orders on both buy and sell sides, and for traders to split meta-orders to reduce price impact. Given the complex non-linear nature of the problem, we consider the prediction of the movement direction of the mid-price on an option order book, using machine learning tools. The applicability of such tools on the options market is currently missing. On an intraday tick-level dataset of options on an exchange traded fund from the Chinese market, we apply a variety of machine learning methods, including decision tree, random forest, logistic regression, and long short-term memory neural network. As machine learning models become more complex, they can extract deeper hidden relationship from input features, which classic market microstructure models struggle to deal with. We discover that the price movement is predictable, deep neural networks with time-lagged features perform better than all other simpler models, and this ability is universal and shared across assets. Using an interpretable model-agnostic tool, we find that the first two levels of features are the most important for prediction. The findings of this article encourage researchers as well as practitioners to explore more sophisticated models and use more relevant features. Full article

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25 pages, 4986 KiB

Open AccessArticle

Estimating Disease-Free Life Expectancy Based on Clinical Data from the French Hospital Discharge Database

by Oleksandr Sorochynskyi, Quentin Guibert, Frédéric Planchet and Michaël Schwarzinger

Risks 2024, 12(6), 92; https://doi.org/10.3390/risks12060092 - 3 Jun 2024

Viewed by 242

Abstract

The development of health indicators to measure healthy life expectancy (HLE) is an active field of research aimed at summarizing the health of a population. Although many health indicators have emerged in the literature as critical metrics in public health assessments, the methods [...] Read more.

The development of health indicators to measure healthy life expectancy (HLE) is an active field of research aimed at summarizing the health of a population. Although many health indicators have emerged in the literature as critical metrics in public health assessments, the methods and data to conduct this evaluation vary considerably in nature and quality. Traditionally, health data collection relies on population surveys. However, these studies, typically of limited size, encompass only a small yet representative segment of the population. This limitation can necessitate the separate estimation of incidence and mortality rates, significantly restricting the available analysis methods. In this article, we leverage an extract from the French National Hospital Discharge database to define health indicators. Our analysis focuses on the resulting Disease-Free Life Expectancy (Dis-FLE) indicator, which provides insights based on the hospital trajectory of each patient admitted to hospital in France during 2008–2013. Through this research, we illustrate the advantages and disadvantages of employing large clinical datasets as the foundation for more robust health indicators. We shed light on the opportunities that such data offer for a more comprehensive understanding of the health status of a population. In particular, we estimate age-dependent hazard rates associated with sex, alcohol abuse, tobacco consumption, and obesity, as well as geographic location. Simultaneously, we delve into the challenges and limitations that arise when adopting such a data-driven approach. Full article

(This article belongs to the Special Issue Advancement in Mortality Forecasting and Mortality/Longevity Risk Management)

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20 pages, 870 KiB

Open AccessArticle

Determinants of Corporate Indebtedness in Portugal: An Analysis of Financial Behaviour Clusters

by Fernando Tavares, Eulália Santos, Margarida Freitas Oliveira and Luís Almeida

Risks 2024, 12(6), 91; https://doi.org/10.3390/risks12060091 - 31 May 2024

Viewed by 146

Abstract

Corporate indebtedness is a powerful tool in determining a company’s financial health with impacts on its image and reputation. The main objective of this research is to study the determining factors in corporate indebtedness in Portugal. It also has the secondary objectives of [...] Read more.

Corporate indebtedness is a powerful tool in determining a company’s financial health with impacts on its image and reputation. The main objective of this research is to study the determining factors in corporate indebtedness in Portugal. It also has the secondary objectives of creating clusters of companies’ behaviour in relation to the use of credit and verifying their differences in relation to the characteristics of the companies. It uses a quantitative methodology based on a questionnaire survey of 1957 Portuguese companies. The results of the factor analysis show the formation of six determining factors in corporate indebtedness, namely the negotiating relationship with banks, financing, cycle and indebtedness, company operating performance, guarantees used to obtain bank financing and financing risk analysis as well as secondary forms of bank financing. The application of cluster analysis to the six factors formed led to the classification of companies into three clusters: the resilient financial cluster, the operational excellence cluster and the strategic financial cluster. There are several statistically significant differences in the corporate financing factors in relation to the clusters to which they belong. The evidence of the factors and clusters explaining company financing provides insights for improving credit access practices and for implementing public policies that facilitate access to credit and promote economic development. Full article

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

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13 pages, 379 KiB

Open AccessArticle

Key Determinants of Corporate Governance in Financial Institutions: Evidence from South Africa

by Floyd Khoza, Daniel Makina and Patricia Lindelwa Makoni

Risks 2024, 12(6), 90; https://doi.org/10.3390/risks12060090 - 30 May 2024

Viewed by 341

Abstract

The purpose of this study was to examine the key determinants of corporate governance in selected financial institutions. Using South African financial institutions as a unit of analysis, namely insurance companies and banks, the study employed a panel generalised method of moments (GMM) [...] Read more.

The purpose of this study was to examine the key determinants of corporate governance in selected financial institutions. Using South African financial institutions as a unit of analysis, namely insurance companies and banks, the study employed a panel generalised method of moments (GMM) model using a data set for the period from 2007 to 2020, to assess key determinants of corporate governance proxies identified for the study. The study sampled 21 South African financial institutions composed of Johannesburg Securities Exchange (JSE) listed and unlisted banks and insurance companies. To measure corporate governance, the study developed a composite index employing the principal components analysis (PCA) method. The findings revealed a positive and significant association between the corporate governance index and its lagged variables. Furthermore, a significant and positive link was found between the efficiency ratio and corporate governance index and capital adequacy ratio (CAR); corporate governance index and firm size; corporate governance index and leverage ratio (LEV); and corporate governance index and return on assets (ROA). However, a negative and significant correlation was found between financial stability and the corporate governance index. The link between return on equity (ROE) and corporate governance was insignificant. A small cohort of financial institutions was excluded because it was challenging to obtain complete annual reports to extract the required data. The study was limited to only five corporate governance measures, namely board diversity, board size, board composition (independent non-executive directors and non-executive directors), and board remuneration. The findings are anticipated to persuade developing countries to pay special attention to how corporate governance is measured. Full article

(This article belongs to the Special Issue Risk Governance in the Finance and Insurance Industry)

23 pages, 441 KiB

Open AccessArticle

A Case Study of Bank Equity Valuation Methods Employed by South African, Nigerian and Kenyan Equity Researchers

by Vusani Moyo and Ayodeji Michael Obadire

Risks 2024, 12(6), 89; https://doi.org/10.3390/risks12060089 - 27 May 2024

Viewed by 310

Abstract

The valuation of banks is inherently complicated because of the uncertainties arising from their information opaqueness and inherent risks. Unlike non-banking firms, banks require specialised equity-side valuation approaches. This study addresses a gap in the literature by examining valuation methods used by bank [...] Read more.

The valuation of banks is inherently complicated because of the uncertainties arising from their information opaqueness and inherent risks. Unlike non-banking firms, banks require specialised equity-side valuation approaches. This study addresses a gap in the literature by examining valuation methods used by bank equity researchers. The study used a total of 201 reports on South African banks (2018–2023), 56 reports on Nigerian banks (2018–2023), and 27 reports on Kenyan banks (2018–2023) to investigate the bank equity valuation methods utilised by analysts in the employ of Investec Ltd. and Standard Bank Group Ltd. The study’s findings show that Investec’s South African analysts predominantly used the warranted equity method, based on book value (BV), and return on equity (ROE), for valuing shares throughout the South African, Nigerian, and Kenyan banks surveyed. Furthermore, Standard Bank Group’s analysts employed this method, incorporating tangible net asset value (tNAV) and return on tangible equity (ROTE), for South African and Nigerian banks, but in Kenya their analysts used the residual income model to value the equities of the five Kenyan banks they covered. These findings suggest that the warranted equity method and the residual income model are the mostly used bank equity valuation methods in South Africa, Nigeria, and Kenya. The study concludes with relevant recommendations, offering significant insights for banks, regulators, and investors to make knowledgeable decisions concerning equity valuation. Full article

17 pages, 574 KiB

Open AccessArticle

Some Results on Bivariate Squared Maximum Sharpe Ratio

by Samane Al-sadat Mousavi, Ali Dolati and Ali Dastbaravarde

Risks 2024, 12(6), 88; https://doi.org/10.3390/risks12060088 - 24 May 2024

Viewed by 361

Abstract

The Sharpe ratio is a widely used tool for assessing investment strategy performance. An essential part of investing involves creating an appropriate portfolio by determining the optimal weights for desired assets. Before constructing a portfolio, selecting a set of investment opportunities is crucial. [...] Read more.

The Sharpe ratio is a widely used tool for assessing investment strategy performance. An essential part of investing involves creating an appropriate portfolio by determining the optimal weights for desired assets. Before constructing a portfolio, selecting a set of investment opportunities is crucial. In the absence of a risk-free asset, investment opportunities can be identified based on the Sharpe ratios of risky assets and their correlation. The maximum squared Sharpe ratio serves as a useful metric that summarizes the performance of an investment opportunity in a single value, considering the Sharpe ratios of assets and their correlation coefficients. However, the assumption of a normal distribution in asset returns, as implied by the Sharpe ratio and related metrics, may not always hold in practice. Non-normal returns with a non-linear dependence structure can result in an overestimation or underestimation of these metrics. Copula functions are commonly utilized to address non-normal dependence structures. This study examines the impact of asset dependence on the squared maximum Sharpe ratio using copulas and proposes a copula-based approach to tackle the estimation issue. The performance of the proposed estimator is illustrated through simulation and real-data analysis. Full article

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21 pages, 1695 KiB

Open AccessArticle

Integration of AI and IoT into Corporate Social Responsibility Strategies for Financial Risk Management and Sustainable Development

by Anna Viktorovna Shkalenko and Anton V. Nazarenko

Risks 2024, 12(6), 87; https://doi.org/10.3390/risks12060087 - 23 May 2024

Viewed by 468

Abstract

This research explores the integration of artificial intelligence (AI) and the Internet of Things (IoT) within corporate social responsibility (CSR) strategies, focusing on financial risk management and sustainable development. Employing a novel Coevolutionary multi-paradigm approach to technological development, this study examines how these [...] Read more.

This research explores the integration of artificial intelligence (AI) and the Internet of Things (IoT) within corporate social responsibility (CSR) strategies, focusing on financial risk management and sustainable development. Employing a novel Coevolutionary multi-paradigm approach to technological development, this study examines how these technologies can be embedded into CSR practices to enhance sustainability and manage risks effectively. The findings reveal that successful integration depends significantly on the adaptability of institutional structures to support technological innovations. This study contributes to the literature by providing a comprehensive analysis of the intersection of AI, IoT, and CSR, highlighting the necessity for robust mechanisms and policies that ensure security, standardization, and sustainable use of emerging technologies. Through this investigation, this research offers a new perspective on leveraging advanced technologies to advance corporate sustainability and risk management objectives. Full article

(This article belongs to the Special Issue Managing Financial Risks Based on Corporate Social Responsibility for Sustainable Development II)

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15 pages, 1332 KiB

Open AccessArticle

Commodity Market Risk: Examining Price Co-Movements in the Pakistan Mercantile Exchange

by Falik Shear, Muhammad Bilal, Badar Nadeem Ashraf and Nasir Ali

Risks 2024, 12(6), 86; https://doi.org/10.3390/risks12060086 - 22 May 2024

Viewed by 506

Abstract

Commodity price co-movements significantly impact investment decisions. High correlations constrain portfolio diversification and limit risk mitigation potential. While international markets often exhibit strong price linkages, understanding national-level dynamics is crucial for effective portfolio optimization. In this paper, we examine the commodity price co-movements [...] Read more.

Commodity price co-movements significantly impact investment decisions. High correlations constrain portfolio diversification and limit risk mitigation potential. While international markets often exhibit strong price linkages, understanding national-level dynamics is crucial for effective portfolio optimization. In this paper, we examine the commodity price co-movements within three key sectors—energy, metals, and agriculture—in the specific context of Pakistan. Utilizing data from 13 January 2013 to 20 August 2020 and employing an autoregressive distributed lag (ARDL) model, we reveal a surprising finding: co-movement among these sectors is weak and primarily short-term. This challenges the conventional assumption of tight coupling in national markets and offers exciting implications for investors. Our analysis suggests that Pakistani commodities hold significant diversification potential, opening promising avenues for risk-reduction strategies within the national market. Full article

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14 pages, 420 KiB

Open AccessArticle

Use of Prediction Bias in Active Learning and Its Application to Large Variable Annuity Portfolios

by Hyukjun Gweon, Shu Li and Yangxuan Xu

Risks 2024, 12(6), 85; https://doi.org/10.3390/risks12060085 - 22 May 2024

Viewed by 556

Abstract

Given the computational challenges associated with valuing large variable annuity (VA) portfolios, a variety of data mining frameworks, including metamodeling and active learning, have been proposed in recent years. Active learning, a promising alternative to metamodeling, enhances the efficiency of VA portfolio assessments [...] Read more.

Given the computational challenges associated with valuing large variable annuity (VA) portfolios, a variety of data mining frameworks, including metamodeling and active learning, have been proposed in recent years. Active learning, a promising alternative to metamodeling, enhances the efficiency of VA portfolio assessments by adaptively improving a predictive regression model. This is achieved by augmenting data for model training with strategically selected informative samples. Successful application of active learning requires an effective metric in order to gauge the informativeness of data. Current sampling methods, which focus on prediction error-based informativeness, typically rely solely on prediction variance and assume an unbiased predictive model. In this paper, we address the fact that prediction bias can be nonnegligible in large VA portfolio valuation and investigate the impact of prediction bias in both the modeling and sampling stages of active learning. Our experimental results suggest that bias-based sampling can rival the efficacy of traditional ambiguity-based sampling, with its success contingent upon the extent of bias present in the predictive model. Full article

(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)

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10 pages, 2477 KiB

Open AccessArticle

Multi-Timescale Recurrent Neural Networks Beat Rough Volatility for Intraday Volatility Prediction

by Damien Challet and Vincent Ragel

Risks 2024, 12(6), 84; https://doi.org/10.3390/risks12060084 - 22 May 2024

Viewed by 431

Abstract

We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or highly disparate timescales. We compare the ability of vanilla and extended long short-term memory [...] Read more.

We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or highly disparate timescales. We compare the ability of vanilla and extended long short-term memory networks (LSTMs) to predict the intraday volatility of a collection of equity indices known to have a long memory. Generally, the number of epochs needed to train the extended LSTMs is divided by about two, while the variation in validation and test losses among models with the same hyperparameters is much smaller. We also show that the single model with the smallest validation loss systemically outperforms rough volatility predictions for the average intraday volatility of equity indices by about 20% when trained and tested on a dataset with multiple time series. Full article

(This article belongs to the Special Issue Advances in Volatility Modeling and Risk in Markets)

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Risks, Volume 12, Issue 6 (June 2024) – 13 articles

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