Risks

Open AccessArticle

Do Behavioral Biases Affect Investors’ Investment Decision Making? Evidence from the Pakistani Equity Market

by

,

,

and

Risks 2023, 11(6), 109; https://doi.org/10.3390/risks11060109 - 06 Jun 2023

Abstract

Using a unique sample constructed by 600 investors’ responses to a structured questionnaire, we investigate the impact of behavioral biases on the investors’ investment decision making in the Pakistani equity market, as well as the roles that market anomalies and financial literacy play [...] Read more.

Using a unique sample constructed by 600 investors’ responses to a structured questionnaire, we investigate the impact of behavioral biases on the investors’ investment decision making in the Pakistani equity market, as well as the roles that market anomalies and financial literacy play in the decision making process. We first document the empirical evidence to support that the behavioral biases and market anomalies are closely associated and that these two factors significantly influence the investors’ investment decision making. The additional analyses confirm the mediating roles of certain market anomalies in the association between the investors’ behavioral biases and their investment decision making. Furthermore, empirical evidence reveals that financial literacy moderates the association between behavioral biases and market anomalies, and eventually influences the investors’ investment decision making. Overall, although the results are inconclusive for the relationships between certain variables, our results highlight the importance of financial literacy in terms of optimal investment decision making of individuals and the stability of the overall stock market. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance and Risk Management)

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Open AccessArticle

The Explanatory Factors of Risk Disclosure in the Integrated Reports of Listed Entities in Brazil

by

Fabio Albuquerque

,

Eveline Monteiro

and

Maria Albertina Barreiro Rodrigues

Risks 2023, 11(6), 108; https://doi.org/10.3390/risks11060108 - 05 Jun 2023

Abstract

The gaps observed in entities’ traditional reports and accounts led to the emergence of the integrated report (IR), which includes several content elements, namely the component relating to risks and opportunities. Within this scope, the specific risks that may affect an organization’s capacity [...] Read more.

The gaps observed in entities’ traditional reports and accounts led to the emergence of the integrated report (IR), which includes several content elements, namely the component relating to risks and opportunities. Within this scope, the specific risks that may affect an organization’s capacity to create value are disclosed, among others, which is information of interest to the different stakeholders. This paper aims to identify the explanatory factors that influence the disclosure of risks in IRs. For this purpose, the IRs of entities listed on the Brazilian stock exchange for the year 2020 were assessed. The study was based on the explanatory theories of risk disclosure usually found in the literature, namely, the legitimacy, the agency, the signaling, and the upper echelon theories. Linear regression models were used with the disclosure rates of different types of risk as dependent variables. The size, profitability, indebtedness, independence, and gender diversity in the board of directors (BD), audit, and activity sector comprised the selected explanatory factors. Associations were found between some of the types of risks disclosed and the size of the entity, the existence of an audit, the independence of the BD, and the activity sector. The paper contributes to the literature about the explanatory factors of risk disclosure by exploring its analysis with different typologies and attributes, having the IR as a source of information, which is still little explored. The scientific contribution encompasses proposing a new risk analysis model in the IR. The innovative elements also comprise the classification of risks related to sustainable development (SD), including environmental, social, and governance (ESG) factors. Full article

Open AccessArticle

A Guaranteed-Return Structured Product as an Investment Risk-Hedging Instrument in Pension Savings Plans

by

Zvika Afik

,

Elroi Hadad

and

Rami Yosef

Risks 2023, 11(6), 107; https://doi.org/10.3390/risks11060107 - 05 Jun 2023

Abstract

This study proposes a structured product (SP) for hedging defined contribution pension fund members against capital market risk. Using Monte Carlo simulations on three different guaranteed returns to test the investment strategy of the SP against a balanced investment portfolio, we measure their [...] Read more.

This study proposes a structured product (SP) for hedging defined contribution pension fund members against capital market risk. Using Monte Carlo simulations on three different guaranteed returns to test the investment strategy of the SP against a balanced investment portfolio, we measure their performance across a wide variety of capital market returns and risk scenarios. The results show that the SP guarantees a minimal return on the pension savings portfolio and offers a higher portfolio return at a lower investment risk, compared with the balanced investment portfolio. We conclude that the SP may become popular among pension fund members, potentially leading to improved risk management, greater competition, and investment strategy innovations for defined contribution pension schemes. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance and Risk Management)

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Open AccessArticle

Big Data Analytics to Support Open Innovation Strategies in Banks

by

Tasya Aspiranti

,

Qaisar Ali

and

Ima Amaliah

Risks 2023, 11(6), 106; https://doi.org/10.3390/risks11060106 - 05 Jun 2023

Abstract

Today’s dynamic business environment has pushed service-oriented firms such as banks to collaborate with external partners through open innovation (OI) to address issues of service differentiation, optimize customer experience, and create effective open innovation strategies (OIS). However, the essential elements required to design [...] Read more.

Today’s dynamic business environment has pushed service-oriented firms such as banks to collaborate with external partners through open innovation (OI) to address issues of service differentiation, optimize customer experience, and create effective open innovation strategies (OIS). However, the essential elements required to design OIS and the methods to manage these strategies are missing. Therefore, this study aims to investigate the strategic resources essential to creating OIS and identify the tools to manage these resources. Following the fundamentals of the resource-based view (RBV), bank openness (BOP), selection of external partners (SEP), open innovation methods (OIM), formalizing collaboration processes (FCP), and banks’ internal practices (BIP) are identified as the strategic elements required for creating OIS, and the role of big data analytics (BDA) in these strategic resources is examined. The data were collected through a survey questionnaire from 425 bank executives employed at different digital banks located in Malaysia. To achieve our research objectives, a quantitative deductive research design was employed and the collected data were processed in WarPLS using the structural equation modeling (SEM) technique to test the research hypotheses of this study. The empirical results reveal that BDA has a significant positive impact on BOP, SEP, and FCP, whereas OIM and BIP have an insignificant positive impact. The findings of this study contribute to designing a robust digital strategy to enhance the banking sector’s contribution to the development of financial industries in developing countries by employing BDA as a major strategic policy tool of OIS Full article

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Open AccessArticle

Constant or Variable? A Performance Analysis among Portfolio Insurance Strategies

by

Daniele Mancinelli

and

Immacolata Oliva

Risks 2023, 11(6), 105; https://doi.org/10.3390/risks11060105 - 02 Jun 2023

Abstract

In this paper, we propose a comparison among three portfolio insurance strategies, namely the constant proportion portfolio insurance, the time-invariant portfolio protection, and the exponential proportion portfolio insurance, via an in-depth performance analysis. We aim to ascertain whether strategies characterized by variable parameters [...] Read more.

In this paper, we propose a comparison among three portfolio insurance strategies, namely the constant proportion portfolio insurance, the time-invariant portfolio protection, and the exponential proportion portfolio insurance, via an in-depth performance analysis. We aim to ascertain whether strategies characterized by variable parameters can outperform those with constant parameters by measuring potential returns, investment riskiness, downside protection capability, and ability to capture market upside. The results, achieved in a model-free framework by exploiting bootstrapping techniques, advise that no winning strategy exists overall, even when considering different volatility regimes, rebalancing frequencies, and protection levels. Full article

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Open AccessArticle

Regulation and De-Risking: Theoretical and Empirical Insights

by

Lawrence Haar

and

Andros Gregoriou

Risks 2023, 11(6), 104; https://doi.org/10.3390/risks11060104 - 02 Jun 2023

Abstract

The purpose of the Bank for International Settlements regulatory agenda, as implemented by financial regulators globally, has been to make banks safer and reduce the likelihood of systemic events. Using an original model of bank profit maximisation under a regulatory constraint, we statistically [...] Read more.

The purpose of the Bank for International Settlements regulatory agenda, as implemented by financial regulators globally, has been to make banks safer and reduce the likelihood of systemic events. Using an original model of bank profit maximisation under a regulatory constraint, we statistically examine how market risk exposure has interacted with financial performance and capital structure, to see if the Basel regulatory agenda concerning the quantity, quality and liquidity of capital, has prompted changes in banking behaviour as measured by exposure to market risk. Breaking new ground, we empirically explore how the regulatory agenda has affected the largest banks. We analyse if the regulatory agenda has succeeded in aligning the cost of capital with their exposure to market risk, measured by Value at Risk; or if regulations have induced changes to banking activities. We find rather than regulation inducing changes to the rate at which unchanged risk exposure is capitalised; it leads to changes in the nature of exposures. Risk has declined along with financial performance while the cost of capital is largely unchanged. A consequence of regulation may be to encourage the migration of riskier activities to organisations where it may be borne more cheaply. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

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Open AccessArticle

On Valuation and Investments of Pension Plans in Discrete Incomplete Markets

by

Michail Anthropelos

and

Evmorfia Blontzou

Risks 2023, 11(6), 103; https://doi.org/10.3390/risks11060103 - 01 Jun 2023

Abstract

We study the valuation of a pension fund’s obligations in a discrete time and space incomplete market model. The market’s incompleteness stems from the non-replicability of the wage process that finances the pension plan through time. The contingent defined-benefit liability of the pension [...] Read more.

We study the valuation of a pension fund’s obligations in a discrete time and space incomplete market model. The market’s incompleteness stems from the non-replicability of the wage process that finances the pension plan through time. The contingent defined-benefit liability of the pension fund is a function of the wages, which can be seen as the payoff of a path-dependent derivative security. We apply the notion of the super-hedging value and propose its difference from the current pension’s fund capital as a measure of distance to liability hedging. The induced closed-form expressions of the values and the related investment strategies provide insightful comparative statistics. Furthermore, we use a utility-based optimization portfolio to point out that in cases of sufficient capital, the application of a subjective investment criterion may result in heavily different strategies than the super-hedging one. This means that the pension fund will be left with some liability risk, although it could have been fully hedged. Finally, we provide conditions under which the effect of a possible early exit leaves the super-hedging valuation unchanged. Full article

(This article belongs to the Special Issue Frontiers in Quantitative Finance and Risk Management)

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Open AccessArticle

The Relationship between Capital Structure and Firm Performance: The Moderating Role of Agency Cost

by

Amanj Mohamed Ahmed

,

Deni Pandu Nugraha

and

István Hágen

Risks 2023, 11(6), 102; https://doi.org/10.3390/risks11060102 - 01 Jun 2023

Abstract

Since it first appeared, agency theory has argued that debt can decrease agency issues between agent and principal and enhance the value of firms. This paper explores the moderating effect of agency cost on the association between capital structure and firm performance. A [...] Read more.

Since it first appeared, agency theory has argued that debt can decrease agency issues between agent and principal and enhance the value of firms. This paper explores the moderating effect of agency cost on the association between capital structure and firm performance. A panel econometric method, namely a fixed-effect regression model, was used to evaluate the above description. This investigation uses secondary data collected from published annual reports of manufacturing firms listed on Tehran Stock Exchange (TSE) during 2011–2019. Empirical results show that capital structure is negatively related to firm performance. Agency cost also has a negative impact on corporate performance; however, in the case of ROA and EPS, the relationship is positive. Interestingly, the findings illustrate that increasing the level of debt can reduce agency costs and enhance firm performance. Moreover, robust correlations are revealing that agency cost significantly affects the relationship between capital structure and corporate performance. These findings provide proof to support the assumptions of agency theory, which explains the association between capital structure and performance of firms. This study provides new perspectives on the relationship between capital structure and firm performance by using data from listed manufacturing firms in Iran; hence, these new insights from a developing market improve the understanding of capital structure in Asian and Middle Eastern markets. Full article

Open AccessArticle

Context-Based and Adaptive Cybersecurity Risk Management Framework

by

Henock Mulugeta Melaku

Risks 2023, 11(6), 101; https://doi.org/10.3390/risks11060101 - 31 May 2023

Abstract

Currently, organizations are faced with a variety of cyber-threats and are possibly challenged by a wide range of cyber-attacks of varying frequency, complexity, and impact. However, they can do something to prevent, or at least mitigate, these cyber-attacks by first understanding and addressing [...] Read more.

Currently, organizations are faced with a variety of cyber-threats and are possibly challenged by a wide range of cyber-attacks of varying frequency, complexity, and impact. However, they can do something to prevent, or at least mitigate, these cyber-attacks by first understanding and addressing their common problems regarding cybersecurity culture, developing a cyber-risk management plan, and devising a more proactive and collaborative approach that is suitable according to their organization context. To this end, firstly various enterprise, Information Technology (IT), and cybersecurity risk management frameworks are thoroughly reviewed along with their advantages and limitations. Then, we propose a proactive cybersecurity risk management framework that is simple and dynamic, and that adapts according to the current threat and technology landscapes and organizational context. Finally, performance metrics to evaluate the framework are proposed. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

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Open AccessArticle

The Generalised Pareto Distribution Model Approach to Comparing Extreme Risk in the Exchange Rate Risk of BitCoin/US Dollar and South African Rand/US Dollar Returns

by

Thabani Ndlovu

and

Delson Chikobvu

Risks 2023, 11(6), 100; https://doi.org/10.3390/risks11060100 - 31 May 2023

Abstract

Cryptocurrencies are said to be very risky, and so are the currencies of emerging economies, including the South African rand. The steady rise in the movement of South Africans’ investments between the rand and BitCoin warrants an investigation as to which of the [...] Read more.

Cryptocurrencies are said to be very risky, and so are the currencies of emerging economies, including the South African rand. The steady rise in the movement of South Africans’ investments between the rand and BitCoin warrants an investigation as to which of the two currencies is riskier. In this paper, the Generalised Pareto Distribution (GPD) model is employed to estimate the Value at Risk (VaR) and the Expected Shortfall (ES) for the two exchange rates, BitCoin/US dollar (BitCoin) and the South African rand/US dollar (ZAR/USD). The estimated risk measures are used to compare the riskiness of the two exchange rates. The Maximum Likelihood Estimation (MLE) method is used to find the optimal parameters of the GPD model. The higher extreme value index estimate associated with the BTC/USD when compared with the ZAR/USD estimate, suggests that the BTC/USD is riskier than the ZAR/USD. The computed VaR estimates for losses of $0.07, $0.09, and $0.16 per dollar invested in the BTC/USD at 90%, 95%, and 99% compared to the ZAR/USD’s $0.02, $0.02, and $0.03 at the respective levels of significance, confirm that BitCoin is riskier than the rand. The ES (average losses) of $0.11, $0.13, and $0.21 per dollar invested in the BTC/USD at 90%, 95%, and 99% compared to the ZAR/USD’s $0.02, $0.02, and $0.03 at the respective levels of significance further confirm the higher risk associated with BitCoin. Model adequacy is confirmed using the Kupiec test procedure. These findings are helpful to risk managers when making adequate risk-based capital requirements more rational between the two currencies. The argument is for more capital requirements for BitCoin than for the South African rand. Full article

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Open AccessArticle

Estimating Territory Risk Relativity Using Generalized Linear Mixed Models and Fuzzy C-Means Clustering

by

Shengkun Xie

and

Chong Gan

Risks 2023, 11(6), 99; https://doi.org/10.3390/risks11060099 - 24 May 2023

Abstract

Territory risk analysis has played an important role in auto insurance rate regulation. It aims to design rating territories from a set of basic rating units so that their respective risk relativities can be estimated to reflect the regional risk of insurance. In [...] Read more.

Territory risk analysis has played an important role in auto insurance rate regulation. It aims to design rating territories from a set of basic rating units so that their respective risk relativities can be estimated to reflect the regional risk of insurance. In this work, spatially constrained clustering is first applied to insurance loss data to form such regions, using the forward sortation area (FSA) as a basic rating unit. The groupings of FSA by spatially constrained clustering reduce the insurance rate heterogeneity caused by smaller risk exposures. Furthermore, the generalized linear mixed model (GLMM) is proposed to derive the risk relativities of clusters and each FSA. In addition, as an alternative approach, fuzzy C-Means clustering is proposed to derive the risk relativity of FSA, and the obtained results are compared to the ones from GLMM. The spatially constrained clustering and risk relativity estimation help to retrieve a set of territory risk benchmarks used in rate filings within the regulation process. It also provides guidance for auto insurance companies on rate making. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Non-Life Insurance: Theory, Methods and Applications)

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Open AccessFeature PaperReview

How to Gain Confidence in the Results of Internal Risk Models? Approaches and Techniques for Validation

by

Michel Dacorogna

Risks 2023, 11(5), 98; https://doi.org/10.3390/risks11050098 - 18 May 2023

Abstract

The development of risk models for managing portfolios of financial institutions and insurance companies requires, both from the regulatory and management points of view, a strong validation of the quality of the results provided by internal risk models. In Solvency II, for instance, [...] Read more.

The development of risk models for managing portfolios of financial institutions and insurance companies requires, both from the regulatory and management points of view, a strong validation of the quality of the results provided by internal risk models. In Solvency II, for instance, regulators ask for independent validation reports from companies who apply for the approval of their internal models. We analyze here various ways to enable management and regulators to gain confidence in the quality of models. It all starts by ensuring a good calibration of the risk models and the dependencies between the various risk drivers. Then, by applying stress tests to the model and various empirical analyses, in particular the probability integral transform, we can build a full and credible framework to validate risk models. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

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Open AccessArticle

Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector

by

Rasa Kanapickienė

,

Tomas Kanapickas

and

Audrius Nečiūnas

Risks 2023, 11(5), 97; https://doi.org/10.3390/risks11050097 - 18 May 2023

Abstract

Credit-risk models that are designed for general application across sectors may not be suitable for the construction industry, which has unique characteristics and financial risks that require specialised modelling approaches. Moreover, advanced bankruptcy-prediction models are often used to achieve the highest accuracy in [...] Read more.

Credit-risk models that are designed for general application across sectors may not be suitable for the construction industry, which has unique characteristics and financial risks that require specialised modelling approaches. Moreover, advanced bankruptcy-prediction models are often used to achieve the highest accuracy in large modern datasets. Therefore, the aim of this research is the creation of enterprise-bankruptcy prediction (EBP) models for Lithuanian micro and small enterprises (MiSEs) in the construction sector. This issue is analysed based on classification models and the specific types of variable used. Firstly, four types of variable are proposed. In EBP models, financial variables substantially explain an enterprise’s financial statements and performance from different perspectives. Including enterprises’ non-financial, construction-sector and macroeconomic variables improves the characteristics of EBP models. The inclusion of macroeconomic variables in the model has a particularly significant impact. These findings can be of great significance to investors, creditors, policymakers and practitioners in assessing financial risks and making informed decisions. The second question is related to the classification models used. To develop the EBP models, logistic regression (LR), artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) were used. In addition, this study developed two-stage hybrid models, i.e., the LR is combined with ANNs. The findings show that two-stage hybrid models do not improve bankruptcy prediction. It cannot be argued that ANN models are more accurate in predicting bankruptcy. The MARS model demonstrates the best bankruptcy prediction, i.e., this model could be a valuable tool for stakeholders to evaluate enterprises’ financial risk. Full article

(This article belongs to the Special Issue Credit Risk Management: Volume II)

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Open AccessArticle

A Non-Performing Loans (NPLs) Portfolio Pricing Model Based on Recovery Performance: The Case of Greece

by

Alexandra Z. Marouli

,

Eugenia N. Giannini

and

Yannis D. Caloghirou

Risks 2023, 11(5), 96; https://doi.org/10.3390/risks11050096 - 18 May 2023

Abstract

In this paper, a method was proposed for pricing NPL portfolios, which is currently a crucial point in the portfolio transactions between the banks and NPL servicers. The method was based on a simple mathematical model which simulated the collection process of the [...] Read more.

In this paper, a method was proposed for pricing NPL portfolios, which is currently a crucial point in the portfolio transactions between the banks and NPL servicers. The method was based on a simple mathematical model which simulated the collection process of the NPL portfolios considering the debtors’ behavioral response to various legal measures (phone calls, extrajudicial notices, court orders, and foreclosures). The model considered the recovery distribution over time and was applied successfully to the case of Greece. The model was also used to predict recovery, cost, and profit future cash flows, and to optimize the collection strategies related to the activation periods of different measures. A sensitivity analysis was also conducted to reveal the most significant factors affecting the collection process. Full article

(This article belongs to the Special Issue Credit Risk Management: Volume II)

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Open AccessArticle

Prospect Theory and the Favorite Long-Shot Bias in Baseball

by

James Nutaro

Risks 2023, 11(5), 95; https://doi.org/10.3390/risks11050095 - 17 May 2023

Abstract

We provide new evidence of a favorite long-shot bias for bets placed on baseball games. Our analysis uses the difference of mean run differentials as an observable proxy for the probability of a team to win. When baseball is viewed through this proxy, [...] Read more.

We provide new evidence of a favorite long-shot bias for bets placed on baseball games. Our analysis uses the difference of mean run differentials as an observable proxy for the probability of a team to win. When baseball is viewed through this proxy, we see that bettors believe favorites are less likely to win than they actually are and long-shots more likely. This result is consistent with prospect theory, which suggests that large and small probabilities are poorly estimated when making decisions with risk. Full article

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Open AccessArticle

COVID-19 Media Chatter and Macroeconomic Reflectors on Black Swan: A Spanish and Indian Stock Markets Comparison

by

Indranil Ghosh

,

Esteban Alfaro-Cortés

,

Matías Gámez

and

Noelia García-Rubio

Risks 2023, 11(5), 94; https://doi.org/10.3390/risks11050094 - 16 May 2023

Abstract

Predictive analytics of financial markets in developed and emerging economies during the COVID-19 regime is undeniably challenging due to unavoidable uncertainty and the profound proliferation of negative news on different platforms. Tracking the media echo is crucial to explaining and anticipating the abrupt [...] Read more.

Predictive analytics of financial markets in developed and emerging economies during the COVID-19 regime is undeniably challenging due to unavoidable uncertainty and the profound proliferation of negative news on different platforms. Tracking the media echo is crucial to explaining and anticipating the abrupt fluctuations in financial markets. The present research attempts to propound a robust framework capable of channeling macroeconomic reflectors and essential media chatter-linked variables to draw precise forecasts of future figures for Spanish and Indian stock markets. The predictive structure combines Isometric Mapping (ISOMAP), which is a non-linear feature transformation tool, and Gradient Boosting Regression (GBR), which is an ensemble machine learning technique to perform predictive modelling. The Explainable Artificial Intelligence (XAI) is used to interpret the black-box type predictive model to infer meaningful insights. The overall results duly justify the incorporation of local and global media chatter indices in explaining the dynamics of respective financial markets. The findings imply marginally better predictability of Indian stock markets than their Spanish counterparts. The current work strives to compare and contrast the reaction of developed and developing financial markets during the COVID-19 pandemic, which has been argued to share a close resemblance to the Black Swan event when applying a robust research framework. The insights linked to the dependence of stock markets on macroeconomic indicators can be leveraged for policy formulations for augmenting household finance. Full article

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Open AccessArticle

A Diversification Framework for Multiple Pairs Trading Strategies

by

Kiseop Lee

,

Tim Leung

and

Boming Ning

Risks 2023, 11(5), 93; https://doi.org/10.3390/risks11050093 - 16 May 2023

Abstract

We propose a framework for constructing diversified portfolios with multiple pairs trading strategies. In our approach, several pairs of co-moving assets are traded simultaneously, and capital is dynamically allocated among different pairs based on the statistical characteristics of the historical spreads. This allows [...] Read more.

We propose a framework for constructing diversified portfolios with multiple pairs trading strategies. In our approach, several pairs of co-moving assets are traded simultaneously, and capital is dynamically allocated among different pairs based on the statistical characteristics of the historical spreads. This allows us to further consider various portfolio designs and rebalancing strategies. Working with empirical data, our experiments suggest the significant benefits of diversification within our proposed framework. Full article

(This article belongs to the Special Issue Emerging Topics in Finance and Risk Engineering—In Memory of Peter Carr)

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Open AccessArticle

Risk Mitigation in Agriculture in Support of COVID-19 Crisis Management

by

Boris M. Leybert

,

Oksana V. Shmaliy

,

Zhanna V. Gornostaeva

and

Daria D. Mironova

Risks 2023, 11(5), 92; https://doi.org/10.3390/risks11050092 - 15 May 2023

Abstract

The main focus of this article is the problem of exacerbating agricultural risks in the context of the COVID-19 crisis, which started against the background of the novel coronavirus (COVID-19) pandemic. The motivation for conducting the research presented in this article was the [...] Read more.

The main focus of this article is the problem of exacerbating agricultural risks in the context of the COVID-19 crisis, which started against the background of the novel coronavirus (COVID-19) pandemic. The motivation for conducting the research presented in this article was the desire to increase the resilience of agricultural companies to economic crises. This paper is aimed at studying the Russian experience of changing the production and financial risks of agricultural companies during the COVID-19 crisis, substantiating the important role of innovations in reducing these risks, and determining the prospects for risk management in agriculture based on innovations to increase its crisis resilience. Using the structural equation modelling (SEM) method, we modelled the contribution of innovations to the risk management of agriculture during the COVID-19 crisis. The advantages of the SEM method, compared to other conventional methods (e.g., independent correlation analysis or independent regression analysis), include the increased depth of analysis, its systemic character, and the consideration of multilateral connections between the indicators. Using the case-study method, a “smart” vertical farm framework is being developed, the risks of which are resistant to crises through the use of datasets and machine learning. The originality of this article lies in rethinking the risks of agriculture from the standpoint of “smart” technologies as a new risk factor and a way to increase resilience to crises. The theoretical significance of the results obtained is that they make it possible to systematically study the changes in the risks of agriculture in the context of the COVID-19 crisis, while outlining the prospects for increasing resilience to crises based on optimising the use of “smart” technologies. The practical significance of the article is related to the fact that the authors’ conclusions and applied recommendations on the use of datasets and machine learning by agricultural companies can improve the efficiency of agricultural risk management and ensure successful COVID-19 crisis management by agricultural companies. Full article

(This article belongs to the Special Issue The COVID-19 Crisis: Datasets and Data Analysis to Reduce Risks)

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Open AccessReview

A Survey on AI Implementation in Finance, (Cyber) Insurance and Financial Controlling

by

Aleksandrina Aleksandrova

,

Valentina Ninova

and

Zhelyo Zhelev

Risks 2023, 11(5), 91; https://doi.org/10.3390/risks11050091 - 11 May 2023

Abstract

Artificial intelligence is changing the world in unprecedented ways and redefining all areas of human activity. In recent decades, the development of AI has progressed at an extraordinary pace. This study examines the scope of implementing AI in the financial sector, insurance, and [...] Read more.

Artificial intelligence is changing the world in unprecedented ways and redefining all areas of human activity. In recent decades, the development of AI has progressed at an extraordinary pace. This study examines the scope of implementing AI in the financial sector, insurance, and financial controlling. The research team focuses on these areas, as the main objective of this review is to provide a comprehensive walk-through and to fill the gaps in the literature related to AI implementation in finance, insurance, and financial control from an economic perspective. We provide a comprehensive overview of AI implementation in finance, insurance, and financial controlling, highlighting crucial issues in that process and identifying the relationship between the development of these economic sectors and AI. The authors’ team identifies the trends and main themes in the existing literature in AI-related publications in finance, insurance, and financial control. We discuss the main advantages and disadvantages of AI implementation, identified by our research, and also make some suggestions regarding future research having in mind the interdisciplinary of the topic, the vast development of AI and technologies, and the increasing demand for AI-based solutions, services and products. Full article

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