Financial risk aversion and financial risk tolerance are sometimes considered to be ‘opposite sides of the same coin’, with the implication being that risk aversion (a term describing the unwillingness of an investor to take risks based on a probability assessment) and risk tolerance (an investor’s willingness to engage in a behavior based on their subjective evaluation of the uncertainty of the outcomes) are inversely-related substitutes. The purpose of this paper is to present an alternative way of viewing these constructs. We show that risk aversion and risk tolerance act as complementary factors in models designed to describe the degree of risk observed in household investment portfolios. A series of multivariate tests were used to determine that financial risk aversion is inversely related to portfolio risk, whereas financial risk tolerance is positively associated with portfolio risk. When used in the same model, the amount of explained variance in portfolio risk was increased compared to models where one, but not the other, measure was used. Overall, financial risk tolerance exhibited the largest model effect, although financial risk aversion was also important across the models analyzed in this study.
{"title":"The Complementary Nature of Financial Risk Aversion and Financial Risk Tolerance","authors":"John Grable, Abed Rabbani, Wookjae Heo","doi":"10.3390/risks12070109","DOIUrl":"https://doi.org/10.3390/risks12070109","url":null,"abstract":"Financial risk aversion and financial risk tolerance are sometimes considered to be ‘opposite sides of the same coin’, with the implication being that risk aversion (a term describing the unwillingness of an investor to take risks based on a probability assessment) and risk tolerance (an investor’s willingness to engage in a behavior based on their subjective evaluation of the uncertainty of the outcomes) are inversely-related substitutes. The purpose of this paper is to present an alternative way of viewing these constructs. We show that risk aversion and risk tolerance act as complementary factors in models designed to describe the degree of risk observed in household investment portfolios. A series of multivariate tests were used to determine that financial risk aversion is inversely related to portfolio risk, whereas financial risk tolerance is positively associated with portfolio risk. When used in the same model, the amount of explained variance in portfolio risk was increased compared to models where one, but not the other, measure was used. Overall, financial risk tolerance exhibited the largest model effect, although financial risk aversion was also important across the models analyzed in this study.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"20 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research delves into the fusion of spatial clustering and predictive modeling within auto insurance data analytics. The primary focus of this research is on addressing challenges stemming from the dynamic nature of spatial patterns in multiple accident year claim data, by using spatially constrained clustering. The spatially constrained clustering is implemented under hierarchical clustering with a soft contiguity constraint. It is highly desirable for insurance companies and insurance regulators to be able to make meaningful comparisons of loss patterns obtained from multiple reporting years that summarize multiple accident year loss metrics. By integrating spatial clustering techniques, the study not only improves the credibility of predictive models but also introduces a strategic dimension reduction method that concurrently enhances the interpretability of predictive models used. The evolving nature of spatial patterns over time poses a significant barrier to a better understanding of complex insurance systems as these patterns transform due to various factors. While spatial clustering effectively identifies regions with similar loss data characteristics, maintaining up-to-date clusters is an ongoing challenge. This research underscores the importance of studying spatial patterns of auto insurance claim data across major insurance coverage types, including Accident Benefits (AB), Collision (CL), and Third-Party Liability (TPL). The research offers regulators valuable insights into distinct risk profiles associated with different coverage categories and territories. By leveraging spatial loss data from pre-pandemic and pandemic periods, this study also aims to uncover the impact of the COVID-19 pandemic on auto insurance claims of major coverage types. From this perspective, we observe a statistically significant increase in insurance premiums for CL coverage after the pandemic. The proposed unified spatial clustering method incorporates a relabeling strategy to standardize comparisons across different accident years, contributing to a more robust understanding of the pandemic effects on auto insurance claims. This innovative approach has the potential to significantly influence data visualization and pattern recognition, thereby improving the reliability and interpretability of clustering methods.
{"title":"Unified Spatial Clustering of Territory Risk to Uncover Impact of COVID-19 Pandemic on Major Coverages of Auto Insurance","authors":"Shengkun Xie, Nathaniel Ho","doi":"10.3390/risks12070108","DOIUrl":"https://doi.org/10.3390/risks12070108","url":null,"abstract":"This research delves into the fusion of spatial clustering and predictive modeling within auto insurance data analytics. The primary focus of this research is on addressing challenges stemming from the dynamic nature of spatial patterns in multiple accident year claim data, by using spatially constrained clustering. The spatially constrained clustering is implemented under hierarchical clustering with a soft contiguity constraint. It is highly desirable for insurance companies and insurance regulators to be able to make meaningful comparisons of loss patterns obtained from multiple reporting years that summarize multiple accident year loss metrics. By integrating spatial clustering techniques, the study not only improves the credibility of predictive models but also introduces a strategic dimension reduction method that concurrently enhances the interpretability of predictive models used. The evolving nature of spatial patterns over time poses a significant barrier to a better understanding of complex insurance systems as these patterns transform due to various factors. While spatial clustering effectively identifies regions with similar loss data characteristics, maintaining up-to-date clusters is an ongoing challenge. This research underscores the importance of studying spatial patterns of auto insurance claim data across major insurance coverage types, including Accident Benefits (AB), Collision (CL), and Third-Party Liability (TPL). The research offers regulators valuable insights into distinct risk profiles associated with different coverage categories and territories. By leveraging spatial loss data from pre-pandemic and pandemic periods, this study also aims to uncover the impact of the COVID-19 pandemic on auto insurance claims of major coverage types. From this perspective, we observe a statistically significant increase in insurance premiums for CL coverage after the pandemic. The proposed unified spatial clustering method incorporates a relabeling strategy to standardize comparisons across different accident years, contributing to a more robust understanding of the pandemic effects on auto insurance claims. This innovative approach has the potential to significantly influence data visualization and pattern recognition, thereby improving the reliability and interpretability of clustering methods.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"28 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange context, with the examples of the EUR/USD futures and USD/JPY futures. The results of the EGARCH (1,1) model show that the introduction of foreign exchange futures decreases spot volatility. It also increases the rate at which new information is impounded into spot prices but decreases the persistency of volatility shocks. A positive effect of unexpected trading volume and a negative effect of unexpected open interest on contemporaneous spot volatility are in line with the VAR(1) model results of the dynamic relationship between spot volatility and foreign exchange futures trading activity. With the impact on spot volatility caused by unexpected open interest rate being stronger than by unexpected trading volume, foreign exchange futures trading stabilizes spot volatility.
{"title":"Foreign Exchange Futures Trading and Spot Market Volatility in Thailand","authors":"Woradee Jongadsayakul","doi":"10.3390/risks12070107","DOIUrl":"https://doi.org/10.3390/risks12070107","url":null,"abstract":"This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange context, with the examples of the EUR/USD futures and USD/JPY futures. The results of the EGARCH (1,1) model show that the introduction of foreign exchange futures decreases spot volatility. It also increases the rate at which new information is impounded into spot prices but decreases the persistency of volatility shocks. A positive effect of unexpected trading volume and a negative effect of unexpected open interest on contemporaneous spot volatility are in line with the VAR(1) model results of the dynamic relationship between spot volatility and foreign exchange futures trading activity. With the impact on spot volatility caused by unexpected open interest rate being stronger than by unexpected trading volume, foreign exchange futures trading stabilizes spot volatility.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"68 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data.
{"title":"Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets","authors":"Viviana Fanelli","doi":"10.3390/risks12070106","DOIUrl":"https://doi.org/10.3390/risks12070106","url":null,"abstract":"In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"26 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The relationship between intellectual capital and firm performance represents a critical facet of corporate governance, warranting comprehensive investigation. By analyzing data from 1,151 non-financial firms listed on the Indonesia Stock Exchange over the period from 2018 to 2022, the authors utilize fixed effect regression analysis to test their hypothesis. This study’s findings reveal a positive and significant relationship between intellectual capital and firm performance. Additionally, the interaction model incorporating political connections yields statistically significant results, indicating that political connections can moderate the relationship between intellectual capital and firm performance. This study makes a substantial contribution to the literature, particularly by advancing the understanding of corporate governance through the lens of intellectual capital’s influence on firm performance. It offers both theoretical and practical insights into the Indonesian context, highlighting the moderating role of political connections. Notably, this study is the first to incorporate interaction models to assess the impact of political connections on this relationship.
{"title":"Intellectual Capital, Political Connection, and Firm Performance: Exploring from Indonesia","authors":"Suham Cahyono, Ardianto Ardianto","doi":"10.3390/risks12070105","DOIUrl":"https://doi.org/10.3390/risks12070105","url":null,"abstract":"The relationship between intellectual capital and firm performance represents a critical facet of corporate governance, warranting comprehensive investigation. By analyzing data from 1,151 non-financial firms listed on the Indonesia Stock Exchange over the period from 2018 to 2022, the authors utilize fixed effect regression analysis to test their hypothesis. This study’s findings reveal a positive and significant relationship between intellectual capital and firm performance. Additionally, the interaction model incorporating political connections yields statistically significant results, indicating that political connections can moderate the relationship between intellectual capital and firm performance. This study makes a substantial contribution to the literature, particularly by advancing the understanding of corporate governance through the lens of intellectual capital’s influence on firm performance. It offers both theoretical and practical insights into the Indonesian context, highlighting the moderating role of political connections. Notably, this study is the first to incorporate interaction models to assess the impact of political connections on this relationship.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"15 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the insurance sector, Zero-Inflated models are commonly used due to the unique nature of insurance data, which often contain both genuine zeros (meaning no claims made) and potential claims. Although active developments in modeling excess zero data have occurred, the use of Bayesian techniques for parameter estimation in Zero-Inflated Poisson models has not been widely explored. This research aims to introduce a new Bayesian approach for estimating the parameters of the Zero-Inflated Poisson model. The method involves employing Gamma and Beta prior distributions to derive closed formulas for Bayes estimators and predictive density. Additionally, we propose a data-driven approach for selecting hyper-parameter values that produce highly accurate Bayes estimates. Simulation studies confirm that, for small and moderate sample sizes, the Bayesian method outperforms the maximum likelihood (ML) method in terms of accuracy. To illustrate the ML and Bayesian methods proposed in the article, a real dataset is analyzed.
{"title":"Inference for the Parameters of a Zero-Inflated Poisson Predictive Model","authors":"Min Deng, Mostafa S. Aminzadeh, Banghee So","doi":"10.3390/risks12070104","DOIUrl":"https://doi.org/10.3390/risks12070104","url":null,"abstract":"In the insurance sector, Zero-Inflated models are commonly used due to the unique nature of insurance data, which often contain both genuine zeros (meaning no claims made) and potential claims. Although active developments in modeling excess zero data have occurred, the use of Bayesian techniques for parameter estimation in Zero-Inflated Poisson models has not been widely explored. This research aims to introduce a new Bayesian approach for estimating the parameters of the Zero-Inflated Poisson model. The method involves employing Gamma and Beta prior distributions to derive closed formulas for Bayes estimators and predictive density. Additionally, we propose a data-driven approach for selecting hyper-parameter values that produce highly accurate Bayes estimates. Simulation studies confirm that, for small and moderate sample sizes, the Bayesian method outperforms the maximum likelihood (ML) method in terms of accuracy. To illustrate the ML and Bayesian methods proposed in the article, a real dataset is analyzed.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"4 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The objective of this paper is to pursue an intuitive idea: for a consumer who represents an “unfavorable” health risk but an “excellent risk” as a driver, a multi-peril policy could be associated with a reduced selection effort on the part of the insurer. If this intuition should be confirmed, it will serve to address the decade-long concern with risk selection both in the economic literature and on the part of policy makers. As an illustrative example, a two-peril model is developed in which consumers deploy effort in search of a policy offering them maximum coverage at the current market price while insurers deploy effort designed to stave off unfavorable risks. Two types of Nash equilibria are compared: one in which the insurer is confronted with high-risk and low-risk types, and another one where both types are a “better risk” with regard to a second peril. The difference in the insurer’s selection effort directed at high-risk and low-risk types is indeed shown to be lower in the latter case, resulting in a mitigation of adverse selection.
{"title":"Can Multi-Peril Insurance Policies Mitigate Adverse Selection?","authors":"Peter Zweifel, Annette Hofmann","doi":"10.3390/risks12060102","DOIUrl":"https://doi.org/10.3390/risks12060102","url":null,"abstract":"The objective of this paper is to pursue an intuitive idea: for a consumer who represents an “unfavorable” health risk but an “excellent risk” as a driver, a multi-peril policy could be associated with a reduced selection effort on the part of the insurer. If this intuition should be confirmed, it will serve to address the decade-long concern with risk selection both in the economic literature and on the part of policy makers. As an illustrative example, a two-peril model is developed in which consumers deploy effort in search of a policy offering them maximum coverage at the current market price while insurers deploy effort designed to stave off unfavorable risks. Two types of Nash equilibria are compared: one in which the insurer is confronted with high-risk and low-risk types, and another one where both types are a “better risk” with regard to a second peril. The difference in the insurer’s selection effort directed at high-risk and low-risk types is indeed shown to be lower in the latter case, resulting in a mitigation of adverse selection.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"59 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhanna V. Gornostaeva, Larisa V. Shabaltina, Igor V. Denisov, Aleksandra A. Musatkina, Nikolai G. Sinyavskiy
The purpose of this paper was to reveal the influence of the support of the sustainable development goals (SDGs) on the financial risks of responsible universities in Russia. This paper fills the gap in the literature that exists regarding the unknown consequences of SDGs’ support by responsible Russian universities concerning their financial risks. Based on the experience of the top 30 most responsible Russian universities in 2023, we used regression analysis to compile a model for their financial risk management. This model mathematically describes the cause-and-effect relationships of financial risk management in responsible Russian universities. This paper offers a new approach to financial risk management in responsible Russian universities. In it, financial risks to Russian universities are reduced due to universities accepting responsibility for state and private investors. A feature of the new approach is that the effective use of university funds is ensured not by cost savings but by the support of the SDGs. The potential for a reduction in financial risk in responsible universities in Russia through alternative approaches to financial risk management was disclosed. The proposed new approach can potentially raise (to a large extent) the aggregate incomes of responsible universities in Russia compared to the existing approach. The main conclusion is that the existing approach to financial risk management in Russian universities is based on low-efficiency managerial measures which risk burdening universities. This burden could be prevented with the newly developed approach to financial risk management in responsible universities in Russia through support of the SDGs. The theoretical significance lies in clarifying the specific list of the SDGs whose support makes the largest contribution to reducing financial risks for the universities—namely, SDG 4, SDG 8, and SDG 9. The practical significance is that the new approach will allow for full disclosure of the potential reduction in financial risks in responsible universities in Russia in the Decade of Action (2020–2030). The managerial significance is as follows: the proposed recommendations will allow improved financial risk management in Russian universities through optimization of the support of the SDGs.
{"title":"Support of the SDGs as a New Approach to Financial Risk Management in Responsible Universities in Russia","authors":"Zhanna V. Gornostaeva, Larisa V. Shabaltina, Igor V. Denisov, Aleksandra A. Musatkina, Nikolai G. Sinyavskiy","doi":"10.3390/risks12060101","DOIUrl":"https://doi.org/10.3390/risks12060101","url":null,"abstract":"The purpose of this paper was to reveal the influence of the support of the sustainable development goals (SDGs) on the financial risks of responsible universities in Russia. This paper fills the gap in the literature that exists regarding the unknown consequences of SDGs’ support by responsible Russian universities concerning their financial risks. Based on the experience of the top 30 most responsible Russian universities in 2023, we used regression analysis to compile a model for their financial risk management. This model mathematically describes the cause-and-effect relationships of financial risk management in responsible Russian universities. This paper offers a new approach to financial risk management in responsible Russian universities. In it, financial risks to Russian universities are reduced due to universities accepting responsibility for state and private investors. A feature of the new approach is that the effective use of university funds is ensured not by cost savings but by the support of the SDGs. The potential for a reduction in financial risk in responsible universities in Russia through alternative approaches to financial risk management was disclosed. The proposed new approach can potentially raise (to a large extent) the aggregate incomes of responsible universities in Russia compared to the existing approach. The main conclusion is that the existing approach to financial risk management in Russian universities is based on low-efficiency managerial measures which risk burdening universities. This burden could be prevented with the newly developed approach to financial risk management in responsible universities in Russia through support of the SDGs. The theoretical significance lies in clarifying the specific list of the SDGs whose support makes the largest contribution to reducing financial risks for the universities—namely, SDG 4, SDG 8, and SDG 9. The practical significance is that the new approach will allow for full disclosure of the potential reduction in financial risks in responsible universities in Russia in the Decade of Action (2020–2030). The managerial significance is as follows: the proposed recommendations will allow improved financial risk management in Russian universities through optimization of the support of the SDGs.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"24 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent decades, the importance of transport and logistics companies has increased considerably, especially for Lithuania, where this sector is on the rise and creating benefits for various users. Therefore, this study aims to analyse the economic–financial situation of transport and logistics companies operating in Lithuania, focusing mainly on their financial risk, probability of bankruptcy, and level of solvency. To achieve these results, 416 companies were analysed based on their data from 2022. The employed methodology included descriptive analysis, quartile ratio analysis, the use of Altman’s Z-score model to predict bankruptcy, and, finally, logistic regression analysis to answer the hypotheses. The results show that the companies analysed in this study were highly profitable, with a high level of solvency and liquidity that did not compromise their continuity in the market. These results were confirmed by the Z-score analysis. In addition, it was observed that the age and size of the companies did not affect their survival on the market. This study presents results that are of great interest for the academic literature, as well as for the management of logistics companies. The originality of the study lies in its relevance and timeliness, presenting robust results for different stakeholders, such as policymakers or new entrepreneurs, among others.
{"title":"The Economic and Financial Health of Lithuanian Logistics Companies","authors":"Rita Bužinskienė, Vera Gelashvili","doi":"10.3390/risks12060099","DOIUrl":"https://doi.org/10.3390/risks12060099","url":null,"abstract":"In recent decades, the importance of transport and logistics companies has increased considerably, especially for Lithuania, where this sector is on the rise and creating benefits for various users. Therefore, this study aims to analyse the economic–financial situation of transport and logistics companies operating in Lithuania, focusing mainly on their financial risk, probability of bankruptcy, and level of solvency. To achieve these results, 416 companies were analysed based on their data from 2022. The employed methodology included descriptive analysis, quartile ratio analysis, the use of Altman’s Z-score model to predict bankruptcy, and, finally, logistic regression analysis to answer the hypotheses. The results show that the companies analysed in this study were highly profitable, with a high level of solvency and liquidity that did not compromise their continuity in the market. These results were confirmed by the Z-score analysis. In addition, it was observed that the age and size of the companies did not affect their survival on the market. This study presents results that are of great interest for the academic literature, as well as for the management of logistics companies. The originality of the study lies in its relevance and timeliness, presenting robust results for different stakeholders, such as policymakers or new entrepreneurs, among others.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"16 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study analyzes the impact of knowledge capital (KC), a key element of firms’ innovation and competitiveness, on stock returns during economic crises when sustainable competitiveness becomes particularly important. We analyze the impact of the Global Financial Crisis and COVID-19 as economic crises, focusing on manufacturing industries with a high proportion of investment shifts from physical capital to KC. Our findings indicate that KC is positively associated with stock returns during the Global Financial Crisis and COVID-19. This positive relationship is strengthened by the firm’s ability to leverage KC, as measured by greater product market share, higher Tobin’s Q, and larger cash holdings. This study emphasizes the protective role of KC during the economic crisis when the market pays more attention to corporate sustainability and provides implications to corporate managers and investors.
{"title":"Knowledge Capital and Stock Returns during Crises in the Manufacturing Sector: Moderating Role of Market Share, Tobin’s Q, and Cash Holdings","authors":"Chaeho Chase Lee, Erdal Atukeren, Hohyun Kim","doi":"10.3390/risks12060100","DOIUrl":"https://doi.org/10.3390/risks12060100","url":null,"abstract":"This study analyzes the impact of knowledge capital (KC), a key element of firms’ innovation and competitiveness, on stock returns during economic crises when sustainable competitiveness becomes particularly important. We analyze the impact of the Global Financial Crisis and COVID-19 as economic crises, focusing on manufacturing industries with a high proportion of investment shifts from physical capital to KC. Our findings indicate that KC is positively associated with stock returns during the Global Financial Crisis and COVID-19. This positive relationship is strengthened by the firm’s ability to leverage KC, as measured by greater product market share, higher Tobin’s Q, and larger cash holdings. This study emphasizes the protective role of KC during the economic crisis when the market pays more attention to corporate sustainability and provides implications to corporate managers and investors.","PeriodicalId":21282,"journal":{"name":"Risks","volume":"16 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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