In the competitive landscape of Airbnb hosting, optimizing pricing strategies for properties is a complex challenge that requires revenue maximization with high occupancy rates. This research aimed to introduce a solution that leverages big data and machine learning techniques to help hosts improve their property’s market performance. Our primary goal was to introduce a solution that can augment property owners’ understanding of their property’s market value within their urban context, thereby optimizing both the utilization and profitability of their listings. We employed a multi-faceted approach with diverse models, including support vector regression, XGBoost, and neural networks, to analyze the influence of factors such as location, host attributes, and guest reviews on a listing’s financial performance. To further refine our predictive models, we integrated natural language processing techniques for in-depth listing review analysis, focusing on term frequency-inverse document frequency (TF-IDF), bag-of-words, and aspect-based sentiment analysis. Integrating such techniques allowed for in-depth listing review analysis, providing nuanced insights into guest preferences and satisfaction. Our findings demonstrated that AirBnB hosts can effectively utilize both state-of-the-art and traditional machine learning algorithms to better understand customer needs and preferences, more accurately assess their listings’ market value, and focus on the importance of dynamic pricing strategies. By adopting this data-driven approach, hosts can achieve a balance between maintaining competitive pricing and ensuring high occupancy rates. This method not only enhances revenue potential but also contributes to improved guest satisfaction and the growing field of data-driven decisions in the sharing economy, specially tailored to the challenges of short-term rentals.
{"title":"Maximizing Profitability and Occupancy: An Optimal Pricing Strategy for Airbnb Hosts Using Regression Techniques and Natural Language Processing","authors":"Luca Di Persio, Enis Lalmi","doi":"10.3390/jrfm17090414","DOIUrl":"https://doi.org/10.3390/jrfm17090414","url":null,"abstract":"In the competitive landscape of Airbnb hosting, optimizing pricing strategies for properties is a complex challenge that requires revenue maximization with high occupancy rates. This research aimed to introduce a solution that leverages big data and machine learning techniques to help hosts improve their property’s market performance. Our primary goal was to introduce a solution that can augment property owners’ understanding of their property’s market value within their urban context, thereby optimizing both the utilization and profitability of their listings. We employed a multi-faceted approach with diverse models, including support vector regression, XGBoost, and neural networks, to analyze the influence of factors such as location, host attributes, and guest reviews on a listing’s financial performance. To further refine our predictive models, we integrated natural language processing techniques for in-depth listing review analysis, focusing on term frequency-inverse document frequency (TF-IDF), bag-of-words, and aspect-based sentiment analysis. Integrating such techniques allowed for in-depth listing review analysis, providing nuanced insights into guest preferences and satisfaction. Our findings demonstrated that AirBnB hosts can effectively utilize both state-of-the-art and traditional machine learning algorithms to better understand customer needs and preferences, more accurately assess their listings’ market value, and focus on the importance of dynamic pricing strategies. By adopting this data-driven approach, hosts can achieve a balance between maintaining competitive pricing and ensuring high occupancy rates. This method not only enhances revenue potential but also contributes to improved guest satisfaction and the growing field of data-driven decisions in the sharing economy, specially tailored to the challenges of short-term rentals.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"203 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142258898","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 paper computes Balassa’s index of revealed comparative advantage for machinery and equipment (a rough proxy for high-tech goods) for a number of emerging areas (East Asia, South-East Asia, South Asia, Eastern Europe, Latin America, Africa, and the Middle East) and for selected individual countries over some 50 years, from the early 1970s to the early 2020s. The focus is on why some economies were successful in promoting high-tech sectors. As could be expected, experience differs hugely. In some countries, interventionist trade or industrial policies were crucial in fostering comparative advantage. In others, however, the role of policies appears to have been minor and successes were achieved thanks to the free play of market forces (including an important contribution, at least in some countries, coming from foreign direct investment).
{"title":"Changes in Revealed Comparative Advantage in Machinery and Equipment: Evidence for Emerging Markets","authors":"Andrea Boltho","doi":"10.3390/jrfm17090412","DOIUrl":"https://doi.org/10.3390/jrfm17090412","url":null,"abstract":"The paper computes Balassa’s index of revealed comparative advantage for machinery and equipment (a rough proxy for high-tech goods) for a number of emerging areas (East Asia, South-East Asia, South Asia, Eastern Europe, Latin America, Africa, and the Middle East) and for selected individual countries over some 50 years, from the early 1970s to the early 2020s. The focus is on why some economies were successful in promoting high-tech sectors. As could be expected, experience differs hugely. In some countries, interventionist trade or industrial policies were crucial in fostering comparative advantage. In others, however, the role of policies appears to have been minor and successes were achieved thanks to the free play of market forces (including an important contribution, at least in some countries, coming from foreign direct investment).","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142258903","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}
Ikhlaas Gurrib, Firuz Kamalov, Osama Atayah, Dalia Hemdan, Olga Starkova
This study investigates the long-run relationship between the U.S. dollar and the Canadian dollar by analyzing the bilateral exchange rate induced by nominal and real shocks. The methodology centers on a structural vector autoregressive (SVAR) model, including the analysis of impulse response and variance decomposition to account for the impact of nominal and real shocks on exchange rate movements. This study also decomposes real shocks into demand and supply factors from both Canada and the U.S. and compares their impacts on the nominal and real exchange rates. The results are compared to shocks driven by country-specific nominal factors. This study uses quarterly data from December 1972 to December 2023. The findings suggest that real shocks have a permanent impact on both the nominal and real exchange rates, compared to nominal shocks, which have a temporary impact. Country-specific real supply-side factors have a more significant impact than country-specific real demand-side factors. Country-specific nominal factors barely impacted the nominal and real exchange rates between the U.S. and Canada.
{"title":"Long-Run Trade Relationship between the U.S. and Canada: The Case of the Canadian Dollar with the U.S. Dollar","authors":"Ikhlaas Gurrib, Firuz Kamalov, Osama Atayah, Dalia Hemdan, Olga Starkova","doi":"10.3390/jrfm17090411","DOIUrl":"https://doi.org/10.3390/jrfm17090411","url":null,"abstract":"This study investigates the long-run relationship between the U.S. dollar and the Canadian dollar by analyzing the bilateral exchange rate induced by nominal and real shocks. The methodology centers on a structural vector autoregressive (SVAR) model, including the analysis of impulse response and variance decomposition to account for the impact of nominal and real shocks on exchange rate movements. This study also decomposes real shocks into demand and supply factors from both Canada and the U.S. and compares their impacts on the nominal and real exchange rates. The results are compared to shocks driven by country-specific nominal factors. This study uses quarterly data from December 1972 to December 2023. The findings suggest that real shocks have a permanent impact on both the nominal and real exchange rates, compared to nominal shocks, which have a temporary impact. Country-specific real supply-side factors have a more significant impact than country-specific real demand-side factors. Country-specific nominal factors barely impacted the nominal and real exchange rates between the U.S. and Canada.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142258900","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 explores the relationship between social media usage for investment advice and financial satisfaction across different generations. Ten ordered logit models were estimated using Stata to explore this relationship. Ordered logit analyses using data from the 2021 National Financial Capability Study State-by-State and Investor survey reveal that Generation X and millennials are less financially satisfied than baby boomers. While general social media use shows no statistically significant association, platform-specific analysis finds that Instagram and TikTok users report higher financial satisfaction, whereas YouTube users report lower satisfaction. Notably, millennials who use social media for investment advice are more financially satisfied than their peers. Detailed analyses reveal that Instagram, TikTok, and Twitter positively influence financial satisfaction across Gen Z, millennials, and Gen X, with more platform-specific associations observed for Facebook, LinkedIn, and Reddit among millennials and Gen X, respectively. These findings provide valuable insights for policymakers, financial professionals, and researchers, highlighting the need for targeted strategies to enhance financial well-being through social media.
本研究探讨了不同年代的人使用社交媒体获取投资建议与财务满意度之间的关系。使用 Stata 估算了 10 个有序对数模型来探讨这种关系。利用 2021 年《国家财务能力研究》各州和投资者调查数据进行的有序对数分析表明，与婴儿潮一代相比，X 代和千禧一代的财务满意度较低。虽然一般社交媒体的使用在统计上没有显著关联，但针对特定平台的分析发现，Instagram 和 TikTok 用户的财务满意度较高，而 YouTube 用户的满意度较低。值得注意的是，使用社交媒体获取投资建议的千禧一代比同龄人的财务满意度更高。详细分析显示，Instagram、TikTok 和 Twitter 对 Z 世代、千禧一代和 X 世代的财务满意度有积极影响，而在千禧一代和 X 世代中，Facebook、LinkedIn 和 Reddit 的特定平台关联度更高。这些发现为政策制定者、金融专业人士和研究人员提供了宝贵的见解，强调了通过社交媒体提高财务状况的针对性策略的必要性。
{"title":"Social Media for Investment Advice and Financial Satisfaction: Does Generation Matter?","authors":"Olamide Olajide, Sabina Pandey, Ichchha Pandey","doi":"10.3390/jrfm17090410","DOIUrl":"https://doi.org/10.3390/jrfm17090410","url":null,"abstract":"This study explores the relationship between social media usage for investment advice and financial satisfaction across different generations. Ten ordered logit models were estimated using Stata to explore this relationship. Ordered logit analyses using data from the 2021 National Financial Capability Study State-by-State and Investor survey reveal that Generation X and millennials are less financially satisfied than baby boomers. While general social media use shows no statistically significant association, platform-specific analysis finds that Instagram and TikTok users report higher financial satisfaction, whereas YouTube users report lower satisfaction. Notably, millennials who use social media for investment advice are more financially satisfied than their peers. Detailed analyses reveal that Instagram, TikTok, and Twitter positively influence financial satisfaction across Gen Z, millennials, and Gen X, with more platform-specific associations observed for Facebook, LinkedIn, and Reddit among millennials and Gen X, respectively. These findings provide valuable insights for policymakers, financial professionals, and researchers, highlighting the need for targeted strategies to enhance financial well-being through social media.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221336","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 examines the impact of market volatility and cryptocurrency holdings on corporate liquidity, with a particular focus on the differences between cryptocurrency exchanges and other businesses. The analysis is based on 181 firm-year observations from 2017 to 2022, using Bitcoin volatility, VIX, and VKOSPI as indicators of market volatility. Ordinary Least Squares (OLS) and robust regression analyses are employed to assess the relationships between these variables. It is first noted that, albeit insignificant, market volatility has a detrimental influence on company liquidity. The positive correlation for cryptocurrency exchanges, however, suggests that cryptocurrency exchanges could potentially leverage market volatility as a strategic advantage. Additionally, the study shows that cryptocurrency holdings enhance corporate liquidity, with a stronger association observed in cryptocurrency exchanges. The analysis also incorporates lagged variables to capture delayed effects, confirming that cryptocurrency holdings exert both immediate and delayed positive impacts on liquidity, likely due to effective strategic management practices within exchanges.
{"title":"Joint Impact of Market Volatility and Cryptocurrency Holdings on Corporate Liquidity: A Comparative Analysis of Cryptocurrency Exchanges and Other Firms","authors":"Namryoung Lee","doi":"10.3390/jrfm17090406","DOIUrl":"https://doi.org/10.3390/jrfm17090406","url":null,"abstract":"This study examines the impact of market volatility and cryptocurrency holdings on corporate liquidity, with a particular focus on the differences between cryptocurrency exchanges and other businesses. The analysis is based on 181 firm-year observations from 2017 to 2022, using Bitcoin volatility, VIX, and VKOSPI as indicators of market volatility. Ordinary Least Squares (OLS) and robust regression analyses are employed to assess the relationships between these variables. It is first noted that, albeit insignificant, market volatility has a detrimental influence on company liquidity. The positive correlation for cryptocurrency exchanges, however, suggests that cryptocurrency exchanges could potentially leverage market volatility as a strategic advantage. Additionally, the study shows that cryptocurrency holdings enhance corporate liquidity, with a stronger association observed in cryptocurrency exchanges. The analysis also incorporates lagged variables to capture delayed effects, confirming that cryptocurrency holdings exert both immediate and delayed positive impacts on liquidity, likely due to effective strategic management practices within exchanges.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221337","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 aims to examine whether corporate Twitter messages and tone have an effect on corporate stock return (RET) for the Saudi Stock Exchange “Tadawul”. The study also investigates whether the association differs across large- and small-sized firms. We used a sample of 11,099 firm-daily observations for non-financial firms that were traded on the Saudi Stock Exchange “Tadawul” across the period 1 April 2020 to 31 December 2020. Using panel ordinary least square (OLS) and two-stage least square (2SLS), we found that corporate Twitter (currently renamed ‘X’) messages is positively and significantly associated with stock return (RET). The findings also suggest that the message tone increases the stock returns. Furthermore, our results show different effects of Twitter messages and tone on stock return across small- and large-sized firms. In addition, our findings show that Twitter tone is positively associated with RET when the firm is large in size. However, when the firm is small, Twitter messages has a stronger effect on RET. Our findings provide policy implications for regulators and investors. Regulators might monitor the information in accurate ways. Also, investors might start to show interest in Twitter channels to follow the firm’s news.
{"title":"The Effect of Twitter Messages and Tone on Stock Return: The Case of Saudi Stock Market “Tadawul”","authors":"Mohammed S. Albarrak","doi":"10.3390/jrfm17090405","DOIUrl":"https://doi.org/10.3390/jrfm17090405","url":null,"abstract":"This research aims to examine whether corporate Twitter messages and tone have an effect on corporate stock return (RET) for the Saudi Stock Exchange “Tadawul”. The study also investigates whether the association differs across large- and small-sized firms. We used a sample of 11,099 firm-daily observations for non-financial firms that were traded on the Saudi Stock Exchange “Tadawul” across the period 1 April 2020 to 31 December 2020. Using panel ordinary least square (OLS) and two-stage least square (2SLS), we found that corporate Twitter (currently renamed ‘X’) messages is positively and significantly associated with stock return (RET). The findings also suggest that the message tone increases the stock returns. Furthermore, our results show different effects of Twitter messages and tone on stock return across small- and large-sized firms. In addition, our findings show that Twitter tone is positively associated with RET when the firm is large in size. However, when the firm is small, Twitter messages has a stronger effect on RET. Our findings provide policy implications for regulators and investors. Regulators might monitor the information in accurate ways. Also, investors might start to show interest in Twitter channels to follow the firm’s news.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221332","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 rigorously investigates the impact of COVID-19 on Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are incorporated. The findings highlight that confirmed and death cases contribute significantly to the escalation in TUNINDEX volatility when using both the conditional variance and the realized volatility. Interestingly, aggregate indices related to government interventions exhibit substantial impacts on the realized volatility, indicating a relative resilience of the Tunisian stock market amidst the challenges posed by COVID-19. However, the application of the bias-corrected wavelet analysis yields more subtle outcomes in terms of the correlations of both measures of volatility to the same metrics. Our econometric implications bear on the application of such a technique, as well as on the use of the realized volatility as an accurate measure of the “true” value of volatility. Nevertheless, the measures and actions undertaken by the authorities do not exclude fear and insecurity from investors due to another virus or any other crisis. The positive and long-term impact on the volatility of US equity market uncertainty, VIX, economic policy uncertainty (EPU), and the infectious disease EMV tracker (IDEMV) is obvious through the autoregressive distributed lag model (ARDL). A potential vulnerability of the Tunisian stock market to future shocks is not excluded. Government and stock market authorities should grapple with economic and financial fallout and always instill investor confidence. Importantly, our results put mechanisms such as overreaction to public news and (in)efficient use of information under test. Questioning the accuracy of announcements is then recommended.
{"title":"COVID-19 and Uncertainty Effects on Tunisian Stock Market Volatility: Insights from GJR-GARCH, Wavelet Coherence, and ARDL","authors":"Emna Trabelsi","doi":"10.3390/jrfm17090403","DOIUrl":"https://doi.org/10.3390/jrfm17090403","url":null,"abstract":"This study rigorously investigates the impact of COVID-19 on Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are incorporated. The findings highlight that confirmed and death cases contribute significantly to the escalation in TUNINDEX volatility when using both the conditional variance and the realized volatility. Interestingly, aggregate indices related to government interventions exhibit substantial impacts on the realized volatility, indicating a relative resilience of the Tunisian stock market amidst the challenges posed by COVID-19. However, the application of the bias-corrected wavelet analysis yields more subtle outcomes in terms of the correlations of both measures of volatility to the same metrics. Our econometric implications bear on the application of such a technique, as well as on the use of the realized volatility as an accurate measure of the “true” value of volatility. Nevertheless, the measures and actions undertaken by the authorities do not exclude fear and insecurity from investors due to another virus or any other crisis. The positive and long-term impact on the volatility of US equity market uncertainty, VIX, economic policy uncertainty (EPU), and the infectious disease EMV tracker (IDEMV) is obvious through the autoregressive distributed lag model (ARDL). A potential vulnerability of the Tunisian stock market to future shocks is not excluded. Government and stock market authorities should grapple with economic and financial fallout and always instill investor confidence. Importantly, our results put mechanisms such as overreaction to public news and (in)efficient use of information under test. Questioning the accuracy of announcements is then recommended.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221333","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}
Kelmara Mendes Vieira, Taiane Keila Matheis, Eliete dos Reis Lehnhart
Financial digitization is an irreversible phenomenon. The objective of this study is to construct the Digital Financial Capability Scale (DFCS). Starting with the development of a definition, we created a multidimensional scale composed of digital financial knowledge, digital financial behavior, and digital financial confidence. The validation process involved a qualitative stage, consisting of focus groups, expert validation, and pre-testing, and a quantitative stage, with exploratory and confirmatory factor analyses and structural equation modeling. The DFCS assesses an individual’s perception of their ability to apply financial knowledge, adopt appropriate financial behaviors, and feel confident in making financial decisions in a digital environment. The final version of the DFCS consists of a set of 33 items divided into the three dimensions. The scale can be very useful for researchers who wish to study financial capability in the digital environment, for financial agents to evaluate clients, and for assessing the outcomes of public policies aimed at enhancing the financial capability of the population.
{"title":"Digital Financial Capability Scale","authors":"Kelmara Mendes Vieira, Taiane Keila Matheis, Eliete dos Reis Lehnhart","doi":"10.3390/jrfm17090404","DOIUrl":"https://doi.org/10.3390/jrfm17090404","url":null,"abstract":"Financial digitization is an irreversible phenomenon. The objective of this study is to construct the Digital Financial Capability Scale (DFCS). Starting with the development of a definition, we created a multidimensional scale composed of digital financial knowledge, digital financial behavior, and digital financial confidence. The validation process involved a qualitative stage, consisting of focus groups, expert validation, and pre-testing, and a quantitative stage, with exploratory and confirmatory factor analyses and structural equation modeling. The DFCS assesses an individual’s perception of their ability to apply financial knowledge, adopt appropriate financial behaviors, and feel confident in making financial decisions in a digital environment. The final version of the DFCS consists of a set of 33 items divided into the three dimensions. The scale can be very useful for researchers who wish to study financial capability in the digital environment, for financial agents to evaluate clients, and for assessing the outcomes of public policies aimed at enhancing the financial capability of the population.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221334","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 potency of economic sanctions imposed on nations depends on demand and supply adjustment possibilities. Adverse GDP impacts will be maximal when import, export, production, distribution and finance are inflexible (universal non-substitution). This paper elaborates on these conditions and quantifies the maximum GDP loss that Western sanctions could have inflicted on Russia in 2022–2023. It reports the World Bank’s predictions, contrasts them with the results and draws inferences about the efficiency of Russia’s workably competitive markets. This paper shows that Russia’s economic system exhibits moderate universal substitutability and is less vulnerable to punitive discipline than Western policymakers suppose. The likelihood that economic sanctions will compel the Kremlin to restore Ukraine’s territorial integrity ceteris paribus is correspondingly low, even though war reduces Russia’s quality of existence. Western economic sanctions serve narrow geostrategic ends that are reconcilable with Pareto-efficient free trade and globalization, if precision-targeted, but as the Russo-Ukrainian war intensifies, an expanded array of novel and dubiously legal sanctions is degrading free trade, and spurring de-globalization and anti-Western coalitions. If this armed combat is prolonged, the goals of free trade and globalization could be set back for decades.
对国家实施经济制裁的效力取决于需求和供应调整的可能性。当进口、出口、生产、分销和金融缺乏灵活性（普遍非替代性）时，对国内生产总值的不利影响将达到最大。本文详细阐述了这些条件，并量化了 2022-2023 年西方制裁可能对俄罗斯造成的最大 GDP 损失。本文报告了世界银行的预测，将其与结果进行了对比，并对俄罗斯有效竞争市场的效率进行了推论。本文表明，俄罗斯的经济体系表现出适度的普遍可替代性，不像西方政策制定者想象的那样容易受到惩罚性纪律的影响。尽管战争会降低俄罗斯的生存质量，但经济制裁迫使克里姆林宫恢复乌克兰领土完整的可能性相应较低。西方的经济制裁服务于狭隘的地缘战略目的，如果目标精准，还能与帕累托效率的自由贸易和全球化相协调，但随着俄乌战争的加剧，一系列新颖且法律上可疑的制裁措施正在削弱自由贸易，刺激去全球化和反西方联盟。如果这场武装冲突旷日持久，自由贸易和全球化的目标可能会倒退几十年。
{"title":"Impairing Globalization: The Russo-Ukrainian War, Western Economic Sanctions and Asset Seizures","authors":"Steven Rosefielde","doi":"10.3390/jrfm17090402","DOIUrl":"https://doi.org/10.3390/jrfm17090402","url":null,"abstract":"The potency of economic sanctions imposed on nations depends on demand and supply adjustment possibilities. Adverse GDP impacts will be maximal when import, export, production, distribution and finance are inflexible (universal non-substitution). This paper elaborates on these conditions and quantifies the maximum GDP loss that Western sanctions could have inflicted on Russia in 2022–2023. It reports the World Bank’s predictions, contrasts them with the results and draws inferences about the efficiency of Russia’s workably competitive markets. This paper shows that Russia’s economic system exhibits moderate universal substitutability and is less vulnerable to punitive discipline than Western policymakers suppose. The likelihood that economic sanctions will compel the Kremlin to restore Ukraine’s territorial integrity ceteris paribus is correspondingly low, even though war reduces Russia’s quality of existence. Western economic sanctions serve narrow geostrategic ends that are reconcilable with Pareto-efficient free trade and globalization, if precision-targeted, but as the Russo-Ukrainian war intensifies, an expanded array of novel and dubiously legal sanctions is degrading free trade, and spurring de-globalization and anti-Western coalitions. If this armed combat is prolonged, the goals of free trade and globalization could be set back for decades.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221335","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 used data from publicly traded restaurant firms between 2000 and 2019 to test the effectiveness of multiple discriminant analysis (MDA) and logistic regression (logit) in predicting the probability of bankruptcy in the restaurant industry. We constructed various financial ratios extracted from the financial information and analyzed them to determine the optimal models. Our results show that liquid ratios (particularly the quick ratio), operating cash flow, and working capital emerge as the most crucial indicators of potential bankruptcy filings for restaurant firms. The results also show that the logit model performs better within the sample. However, both models exhibit similar predictive capacities with out-of-sample data.
{"title":"Bankruptcy Prediction for Restaurant Firms: A Comparative Analysis of Multiple Discriminant Analysis and Logistic Regression","authors":"Yang Huo, Leo H. Chan, Doug Miller","doi":"10.3390/jrfm17090399","DOIUrl":"https://doi.org/10.3390/jrfm17090399","url":null,"abstract":"In this paper, we used data from publicly traded restaurant firms between 2000 and 2019 to test the effectiveness of multiple discriminant analysis (MDA) and logistic regression (logit) in predicting the probability of bankruptcy in the restaurant industry. We constructed various financial ratios extracted from the financial information and analyzed them to determine the optimal models. Our results show that liquid ratios (particularly the quick ratio), operating cash flow, and working capital emerge as the most crucial indicators of potential bankruptcy filings for restaurant firms. The results also show that the logit model performs better within the sample. However, both models exhibit similar predictive capacities with out-of-sample data.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221243","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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