Quyen Nguyen, I. Diaz‐Rainey, D. Kuruppuarachchi, Matthew McCarten, Eric K. M. Tan
We examine banks’ exposure to climate transition risk using a bottom-up, loan-level methodology incorporating climate stress test based on the Merton probability of default model and transition pathways from the IPCC. Specifically, we match machine learning predictions of corporate carbon footprints to syndicated loans initiated in 2010-2018 and aggregate these to loan portfolios of the twenty largest banks in the United States. Banks vary in their climate transition risk not only due to their exposure to the energy sectors but also due to borrowers’ carbon emission profiles from other sectors. Banks generally lend a minimal amount to coal (0.4%) but hold a considerable exposure in oil and gas (8.6%) and electricity firms (4.6%) and thus have a large exposure to the energy sectors (13.5%). We observe that climate transition risk profile was stable over time, save for a temporary (in some cases) and permanent (in others), reduction in their fossil-fuel exposure after the Paris Agreement. From the stress testing, the median loss is 0.5% of US syndicated loans, representing a decrease in CET1 capital of 4.1% but this may grow twice as large in the 1.5oC scenarios (1.4%-2.1% of loan value, 12%-16% of CET1 capital) compared to the 2oC target (0.6%-1.1% of loan value, 5%-9% of CET1 capital) with significant tail-end risk (7.7% of loan value, 62% of CET1 capital). Banks’ vulnerabilities are also driven by the ex-ante financial risk of their borrowers more generally, highlighting that climate risk is not independent from conventional risks.
{"title":"Climate Transition Risk in U.S. Loan Portfolios: Are All Banks the Same?","authors":"Quyen Nguyen, I. Diaz‐Rainey, D. Kuruppuarachchi, Matthew McCarten, Eric K. M. Tan","doi":"10.2139/ssrn.3766592","DOIUrl":"https://doi.org/10.2139/ssrn.3766592","url":null,"abstract":"We examine banks’ exposure to climate transition risk using a bottom-up, loan-level methodology incorporating climate stress test based on the Merton probability of default model and transition pathways from the IPCC. Specifically, we match machine learning predictions of corporate carbon footprints to syndicated loans initiated in 2010-2018 and aggregate these to loan portfolios of the twenty largest banks in the United States. Banks vary in their climate transition risk not only due to their exposure to the energy sectors but also due to borrowers’ carbon emission profiles from other sectors. Banks generally lend a minimal amount to coal (0.4%) but hold a considerable exposure in oil and gas (8.6%) and electricity firms (4.6%) and thus have a large exposure to the energy sectors (13.5%). We observe that climate transition risk profile was stable over time, save for a temporary (in some cases) and permanent (in others), reduction in their fossil-fuel exposure after the Paris Agreement. From the stress testing, the median loss is 0.5% of US syndicated loans, representing a decrease in CET1 capital of 4.1% but this may grow twice as large in the 1.5oC scenarios (1.4%-2.1% of loan value, 12%-16% of CET1 capital) compared to the 2oC target (0.6%-1.1% of loan value, 5%-9% of CET1 capital) with significant tail-end risk (7.7% of loan value, 62% of CET1 capital). Banks’ vulnerabilities are also driven by the ex-ante financial risk of their borrowers more generally, highlighting that climate risk is not independent from conventional risks.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133544427","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}
Abstract Cap-and-trade programs such as the European Union's Emissions Trading System (EU ETS) expose firms to considerable risks, to which the firms can respond with hedging. We develop an intertemporal stochastic equilibrium model to analyze the implications of hedging by risk-averse firms. We show that the resulting time-varying risk premium depends on the size of the permit bank. Applying the model to the EU ETS, we find that hedging can lead to a U-shaped price path, because prices initially fall due to negative risk premiums and then rise as the hedging demand declines. The Market Stability Reserve (MSR) reduces the permit bank and thus, increases the hedging value of the permits. This offers an explanation for the recent price hike, but also implies that prices may decline in the future due to more negative risk premiums. In addition, we find higher permit cancellations through the MSR than previous analyses, which do not account for hedging.
{"title":"Hedging and temporal permit issuances in cap-and-trade programs: the Market Stability Reserve under risk aversion","authors":"O. Tietjen, K. Lessmann, M. Pahle","doi":"10.2139/ssrn.3436736","DOIUrl":"https://doi.org/10.2139/ssrn.3436736","url":null,"abstract":"Abstract Cap-and-trade programs such as the European Union's Emissions Trading System (EU ETS) expose firms to considerable risks, to which the firms can respond with hedging. We develop an intertemporal stochastic equilibrium model to analyze the implications of hedging by risk-averse firms. We show that the resulting time-varying risk premium depends on the size of the permit bank. Applying the model to the EU ETS, we find that hedging can lead to a U-shaped price path, because prices initially fall due to negative risk premiums and then rise as the hedging demand declines. The Market Stability Reserve (MSR) reduces the permit bank and thus, increases the hedging value of the permits. This offers an explanation for the recent price hike, but also implies that prices may decline in the future due to more negative risk premiums. In addition, we find higher permit cancellations through the MSR than previous analyses, which do not account for hedging.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123256023","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 aims to extend downside protection to a hedge fund investment portfolio based on shared loss fee structures that have become increasing popular in the market. In particular, we consider a second tranche and suggest the purchase of an upfront reinsurance contract for any losses on the fund beyond the threshold covered by the first tranche, i.e. gaining full portfolio protection. We identify a fund’s underlying liquidity as a key parameter and study the pricing of this additional reinsurance using two approaches: First, an analytic closed-form solution based on the Black-Scholes framework and second, a numerical simulation using a Markov-switching model. In addition, a simplified backtesting method is implemented to evaluate the practical application of the concept.
{"title":"Price of Liquidity in the Reinsurance of Fund Returns","authors":"D. Saunders, L. Seco, M. Senn","doi":"10.2139/ssrn.3738175","DOIUrl":"https://doi.org/10.2139/ssrn.3738175","url":null,"abstract":"This paper aims to extend downside protection to a hedge fund investment portfolio based on shared loss fee structures that have become increasing popular in the market. In particular, we consider a second tranche and suggest the purchase of an upfront reinsurance contract for any losses on the fund beyond the threshold covered by the first tranche, i.e. gaining full portfolio protection. We identify a fund’s underlying liquidity as a key parameter and study the pricing of this additional reinsurance using two approaches: First, an analytic closed-form solution based on the Black-Scholes framework and second, a numerical simulation using a Markov-switching model. In addition, a simplified backtesting method is implemented to evaluate the practical application of the concept.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129376980","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 Credit Rating Agencies (CRAs) is to reduce the degree of asymmetric information in capital markets by issuing judgments. These judgments, also known as ratings, provide investors with the likeliness of default of debt issuers and represent valuable input for the global financial system as they allow them to better perceive the risks associated with investing in government/corporate bonds or preferred stock. Despite how useful they are providing an estimation of the credit quality of investment products, CRAs remain open to criticism because of three main issues which were some of the main catalysts of the 2008 financial crisis: 1) The issuer-pays model, 2) lack of competition, and 3) mandatory reliance on ratings. Since then, international financial authorities worldwide have focused on implementing regulations to address the inconveniences regarding the rating process.
This paper deeply reviews the previously enounced issues involving CRAs and it proposes the following policy recommendations for Canada: 1) To make CRAs liable for deliberate malperformance, 2) to improve the central monitoring of CRAs’ activities, and 3) to conduct research on which business models may be more suitable than the issuer-pays model. These recommendations are extensively based on the academic literature about the CRA industry, and the implemented measures in the United States and the European Union. It is concluded that regardless of the numerous efficient regulations implemented after the Great Recession, another global financial crisis remains latent while CRAs continue to operate under the current framework.
{"title":"Credit Rating Agencies in Canada: Industry Issues and How to Regulate Them","authors":"Branko Malaver-Vojvodic","doi":"10.2139/ssrn.3757290","DOIUrl":"https://doi.org/10.2139/ssrn.3757290","url":null,"abstract":"The objective of Credit Rating Agencies (CRAs) is to reduce the degree of asymmetric information in capital markets by issuing judgments. These judgments, also known as ratings, provide investors with the likeliness of default of debt issuers and represent valuable input for the global financial system as they allow them to better perceive the risks associated with investing in government/corporate bonds or preferred stock. Despite how useful they are providing an estimation of the credit quality of investment products, CRAs remain open to criticism because of three main issues which were some of the main catalysts of the 2008 financial crisis: 1) The issuer-pays model, 2) lack of competition, and 3) mandatory reliance on ratings. Since then, international financial authorities worldwide have focused on implementing regulations to address the inconveniences regarding the rating process.<br><br>This paper deeply reviews the previously enounced issues involving CRAs and it proposes the following policy recommendations for Canada: 1) To make CRAs liable for deliberate malperformance, 2) to improve the central monitoring of CRAs’ activities, and 3) to conduct research on which business models may be more suitable than the issuer-pays model. These recommendations are extensively based on the academic literature about the CRA industry, and the implemented measures in the United States and the European Union. It is concluded that regardless of the numerous efficient regulations implemented after the Great Recession, another global financial crisis remains latent while CRAs continue to operate under the current framework.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122106511","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}
“Getting Rid of Issuer-Pay Will Not Improve Credit Ratings” sparked questions, which we will try to answer here:
• Why was S&P so slow to downgrade subprime ratings 2007-09? • Did other rating agencies downgrade these debts faster than S&P? • If banning issuer pay isn’t sufficient, how can S&P’s credit ratings be improved?
{"title":"FAQs on 'Getting Rid of Issuer-Pay Will Not Improve Credit Ratings'","authors":"Douglas J. Lucas","doi":"10.2139/ssrn.3743358","DOIUrl":"https://doi.org/10.2139/ssrn.3743358","url":null,"abstract":"“Getting Rid of Issuer-Pay Will Not Improve Credit Ratings” sparked questions, which we will try to answer here:<br><br>• Why was S&P so slow to downgrade subprime ratings 2007-09?<br>• Did other rating agencies downgrade these debts faster than S&P?<br>• If banning issuer pay isn’t sufficient, how can S&P’s credit ratings be improved?","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132743811","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 discuss how to discretize continuous-time short rate models in order to properly handle backward-looking interest rate derivatives. We show that the popular discretization approaches are based on forward-looking one-period rates, making them intrinsically ill-suited to deal with backward-looking rates. We propose a simple backward discretization approach that is beneficial when dealing with both backward-looking and forward-looking interest rate derivatives.
{"title":"The Curious Case of Backward Short Rates","authors":"A. Lyashenko, Yutian Nie","doi":"10.2139/ssrn.3728873","DOIUrl":"https://doi.org/10.2139/ssrn.3728873","url":null,"abstract":"In this paper, we discuss how to discretize continuous-time short rate models in order to properly handle backward-looking interest rate derivatives. We show that the popular discretization approaches are based on forward-looking one-period rates, making them intrinsically ill-suited to deal with backward-looking rates. We propose a simple backward discretization approach that is beneficial when dealing with both backward-looking and forward-looking interest rate derivatives.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131813384","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}
Optimization of distortion riskmetrics with distributional uncertainty has wide applications in finance and operations research. Distortion riskmetrics include many commonly applied risk measures and deviation measures, which are not necessarily monotone or convex. One of our central findings is a unifying result that allows us to convert an optimization of a non-convex distortion riskmetric with distributional uncertainty to a convex one, leading to great tractability. The key to the unifying equivalence result is the novel notion of closedness under concentration of sets of distributions. Our results include many special cases that are well studied in the optimization literature, including but not limited to optimizing probabilities, Value-at-Risk, Expected Shortfall, and Yaari's dual utility under various forms of distributional uncertainty. We illustrate our theoretical results via applications to portfolio optimization, optimization under moment constraints, and preference robust optimization.
{"title":"Optimizing Distortion Riskmetrics With Distributional Uncertainty","authors":"Silvana M. Pesenti, Qiuqi Wang, Ruodu Wang","doi":"10.2139/ssrn.3728638","DOIUrl":"https://doi.org/10.2139/ssrn.3728638","url":null,"abstract":"Optimization of distortion riskmetrics with distributional uncertainty has wide applications in finance and operations research. Distortion riskmetrics include many commonly applied risk measures and deviation measures, which are not necessarily monotone or convex. One of our central findings is a unifying result that allows us to convert an optimization of a non-convex distortion riskmetric with distributional uncertainty to a convex one, leading to great tractability. The key to the unifying equivalence result is the novel notion of closedness under concentration of sets of distributions. Our results include many special cases that are well studied in the optimization literature, including but not limited to optimizing probabilities, Value-at-Risk, Expected Shortfall, and Yaari's dual utility under various forms of distributional uncertainty. We illustrate our theoretical results via applications to portfolio optimization, optimization under moment constraints, and preference robust optimization.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132971633","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}
New reports show that the fi nancial sector is increasingly adopting machine learning (ML) tools to manage credit risk. In this environment, supervisors face the challenge of allowing credit institutions to benefi t from technological progress and financial innovation, while at the same ensuring compatibility with regulatory requirements and that technological neutrality is observed. We propose a new framework for supervisors to measure the costs and benefi ts of evaluating ML models, aiming to shed more light on this technology’s alignment with the regulation. We follow three steps. First, we identify the benefi ts by reviewing the literature. We observe that ML delivers predictive gains of up to 20 % in default classifi cation compared with traditional statistical models. Second, we use the process for validating internal ratings-based (IRB) systems for regulatory capital to detect ML’s limitations in credit risk mangement. We identify up to 13 factors that might constitute a supervisory cost. Finally, we propose a methodology for evaluating these costs. For illustrative purposes, we compute the benefi ts by estimating the predictive gains of six ML models using a public database on credit default. We then calculate a supervisory cost function through a scorecard in which we assign weights to each factor for each ML model, based on how the model is used by the fi nancial institution and the supervisor’s risk tolerance. From a supervisory standpoint,having a structured methodology for assessing ML models could increase transparency and remove an obstacle to innovation in the financial industry.
{"title":"Machine Learning in Credit Risk: Measuring the Dilemma Between Prediction and Supervisory Cost","authors":"A. Alonso, Joselyn Carbo","doi":"10.2139/ssrn.3724374","DOIUrl":"https://doi.org/10.2139/ssrn.3724374","url":null,"abstract":"New reports show that the fi nancial sector is increasingly adopting machine learning (ML) tools to manage credit risk. In this environment, supervisors face the challenge of allowing credit institutions to benefi t from technological progress and financial innovation, while at the same ensuring compatibility with regulatory requirements and that technological neutrality is observed. We propose a new framework for supervisors to measure the costs and benefi ts of evaluating ML models, aiming to shed more light on this technology’s alignment with the regulation. We follow three steps. First, we identify the benefi ts by reviewing the literature. We observe that ML delivers predictive gains of up to 20 % in default classifi cation compared with traditional statistical models. Second, we use the process for validating internal ratings-based (IRB) systems for regulatory capital to detect ML’s limitations in credit risk mangement. We identify up to 13 factors that might constitute a supervisory cost. Finally, we propose a methodology for evaluating these costs. For illustrative purposes, we compute the benefi ts by estimating the predictive gains of six ML models using a public database on credit default. We then calculate a supervisory cost function through a scorecard in which we assign weights to each factor for each ML model, based on how the model is used by the fi nancial institution and the supervisor’s risk tolerance. From a supervisory standpoint,having a structured methodology for assessing ML models could increase transparency and remove an obstacle to innovation in the financial industry.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132806979","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}
We investigate how a borrower’s adverse climate-related incidents affect bank loan contracting. Using a sample of 2,622 publicly traded US firms over the period 2000–2016, we construct event-based measures of corporate climate performances based on firm-level adverse climate incidents such as oil spills, excess carbon emissions and deforestation projects. We show that loans initiated after the occurrence of firms’ first adverse climate-related incidents have significantly higher spreads, shorter maturities, more covenant restrictions, and higher likelihood of being secured with collateral. In cross-sectional tests, we find that the intensity and influence of adverse climate-related incidents exacerbate the pricing of bank loans. Our results support the notion that banks incorporate firm-specific climate performance into their lending contracts.
{"title":"Adverse Climate Incidents and Bank Loan Contracting","authors":"D. Anginer, Karel Hrazdil, Jiyuan Li, Ray Zhang","doi":"10.2139/ssrn.3723771","DOIUrl":"https://doi.org/10.2139/ssrn.3723771","url":null,"abstract":"We investigate how a borrower’s adverse climate-related incidents affect bank loan contracting. Using a sample of 2,622 publicly traded US firms over the period 2000–2016, we construct event-based measures of corporate climate performances based on firm-level adverse climate incidents such as oil spills, excess carbon emissions and deforestation projects. We show that loans initiated after the occurrence of firms’ first adverse climate-related incidents have significantly higher spreads, shorter maturities, more covenant restrictions, and higher likelihood of being secured with collateral. In cross-sectional tests, we find that the intensity and influence of adverse climate-related incidents exacerbate the pricing of bank loans. Our results support the notion that banks incorporate firm-specific climate performance into their lending contracts.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115661302","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}
We perform a historical analysis of selected rough volatility models to the SPX market. Tailoring the neural network pricing method of [27] to our needs, we train neural networks for the rough Heston model from [14], the rough Bergomi model from [4] as well as an extended version of the latter. As a benchmark we include also the classical Heston model from [24]. Calibrating the models across 15 years of historical SPX options prices we first and foremost document consistently superior results using rough volatility. Comparing rough Heston and rough Bergomi we also find that while the former model calibrates slightly better, the latter model produces more robust predictions. Our calibration results also illuminate a structural problem in that both of these models (on average) produces too little curvature at short expirations, too little skew at long expirations. Using an extended rough Bergomi model where the explosion rates of smile and skew are decoupled, did not resolve this problem.
{"title":"Historical Analysis of Rough Volatility Models to the SPX Market","authors":"Sigurd Emil Rømer","doi":"10.2139/ssrn.3678235","DOIUrl":"https://doi.org/10.2139/ssrn.3678235","url":null,"abstract":"We perform a historical analysis of selected rough volatility models to the SPX market. Tailoring the neural network pricing method of [27] to our needs, we train neural networks for the rough Heston model from [14], the rough Bergomi model from [4] as well as an extended version of the latter. As a benchmark we include also the classical Heston model from [24]. Calibrating the models across 15 years of historical SPX options prices we first and foremost document consistently superior results using rough volatility. Comparing rough Heston and rough Bergomi we also find that while the former model calibrates slightly better, the latter model produces more robust predictions. Our calibration results also illuminate a structural problem in that both of these models (on average) produces too little curvature at short expirations, too little skew at long expirations. Using an extended rough Bergomi model where the explosion rates of smile and skew are decoupled, did not resolve this problem.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125026072","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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