Pub Date : 2021-01-09DOI: 10.25300/misq/2021/16118
Gang Wang, Gang Chen, Huimin Zhao, Feng-xin Zhang, Shanlin Yang, Tian Lu
Emerging phenomena of ubiquitous multisource data offer promising avenues for making breakthroughs in financial risk prediction. While most existing methods for financial risk prediction are based on a single information source, which may not adequately capture various complex factors that jointly influence financial risks, we propose a hybrid-strategy-based self-adaptive method to effectively leverage heterogeneous soft information drawn from a variety of sources. The method uses a proposed new feature- sparsity learning method to adaptively integrate multisource heterogeneous soft features with hard features and a proposed improved evidential reasoning rule to adaptively aggregate base classifier predictions, thereby alleviating both the declarative bias and the procedural bias of the learning process. Evaluation in two cases at the individual level (concerning borrowers at a P2P lending platform) and the company level (concerning listed companies in the Chinese stock market) showed that, compared with relying solely on hard features, effectively incorporating multisource heterogeneous soft features using our proposed method enabled earlier prediction of financial risks with desirable performance.
{"title":"Leveraging Multi-Source Heterogeneous Data for Financial Risk Prediction: A Novel Hybrid-Strategy-Based Self-Adaptive Method","authors":"Gang Wang, Gang Chen, Huimin Zhao, Feng-xin Zhang, Shanlin Yang, Tian Lu","doi":"10.25300/misq/2021/16118","DOIUrl":"https://doi.org/10.25300/misq/2021/16118","url":null,"abstract":"Emerging phenomena of ubiquitous multisource data offer promising avenues for making breakthroughs in financial risk prediction. While most existing methods for financial risk prediction are based on a single information source, which may not adequately capture various complex factors that jointly influence financial risks, we propose a hybrid-strategy-based self-adaptive method to effectively leverage heterogeneous soft information drawn from a variety of sources. The method uses a proposed new feature- sparsity learning method to adaptively integrate multisource heterogeneous soft features with hard features and a proposed improved evidential reasoning rule to adaptively aggregate base classifier predictions, thereby alleviating both the declarative bias and the procedural bias of the learning process. Evaluation in two cases at the individual level (concerning borrowers at a P2P lending platform) and the company level (concerning listed companies in the Chinese stock market) showed that, compared with relying solely on hard features, effectively incorporating multisource heterogeneous soft features using our proposed method enabled earlier prediction of financial risks with desirable performance.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125989044","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 disentangle liquidity-constrained default and the incentives for strategic default using Deep Neural Network (DNN) methodology on a proprietary Trepp data set of commercial mortgages. Our results are robust (insensitive) to severe Financial Crisis (2008) and plausible economic catastrophe ensuing from COVID-19 pandemic (2020-2021). We demonstrate an identification strategy to retrieve the motive of default from observationally equivalent delinquency classes by bivariate analysis of default rate on Net operating income (NOI) and Loan-to-Value (LTV). NOI, appraisal reduction amount, prepayment penalty clause, balloon payment amongst others co-determine the delinquency class in highly nonlinear ways compared to more statistically significant variables such as LTV. Prediction accuracy for defaulted loans is higher when DNN is compared with other models, by increasing flexibility and relaxing the specification structure. These findings have significant implications for investors, rating agencies and policymakers.
{"title":"Deep Learning for Disentangling Liquidity-Constrained and Strategic Default","authors":"A. Bandyopadhyay, Yildiray Yildirim","doi":"10.2139/ssrn.3755672","DOIUrl":"https://doi.org/10.2139/ssrn.3755672","url":null,"abstract":"We disentangle liquidity-constrained default and the incentives for strategic default using Deep Neural Network (DNN) methodology on a proprietary Trepp data set of commercial mortgages. Our results are robust (insensitive) to severe Financial Crisis (2008) and plausible economic catastrophe ensuing from COVID-19 pandemic (2020-2021). We demonstrate an identification strategy to retrieve the motive of default from observationally equivalent delinquency classes by bivariate analysis of default rate on Net operating income (NOI) and Loan-to-Value (LTV). NOI, appraisal reduction amount, prepayment penalty clause, balloon payment amongst others co-determine the delinquency class in highly nonlinear ways compared to more statistically significant variables such as LTV. Prediction accuracy for defaulted loans is higher when DNN is compared with other models, by increasing flexibility and relaxing the specification structure. These findings have significant implications for investors, rating agencies and policymakers.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129780320","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}
Sensitivities are the core inputs to the Standardized Approach of the Fundamental Review of the Trading Book (FRTB) and are costly to implement and calculate for large portfolios and complex products. The internally calculated sensitivities by institutions may not be directly applicable for FRTB purpose due to different choices of risk factors. This paper introduces a new framework of defining and deriving FRTB sensitivities from the internally calculated sensitivities while keeping consistent risk measurement under the Standardized Approach framework, which will significantly improve efficiency of implementation, validation and model risk management for FRTB Standardized Approach and other similar regulatory programs, including SA-CVA (Credit Valuation Adjustment) VaR and ISDA-Standard Initial Margin Model (SIMM) etc.
{"title":"Calculation of Sensitivities for FRTB Standardized Approach","authors":"J. Zhan","doi":"10.2139/ssrn.3764941","DOIUrl":"https://doi.org/10.2139/ssrn.3764941","url":null,"abstract":"Sensitivities are the core inputs to the Standardized Approach of the Fundamental Review of the Trading Book (FRTB) and are costly to implement and calculate for large portfolios and complex products. The internally calculated sensitivities by institutions may not be directly applicable for FRTB purpose due to different choices of risk factors. This paper introduces a new framework of defining and deriving FRTB sensitivities from the internally calculated sensitivities while keeping consistent risk measurement under the Standardized Approach framework, which will significantly improve efficiency of implementation, validation and model risk management for FRTB Standardized Approach and other similar regulatory programs, including SA-CVA (Credit Valuation Adjustment) VaR and ISDA-Standard Initial Margin Model (SIMM) etc.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122459527","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}
Cryptocurrencies are gaining momentum in investor attention, are about to become a new asset class, and may provide a hedging alternative against the risk of devaluation of fiat currencies following the COVID-19 crisis. In order to provide a thorough understanding of this new asset class, risk indicators need to consider tail risk behaviour and the interdependencies between the cryptocurrencies not only for risk management but also for portfolio optimization. The tail risk network analysis framework proposed in the paper is able to identify individual risk characteristics and capture spillover effect in a network topology. Finally we construct tail event sensitive portfolios and consequently test the performance during an unforeseen COVID-19 pandemic.
{"title":"Tail Risk Network Effects in the Cryptocurrency Market during the COVID-19 Crisis","authors":"Rui Ren, Michael Althof, W. Härdle","doi":"10.2139/ssrn.3753421","DOIUrl":"https://doi.org/10.2139/ssrn.3753421","url":null,"abstract":"Cryptocurrencies are gaining momentum in investor attention, are about to become a new asset class, and may provide a hedging alternative against the risk of devaluation of fiat currencies following the COVID-19 crisis. In order to provide a thorough understanding of this new asset class, risk indicators need to consider tail risk behaviour and the interdependencies between the cryptocurrencies not only for risk management but also for portfolio optimization. The tail risk network analysis framework proposed in the paper is able to identify individual risk characteristics and capture spillover effect in a network topology. Finally we construct tail event sensitive portfolios and consequently test the performance during an unforeseen COVID-19 pandemic.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134293192","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}
Relying on a perspective borrowed from monetary policy announcements and introducing an econometric twist in the traditional event study analysis, we document the existence of an .event risk transfer., namely a significant credit risk transmission from the sovereign to the corporate sector after a sovereign rating downgrade. We find that after the delivery of the downgrade, corporate CDS spreads rise by 36% per annum and there is a widespread contagion across countries, in particular among those which were most exposed to the sovereign debt crisis. This effect exists on top of the standard relation between sovereign and corporate credit risk.
{"title":"It’s Not Time To Make a Change: Sovereign Fragility and the Corporate Credit Risk","authors":"F. Fornari, Andrea Zaghini","doi":"10.2139/ssrn.3785620","DOIUrl":"https://doi.org/10.2139/ssrn.3785620","url":null,"abstract":"Relying on a perspective borrowed from monetary policy announcements and introducing an econometric twist in the traditional event study analysis, we document the existence of an .event risk transfer., namely a significant credit risk transmission from the sovereign to the corporate sector after a sovereign rating downgrade. We find that after the delivery of the downgrade, corporate CDS spreads rise by 36% per annum and there is a widespread contagion across countries, in particular among those which were most exposed to the sovereign debt crisis. This effect exists on top of the standard relation between sovereign and corporate credit risk.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121588548","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}
Xiang Hu Bao (XHB), meaning 'mutual treasury' in Chinese, is a novel online mutual aid platform operated by Alibaba's Ant Financial to facilitate mutual risk sharing of critical illness exposures. XHB reached nearly 100 million members in less than one year since its launch and so far has offered its members critical illness protections at significantly lower cost than traditional critical illness insurance. There are three major distinctions between XHB and traditional insurance products. First, XHB leverages the tech giant's platform and digital technology to lower enrollment and claim processing costs. Second, different from insurance applying sophisticated actuarial pricing models, XHB collects no premiums ex ante from members, but instead equally allocates indemnities and administrative costs among participants after each claims period. Third, XHB limits coverage amount, often below critical illness insurance products, particularly for older participants. We show this restriction potentially leads to separating equilibrium, a la Rothschild-Stiglitz, where low-risk individuals enroll in XHB while high-risk individuals purchase critical illness insurance. Data shows that the incidence rate of the covered illness among XHB members is well below that of comparable critical illness insurance. Our findings further suggest the role of advantageous selection in explaining the cost advantages of the Fintech-based mutual aid programs.
{"title":"Mutual Risk Sharing and Fintech: The Case of Xiang Hu Bao","authors":"Hanming Fang, X. Qin, Wenfeng Wu, Tong Yu","doi":"10.2139/ssrn.3781998","DOIUrl":"https://doi.org/10.2139/ssrn.3781998","url":null,"abstract":"Xiang Hu Bao (XHB), meaning 'mutual treasury' in Chinese, is a novel online mutual aid platform operated by Alibaba's Ant Financial to facilitate mutual risk sharing of critical illness exposures. XHB reached nearly 100 million members in less than one year since its launch and so far has offered its members critical illness protections at significantly lower cost than traditional critical illness insurance. There are three major distinctions between XHB and traditional insurance products. First, XHB leverages the tech giant's platform and digital technology to lower enrollment and claim processing costs. Second, different from insurance applying sophisticated actuarial pricing models, XHB collects no premiums ex ante from members, but instead equally allocates indemnities and administrative costs among participants after each claims period. Third, XHB limits coverage amount, often below critical illness insurance products, particularly for older participants. We show this restriction potentially leads to separating equilibrium, a la Rothschild-Stiglitz, where low-risk individuals enroll in XHB while high-risk individuals purchase critical illness insurance. Data shows that the incidence rate of the covered illness among XHB members is well below that of comparable critical illness insurance. Our findings further suggest the role of advantageous selection in explaining the cost advantages of the Fintech-based mutual aid programs.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129785591","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}
M. Goergen, D. Gounopoulos, Panagiotis Koutroumpis
Abstract Using credit ratings as an uncertainty-reducing mechanism, we provide evidence of the beneficial impact of multiple credit ratings on reducing IPO underpricing and filing price revision. We find that the acquisition of multiple ratings in the pre-IPO period mitigates uncertainty more than the acquisition of a single rating. Multi-rated firms also have higher probabilities of survival than those with a single rating, whereas credit rating levels matter only for IPOs with more than one rating. The IPOs that are awarded the first rating on the borderline between investment and non-investment grades are more likely to seek an additional rating.
{"title":"Do Multiple Credit Ratings Reduce Money Left on the Table? Evidence from U.S. IPOs","authors":"M. Goergen, D. Gounopoulos, Panagiotis Koutroumpis","doi":"10.2139/ssrn.3358474","DOIUrl":"https://doi.org/10.2139/ssrn.3358474","url":null,"abstract":"Abstract Using credit ratings as an uncertainty-reducing mechanism, we provide evidence of the beneficial impact of multiple credit ratings on reducing IPO underpricing and filing price revision. We find that the acquisition of multiple ratings in the pre-IPO period mitigates uncertainty more than the acquisition of a single rating. Multi-rated firms also have higher probabilities of survival than those with a single rating, whereas credit rating levels matter only for IPOs with more than one rating. The IPOs that are awarded the first rating on the borderline between investment and non-investment grades are more likely to seek an additional rating.","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":"121685180","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 article builds a new model of capital structure and nonpension investment decisions to show that regulatory and investment incentives created by accrued pension obligations exacerbate traditional agency problems between stockholders and bondholders. The article identifies conditions under which firms with accrued pension liabilities have an incentive to choose an overly risky capital structure, invest in risky projects with negative net present value, and/or under‐fund their pension accounts.
{"title":"Pension Regulation, Firm Borrowing, and Investment Risk","authors":"Margaret J. Lay","doi":"10.1111/jori.12299","DOIUrl":"https://doi.org/10.1111/jori.12299","url":null,"abstract":"This article builds a new model of capital structure and nonpension investment decisions to show that regulatory and investment incentives created by accrued pension obligations exacerbate traditional agency problems between stockholders and bondholders. The article identifies conditions under which firms with accrued pension liabilities have an incentive to choose an overly risky capital structure, invest in risky projects with negative net present value, and/or under‐fund their pension accounts.","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":"126079613","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}
Pub Date : 2020-12-01DOI: 10.5089/9781513564548.001.A001
Serhan Cevik, J. Jalles
Climate change is an existential threat to the world economy like no other, with complex, evolving and nonlinear dynamics that remain a source of great uncertainty. There is a bourgeoning literature on the economic impact of climate change, but research on how climate change affects sovereign risks is limited. Building on our previous research focusing on the impact of climate change on sovereign risks, this paper empirically investigates how climate change may affect sovereign credit ratings. By means of binary-choice models, we find that climate change vulnerability has adverse effects on sovereign credit ratings, after controlling for conventional macroeconomic determinants of credit worthiness. On the other hand, with regards to climate change resilience, we find that countries with greater climate change resilience benefit from higher (better) credit ratings. These findings, robust to a battery of sensitivity checks, also show that impact of climate change is disproportionately greater in developing countries due largely to weaker capacity to adapt to and mitigate the consequences of climate change.
{"title":"Feeling the Heat: Climate Shocks and Credit Ratings","authors":"Serhan Cevik, J. Jalles","doi":"10.5089/9781513564548.001.A001","DOIUrl":"https://doi.org/10.5089/9781513564548.001.A001","url":null,"abstract":"Climate change is an existential threat to the world economy like no other, with complex, evolving and nonlinear dynamics that remain a source of great uncertainty. There is a bourgeoning literature on the economic impact of climate change, but research on how climate change affects sovereign risks is limited. Building on our previous research focusing on the impact of climate change on sovereign risks, this paper empirically investigates how climate change may affect sovereign credit ratings. By means of binary-choice models, we find that climate change vulnerability has adverse effects on sovereign credit ratings, after controlling for conventional macroeconomic determinants of credit worthiness. On the other hand, with regards to climate change resilience, we find that countries with greater climate change resilience benefit from higher (better) credit ratings. These findings, robust to a battery of sensitivity checks, also show that impact of climate change is disproportionately greater in developing countries due largely to weaker capacity to adapt to and mitigate the consequences of climate change.","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":"130709371","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}
G. Yu, Xiaoqiang Cai, Daniel Zhuoyu Long, Lianmin Zhang
We consider a multi-portfolio optimization problem where nonlinear market impact costs result in a strong dependency of one account's performance on the trading activities of other accounts. We develop a novel target-oriented model that jointly optimizes the rebalancing trades and split of market impact costs. The key advantages of our proposed model include the consideration of clients' targets on investment returns and the incorporation of distributional uncertainty. The former helps the fund manager circumvent the difficulty in identifying clients' utility functions or risk parameters, while the latter addresses a practical challenge that the probability distributions of risky asset returns cannot be fully observed. Specifically, to evaluate multiple portfolios' investment payoffs achieving their targets, we propose a new type of performance measure, called the fairness-aware multi-participant satisficing (FMS) criterion. This criterion can be extended to encompass the distributional uncertainty and has the salient feature of addressing the fairness issue with the collective satisfaction level as determined by the least satisfied participant. We find that, structurally, the FMS criterion has a dual connection with a set of risk measures. For multi-portfolio optimization, we consider the FMS criterion with conditional value-at-risk, a popular risk proxy in financial studies, being the underlying risk measure to further account for the magnitude of shortfalls against targets. The resulting problem, although non-convex, can be solved efficiently by solving an equivalent converging sequence of tractable subproblems. The numerical study shows that our approach outperforms utility-based models in achieving targets and is more robust in out-of-sample performance.
{"title":"Multi-portfolio Optimization: A Fairness-aware Target-oriented Model","authors":"G. Yu, Xiaoqiang Cai, Daniel Zhuoyu Long, Lianmin Zhang","doi":"10.2139/ssrn.3740629","DOIUrl":"https://doi.org/10.2139/ssrn.3740629","url":null,"abstract":"We consider a multi-portfolio optimization problem where nonlinear market impact costs result in a strong dependency of one account's performance on the trading activities of other accounts. We develop a novel target-oriented model that jointly optimizes the rebalancing trades and split of market impact costs. The key advantages of our proposed model include the consideration of clients' targets on investment returns and the incorporation of distributional uncertainty. The former helps the fund manager circumvent the difficulty in identifying clients' utility functions or risk parameters, while the latter addresses a practical challenge that the probability distributions of risky asset returns cannot be fully observed. Specifically, to evaluate multiple portfolios' investment payoffs achieving their targets, we propose a new type of performance measure, called the fairness-aware multi-participant satisficing (FMS) criterion. This criterion can be extended to encompass the distributional uncertainty and has the salient feature of addressing the fairness issue with the collective satisfaction level as determined by the least satisfied participant. We find that, structurally, the FMS criterion has a dual connection with a set of risk measures. For multi-portfolio optimization, we consider the FMS criterion with conditional value-at-risk, a popular risk proxy in financial studies, being the underlying risk measure to further account for the magnitude of shortfalls against targets. The resulting problem, although non-convex, can be solved efficiently by solving an equivalent converging sequence of tractable subproblems. The numerical study shows that our approach outperforms utility-based models in achieving targets and is more robust in out-of-sample performance.","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":"129545097","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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