Shadow financing through off-balance sheet wealth management products (WMPs) has become increasingly important besides deposits in China. We quantify the economic magnitude of the effect of WMPs on banking stability in an equilibrium model calibrated to Chinese banking sector data. Alternative equilibria emerge, which deviate substantially from the observed banking system and lead to severe financial distress and large welfare losses. Rollover costs from the WMP market and negative shocks to the asset market underlying WMPs can exacerbate banking instability. Moreover, we show that smaller and medium sized banks are comparably relevant for financial stability as the systemically important big 4 banks in China.
{"title":"Wealth Management Products, Banking Competition, and Stability: Evidence from China","authors":"Xu Feng, Eva Luetkebohmert, Yajun Xiao","doi":"10.2139/ssrn.3892709","DOIUrl":"https://doi.org/10.2139/ssrn.3892709","url":null,"abstract":"Shadow financing through off-balance sheet wealth management products (WMPs) has become increasingly important besides deposits in China. We quantify the economic magnitude of the effect of WMPs on banking stability in an equilibrium model calibrated to Chinese banking sector data. Alternative equilibria emerge, which deviate substantially from the observed banking system and lead to severe financial distress and large welfare losses. Rollover costs from the WMP market and negative shocks to the asset market underlying WMPs can exacerbate banking instability. Moreover, we show that smaller and medium sized banks are comparably relevant for financial stability as the systemically important big 4 banks in China.","PeriodicalId":331807,"journal":{"name":"Banking & Insurance eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134330283","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}
Predicting the number of outstanding claims (IBNR) is a central problem in actuarial loss reserving. Classical approaches like the Chain Ladder method rely on aggregating the available data in form of loss triangles, thereby wasting potentially useful additional claims information. A new approach based on a micro-level model for reporting delays involving neural networks is proposed. It is shown by extensive simulation experiments and an application to a large-scale real data set involving motor legal insurance claims that the new approach provides more accurate predictions in case of non-homogeneous portfolios.
{"title":"Mirco-level Prediction of Outstanding Claim Counts using Neural Networks","authors":"Axel Bücher, Alexander Rosenstock","doi":"10.2139/ssrn.3949754","DOIUrl":"https://doi.org/10.2139/ssrn.3949754","url":null,"abstract":"Predicting the number of outstanding claims (IBNR) is a central problem in actuarial loss reserving. Classical approaches like the Chain Ladder method rely on aggregating the available data in form of loss triangles, thereby wasting potentially useful additional claims information. A new approach based on a micro-level model for reporting delays involving neural networks is proposed. It is shown by extensive simulation experiments and an application to a large-scale real data set involving motor legal insurance claims that the new approach provides more accurate predictions in case of non-homogeneous portfolios.","PeriodicalId":331807,"journal":{"name":"Banking & Insurance eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115170900","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 examine the factors that determine the likelihood of borrowers using non-traditional mortgages (NTMs) prior to the Great Recession. Borrower choice depends on borrower characteristics such as income, levels of asset holdings, credit score and age and on market factors such as house price appreciation as shown in the literature. We add to the literature by showing that lending competition was significantly associated with the early growth of NTMs while growth of non-bank lending was associated with later-stage expansion of NTMs. We also find that state level anti-predatory lending laws were more effective in restraining the origination of NTMs in markets with higher levels of lending competition.
{"title":"Lending Competition, Regulation and Non-Traditional Mortgages","authors":"Arthur Acolin, Xudong An, Susan M. Wachter","doi":"10.1111/1540-6229.12366","DOIUrl":"https://doi.org/10.1111/1540-6229.12366","url":null,"abstract":"We examine the factors that determine the likelihood of borrowers using non-traditional mortgages (NTMs) prior to the Great Recession. Borrower choice depends on borrower characteristics such as income, levels of asset holdings, credit score and age and on market factors such as house price appreciation as shown in the literature. We add to the literature by showing that lending competition was significantly associated with the early growth of NTMs while growth of non-bank lending was associated with later-stage expansion of NTMs. We also find that state level anti-predatory lending laws were more effective in restraining the origination of NTMs in markets with higher levels of lending competition.","PeriodicalId":331807,"journal":{"name":"Banking & Insurance eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131673952","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}
A special purpose acquisition company (SPAC) allows sponsors to directly access public capital markets to raise funds to conduct acquisitions. Traditionally, such sponsors would raise capital by first tapping private markets to initiate a venture capital (VC) or private equity (PE) fund. We present a model that lends itself to an evaluation of the traditional PE-to-IPO approach and SPAC financing. PE-to-IPO financing more efficiently separates high-quality from low-quality sponsors. SPAC financing more efficiently separates good acquisitions from bad acquisitions and therefore is the preferred mode of funding for firms subject to severe adverse selection. According to our model, the recent rise of intangible assets and technology companies can explain the increased use of SPAC financing.
{"title":"PE for the Public: The Rise of SPACs","authors":"S. Gryglewicz, Barney Hartman-Glaser, S. Mayer","doi":"10.2139/ssrn.3947368","DOIUrl":"https://doi.org/10.2139/ssrn.3947368","url":null,"abstract":"A special purpose acquisition company (SPAC) allows sponsors to directly access public capital markets to raise funds to conduct acquisitions. Traditionally, such sponsors would raise capital by first tapping private markets to initiate a venture capital (VC) or private equity (PE) fund. We present a model that lends itself to an evaluation of the traditional PE-to-IPO approach and SPAC financing. PE-to-IPO financing more efficiently separates high-quality from low-quality sponsors. SPAC financing more efficiently separates good acquisitions from bad acquisitions and therefore is the preferred mode of funding for firms subject to severe adverse selection. According to our model, the recent rise of intangible assets and technology companies can explain the increased use of SPAC financing.","PeriodicalId":331807,"journal":{"name":"Banking & Insurance eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124776397","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 examines a fundamental failure of process in lawmaking: the overreliance of lawmakers on economists and economic scholarship when designing and implementing financial regulation, to the virtual exclusion of lawyers and legal scholarship. This overreliance leads to regulation that often is based on theoretical models and assumptions that are poorly informed by experience and may not withstand real-world testing. The article examines how to improve financial regulation by better integrating legal scholarship and lawyerly insights into the lawmaking process. Among other things, that will require law professors to gain the attention, and earn the respect, of lawmakers by writing more reality-based articles and publishing them not only in traditional law reviews but also in more accessible formats and policy-oriented forums.
{"title":"Lawmaking Without Law: How Overreliance on Economics Fails Financial Regulation","authors":"S. Schwarcz, Theodore L. Leonhardt","doi":"10.2139/ssrn.3942767","DOIUrl":"https://doi.org/10.2139/ssrn.3942767","url":null,"abstract":"This article examines a fundamental failure of process in lawmaking: the overreliance of lawmakers on economists and economic scholarship when designing and implementing financial regulation, to the virtual exclusion of lawyers and legal scholarship. This overreliance leads to regulation that often is based on theoretical models and assumptions that are poorly informed by experience and may not withstand real-world testing. The article examines how to improve financial regulation by better integrating legal scholarship and lawyerly insights into the lawmaking process. Among other things, that will require law professors to gain the attention, and earn the respect, of lawmakers by writing more reality-based articles and publishing them not only in traditional law reviews but also in more accessible formats and policy-oriented forums.","PeriodicalId":331807,"journal":{"name":"Banking & Insurance eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115846196","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 provides new evidence of agency conflicts in securitization by documenting adverse selection in Ginnie Mae issuers' early buyout activity. Conditioning on delinquency, we find issuers buy out less risky loans with higher interest rate spreads. We illustrate not only how information asymmetries arise during the loan servicing process but also how issuers exploit them in their early buyout decisions. Unlike prior studies examining information asymmetries introduced by the securitization process, we employ unique data on a subset of early buyout loans that directly observes the soft information collected by issuers.
{"title":"Agency Conflicts in Securitization: Evidence From Ginnie Mae Early Buyouts","authors":"A. Bandyopadhyay, Dongshin Kim, Patrick S. Smith","doi":"10.2139/ssrn.3869192","DOIUrl":"https://doi.org/10.2139/ssrn.3869192","url":null,"abstract":"This paper provides new evidence of agency conflicts in securitization by documenting adverse selection in Ginnie Mae issuers' early buyout activity. Conditioning on delinquency, we find issuers buy out less risky loans with higher interest rate spreads. We illustrate not only how information asymmetries arise during the loan servicing process but also how issuers exploit them in their early buyout decisions. Unlike prior studies examining information asymmetries introduced by the securitization process, we employ unique data on a subset of early buyout loans that directly observes the soft information collected by issuers.","PeriodicalId":331807,"journal":{"name":"Banking & Insurance eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120969600","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}
Using natural disasters as exogenous shocks to the peer-to-peer (P2P) loan market, we document a local increase in loan demand post-disaster. Interest rates and delinquencies from loans approved during this demand shock are similar to pre-event levels. Loans allocated prior to a disaster are more likely to suffer delinquency over the life of the loan, but loans granted a hardship accommodation delay of payment reduce the likelihood of future delinquency providing relief to borrowers and reduced delinquency costs to investors. Contrary to regulatory concerns that P2P lending is predatory, our results suggest they provide positive social welfare benefits.
{"title":"The social welfare of marketplace lending: Evidence from natural disasters","authors":"D. Bradley, Matthew Henriksson, Sarath Valsalan","doi":"10.2139/ssrn.3940557","DOIUrl":"https://doi.org/10.2139/ssrn.3940557","url":null,"abstract":"Using natural disasters as exogenous shocks to the peer-to-peer (P2P) loan market, we document a local increase in loan demand post-disaster. Interest rates and delinquencies from loans approved during this demand shock are similar to pre-event levels. Loans allocated prior to a disaster are more likely to suffer delinquency over the life of the loan, but loans granted a hardship accommodation delay of payment reduce the likelihood of future delinquency providing relief to borrowers and reduced delinquency costs to investors. Contrary to regulatory concerns that P2P lending is predatory, our results suggest they provide positive social welfare benefits.","PeriodicalId":331807,"journal":{"name":"Banking & Insurance eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131295174","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 use machine learning methods for modeling multi-period corporate default probabilities and obtain higher prediction accuracy compared to linear models with the differences being larger for longer prediction horizons. Overall, tree-boosting has the highest prediction accuracy. In addition, we introduce a novel hybrid econometric-machine learning model combining tree-boosting with a latent frailty model. This ``LaGaBoost frailty model" results in more accurate predictions of upper tails of portfolio losses compared to both a linear frailty model and machine learning methods ignoring frailty correlation. We also investigate the reasons and find various explanations for the observed differences in prediction accuracy.
{"title":"Machine Learning for Corporate Default Risk: Multi-Period Prediction, Frailty Correlation, Loan Portfolios, and Tail Probabilities","authors":"Fabio Sigrist, N. Leuenberger","doi":"10.2139/ssrn.3938972","DOIUrl":"https://doi.org/10.2139/ssrn.3938972","url":null,"abstract":"We use machine learning methods for modeling multi-period corporate default probabilities and obtain higher prediction accuracy compared to linear models with the differences being larger for longer prediction horizons. Overall, tree-boosting has the highest prediction accuracy. In addition, we introduce a novel hybrid econometric-machine learning model combining tree-boosting with a latent frailty model. This ``LaGaBoost frailty model\" results in more accurate predictions of upper tails of portfolio losses compared to both a linear frailty model and machine learning methods ignoring frailty correlation. We also investigate the reasons and find various explanations for the observed differences in prediction accuracy.","PeriodicalId":331807,"journal":{"name":"Banking & Insurance eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116366715","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 analyze competition in the consumer lending segment between banks and financial technology (or “fintech”) companies (or “fintechs”) as well as giant technology (or “bigtech”) companies (or “bigtechs”) providing alternative credit. We use a database combining banklevel characteristics and country-level proxies for 72 countries during 2013–2018. We find that in developed markets, the relations between fintech/bigtech credit providers and banks are similar and competitive in nature. However, banks’ consumer lending grows simultaneously with fintech credit market development in emerging economies but decreases in the aftermath of bigtech credit emergence. Fintech credit seems to penetrate market segments not serviced by banks; thus, it plays a complementary role, but only in emerging economies. Bigtechs compete even more with banks and push some banking offers out of the market, both in emerging and developed economies. Furthermore, we show that domestic and privately owned banks are more negatively affected by competition from technology-based lending, particularly bigtech, compared to foreign banks. Thus, bigtech lending may be treated as a serious competition for banks’ relationship lending, based on soft credit information processing, provisioned traditionally by local banks.
{"title":"Banks’ consumer lending reaction to fintech and bigtech credit emergence in the context of soft versus hard credit information processing","authors":"Oskar Kowalewski, Paweł Pisany","doi":"10.2139/ssrn.3935800","DOIUrl":"https://doi.org/10.2139/ssrn.3935800","url":null,"abstract":"We analyze competition in the consumer lending segment between banks and financial technology (or “fintech”) companies (or “fintechs”) as well as giant technology (or “bigtech”) companies (or “bigtechs”) providing alternative credit. We use a database combining banklevel characteristics and country-level proxies for 72 countries during 2013–2018. We find that in developed markets, the relations between fintech/bigtech credit providers and banks are similar and competitive in nature. However, banks’ consumer lending grows simultaneously with fintech credit market development in emerging economies but decreases in the aftermath of bigtech credit emergence. Fintech credit seems to penetrate market segments not serviced by banks; thus, it plays a complementary role, but only in emerging economies. Bigtechs compete even more with banks and push some banking offers out of the market, both in emerging and developed economies. Furthermore, we show that domestic and privately owned banks are more negatively affected by competition from technology-based lending, particularly bigtech, compared to foreign banks. Thus, bigtech lending may be treated as a serious competition for banks’ relationship lending, based on soft credit information processing, provisioned traditionally by local banks.","PeriodicalId":331807,"journal":{"name":"Banking & Insurance eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131370447","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}
Up to this point, the literature on the issuance of convertible bonds has neglected financial institutions. Contrary to firms, banks not only can issue convertible bonds but also, after the subprime crises, contingent convertible (CoCo) bonds emerged as an alternative. Hence, the purpose of this study is threefold: first, we expand the literature on the motivation to issue convertible bonds in the banking sector; second, we introduce a new proxy (Loans-Deposits Flow) to measure the reinvestment in this sector; and third, we analyze the differences in the motivation for issuing CoCo bonds when compared to convertible bonds. Our results show that the theory of sequential financing is not confirmed for CoCo bonds in the banking sector. Additionally, we provide evidence that banks issue CoCo bonds for regulatory purposes (to increase their capital), while convertibles are issued to allow banks to expand their investments and loan portfolios. The results are robust to several specifications including a propensity-score matching and a difference-in-difference analysis.
{"title":"Hybrid Bonds as a strategy of sequential finance in the banking sector","authors":"J. Fajardo, L. Mendes, R. Leite","doi":"10.2139/ssrn.3933950","DOIUrl":"https://doi.org/10.2139/ssrn.3933950","url":null,"abstract":"Up to this point, the literature on the issuance of convertible bonds has neglected financial institutions. Contrary to firms, banks not only can issue convertible bonds but also, after the subprime crises, contingent convertible (CoCo) bonds emerged as an alternative. Hence, the purpose of this study is threefold: first, we expand the literature on the motivation to issue convertible bonds in the banking sector; second, we introduce a new proxy (Loans-Deposits Flow) to measure the reinvestment in this sector; and third, we analyze the differences in the motivation for issuing CoCo bonds when compared to convertible bonds. Our results show that the theory of sequential financing is not confirmed for CoCo bonds in the banking sector. Additionally, we provide evidence that banks issue CoCo bonds for regulatory purposes (to increase their capital), while convertibles are issued to allow banks to expand their investments and loan portfolios. The results are robust to several specifications including a propensity-score matching and a difference-in-difference analysis.","PeriodicalId":331807,"journal":{"name":"Banking & Insurance eJournal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134193925","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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