In this study, we analyze the trading behavior of banks with lending relationships. We combine detailed German data on banks’ proprietary trading and market making with lending information from the credit register and then examine how banks trade stocks of their borrowers around important corporate events. We find that banks trade more frequently and also profitably ahead of events when they are the main lender (or relationship bank) for the borrower. Specifically, we show that relationship banks are more likely to build up positive (negative) trading positions in the two weeks before positive (negative) news events, and also that they unwind these positions shortly after the event. This trading pattern is more pronounced for unscheduled earnings events, M&A transactions, and after borrower obtain new bank loans. Our results suggest that lending relationships endow banks with important information, highlighting the potential for conflicts of interest in banking, which has been a prominent concern in the regulatory debate.
{"title":"Know Your Customer: Relationship Lending and Bank Trading","authors":"R. Haselmann, C. Leuz, S. Schreiber","doi":"10.2139/ssrn.3903968","DOIUrl":"https://doi.org/10.2139/ssrn.3903968","url":null,"abstract":"In this study, we analyze the trading behavior of banks with lending relationships. We combine detailed German data on banks’ proprietary trading and market making with lending information from the credit register and then examine how banks trade stocks of their borrowers around important corporate events. We find that banks trade more frequently and also profitably ahead of events when they are the main lender (or relationship bank) for the borrower. Specifically, we show that relationship banks are more likely to build up positive (negative) trading positions in the two weeks before positive (negative) news events, and also that they unwind these positions shortly after the event. This trading pattern is more pronounced for unscheduled earnings events, M&A transactions, and after borrower obtain new bank loans. Our results suggest that lending relationships endow banks with important information, highlighting the potential for conflicts of interest in banking, which has been a prominent concern in the regulatory debate.","PeriodicalId":251522,"journal":{"name":"Risk Management & Analysis in Financial Institutions eJournal","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131916668","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}
Firms with similar credit ratings, especially junk-rated ones, tend to comove strongly in stock returns with each other, which is not fully explained by their exposures to systematic factors. Following a firm’s downgrade into the junk-grade group, it tends to comove much more strongly in stock returns with firms in the junk-grade group and less with those in the investment-grade group. There is no similar trend in comovement with either credit rating group in the one-year window prior to the downgrade, indicating that changes in comovement are unlikely driven by changes in fundamentals of affected firms. Finally, we find evidence consistent with the investor clientele effect explanation for excessive comovement related to credit ratings by examining a) how mutual funds with different credit preferences adjust their stock holdings of firms being downgraded into junk-grade ratings and b) how flows to mutual funds that tend to invest in junk-rated firms affect these firms’ stock returns and their comovement.
{"title":"Credit Rating and Stock Return Comovement","authors":"Jianfeng Shen, Huiping Zhang, Weiqi Zhang","doi":"10.2139/ssrn.3801282","DOIUrl":"https://doi.org/10.2139/ssrn.3801282","url":null,"abstract":"Firms with similar credit ratings, especially junk-rated ones, tend to comove strongly in stock returns with each other, which is not fully explained by their exposures to systematic factors. Following a firm’s downgrade into the junk-grade group, it tends to comove much more strongly in stock returns with firms in the junk-grade group and less with those in the investment-grade group. There is no similar trend in comovement with either credit rating group in the one-year window prior to the downgrade, indicating that changes in comovement are unlikely driven by changes in fundamentals of affected firms. Finally, we find evidence consistent with the investor clientele effect explanation for excessive comovement related to credit ratings by examining a) how mutual funds with different credit preferences adjust their stock holdings of firms being downgraded into junk-grade ratings and b) how flows to mutual funds that tend to invest in junk-rated firms affect these firms’ stock returns and their comovement.","PeriodicalId":251522,"journal":{"name":"Risk Management & Analysis in Financial Institutions eJournal","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115145161","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}
One prominent aspect of the MiFID II regulation that became effective in Europe in 2018 is the unbundling of research and execution costs. We exploit the early adoption of this rule in Sweden already in 2016 to provide early evidence on the implications for fund investors. Using a diff-in-diff framework and mostly hand-collected data on bundled and unbundled commissions, we find basically no impact of the regulation on fund investors: neither total expense ratios nor fund performance changed in response to the unbundling. We also fail to document any information gains for investors’ fund selection process from the increased transparency of observing execution and research costs separately. Overall, we are skeptical that the unbundling of commissions has had any positive impact on fund investors.
{"title":"The Unbundling of Mutual Funds’ Trading and Research Commissions: Have Investors Benefited?","authors":"Emelie Fröberg, M. Halling","doi":"10.2139/ssrn.3892441","DOIUrl":"https://doi.org/10.2139/ssrn.3892441","url":null,"abstract":"One prominent aspect of the MiFID II regulation that became effective in Europe in 2018 is the unbundling of research and execution costs. We exploit the early adoption of this rule in Sweden already in 2016 to provide early evidence on the implications for fund investors. Using a diff-in-diff framework and mostly hand-collected data on bundled and unbundled commissions, we find basically no impact of the regulation on fund investors: neither total expense ratios nor fund performance changed in response to the unbundling. We also fail to document any information gains for investors’ fund selection process from the increased transparency of observing execution and research costs separately. Overall, we are skeptical that the unbundling of commissions has had any positive impact on fund investors.","PeriodicalId":251522,"journal":{"name":"Risk Management & Analysis in Financial Institutions eJournal","volume":"59 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114112021","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 how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of the conditional Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), which predicts an average maximum of 5.6 percent of risk premium in REIT equity return. In subsample analyses, the positive relationship is not affected by sample periods' choice but shows higher housing risk beta values for the 2009-18 sample period. The relationship remains significant after controlling for VIX, Fama-French three factors, and a broad set of macroeconomic and financial variables. Moreover, the proposed housing beta also accurately forecasts U.S. macroeconomic and financial conditions.
{"title":"Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return","authors":"Masudul Alam","doi":"10.2139/ssrn.3893131","DOIUrl":"https://doi.org/10.2139/ssrn.3893131","url":null,"abstract":"This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of the conditional Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), which predicts an average maximum of 5.6 percent of risk premium in REIT equity return. In subsample analyses, the positive relationship is not affected by sample periods' choice but shows higher housing risk beta values for the 2009-18 sample period. The relationship remains significant after controlling for VIX, Fama-French three factors, and a broad set of macroeconomic and financial variables. Moreover, the proposed housing beta also accurately forecasts U.S. macroeconomic and financial conditions.","PeriodicalId":251522,"journal":{"name":"Risk Management & Analysis in Financial Institutions eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114519096","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 discusses the tools and techniques for risk mitigation in Life Insurance
本文讨论了在人寿保险中降低风险的工具和技术
{"title":"Risk Management in Life Insurance","authors":"Sonjai Kumar","doi":"10.2139/ssrn.3888615","DOIUrl":"https://doi.org/10.2139/ssrn.3888615","url":null,"abstract":"This paper discusses the tools and techniques for risk mitigation in Life Insurance","PeriodicalId":251522,"journal":{"name":"Risk Management & Analysis in Financial Institutions eJournal","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123904477","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}
Hitesh Doshi, Jan Ericsson, Mathieu Fournier, S. Seo
We evaluate the empirical validity of the compound option framework. In a model where corporate securities are options on a firm's assets, option contracts on these can be viewed as options on options, or compound options. We estimate a model with priced asset variance risk and find that it jointly explains the level and time variation of both equity index (SPX) and credit index (CDX) option prices well out-of-sample. This suggests that the two options markets are priced consistently, contrary to recent findings. We show that variance risk is important for establishing pricing consistency between equity, credit, and related derivatives.
{"title":"Asset Variance Risk and Compound Option Prices","authors":"Hitesh Doshi, Jan Ericsson, Mathieu Fournier, S. Seo","doi":"10.2139/ssrn.3885357","DOIUrl":"https://doi.org/10.2139/ssrn.3885357","url":null,"abstract":"We evaluate the empirical validity of the compound option framework. In a model where corporate securities are options on a firm's assets, option contracts on these can be viewed as options on options, or compound options. We estimate a model with priced asset variance risk and find that it jointly explains the level and time variation of both equity index (SPX) and credit index (CDX) option prices well out-of-sample. This suggests that the two options markets are priced consistently, contrary to recent findings. We show that variance risk is important for establishing pricing consistency between equity, credit, and related derivatives.<br>","PeriodicalId":251522,"journal":{"name":"Risk Management & Analysis in Financial Institutions eJournal","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122746291","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 is the first to examine whether the performance of credit rating, one of the most important data-based decision-making of banks, can be improved by using system log data that is extensively accumulated inside the bank for system operation. This study uses the log data recorded for the mobile app system of Kakaobank, a leading internet bank used by more than 14 million people in Korea. After generating candidate variables from Kakaobank's vast log data, we develop a credit scoring model by utilizing variables with high information values. Consequently, the discrimination power of the new model compared to the credit bureau grades was improved by 2.4% points based on the K-S statistics. Therefore, the results of this study imply that if a bank utilizes its log data that have already been extensively accumulated inside the bank, decision-making systems, including credit scoring, can be efficiently improved at a low cost.
{"title":"Credit scoring using system log data in the internet bank","authors":"S. Kyeong, Daehee Kim, Jinho Shin","doi":"10.2139/ssrn.3910199","DOIUrl":"https://doi.org/10.2139/ssrn.3910199","url":null,"abstract":"This study is the first to examine whether the performance of credit rating, one of the most important data-based decision-making of banks, can be improved by using system log data that is extensively accumulated inside the bank for system operation. This study uses the log data recorded for the mobile app system of Kakaobank, a leading internet bank used by more than 14 million people in Korea. After generating candidate variables from Kakaobank's vast log data, we develop a credit scoring model by utilizing variables with high information values. Consequently, the discrimination power of the new model compared to the credit bureau grades was improved by 2.4% points based on the K-S statistics. Therefore, the results of this study imply that if a bank utilizes its log data that have already been extensively accumulated inside the bank, decision-making systems, including credit scoring, can be efficiently improved at a low cost.","PeriodicalId":251522,"journal":{"name":"Risk Management & Analysis in Financial Institutions eJournal","volume":"540 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124532748","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 presents a new mechanism through which the geography of bank deposits increases financial fragility. We document the within-bank geographic concentration of deposits -- 30% of bank deposits are concentrated in a single county. We combine this within-bank geographic concentration of deposits with local natural-disaster-induced property damages to construct novel bank deposit shocks. On aggregate, these shocks can explain 3.30% of variation in economic growth. Local disaster shocks result in aggregate fluctuations through their effect on deposits, which negatively affect bank lending. Financial frictions such as regulatory constraints, informational advantages, and borrower constraints are critical for the aggregation of shocks.
{"title":"The Deposits Channel of Aggregate Fluctuations","authors":"Shohini Kundu, Seongjin Park, Nishant Vats","doi":"10.2139/ssrn.3883605","DOIUrl":"https://doi.org/10.2139/ssrn.3883605","url":null,"abstract":"This paper presents a new mechanism through which the geography of bank deposits increases financial fragility. We document the within-bank geographic concentration of deposits -- 30% of bank deposits are concentrated in a single county. We combine this within-bank geographic concentration of deposits with local natural-disaster-induced property damages to construct novel bank deposit shocks. On aggregate, these shocks can explain 3.30% of variation in economic growth. Local disaster shocks result in aggregate fluctuations through their effect on deposits, which negatively affect bank lending. Financial frictions such as regulatory constraints, informational advantages, and borrower constraints are critical for the aggregation of shocks.","PeriodicalId":251522,"journal":{"name":"Risk Management & Analysis in Financial Institutions eJournal","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122801731","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}
Maxime Bergeron, Ryan Ferguson, V. Lucic, Ivan Sergienko
Inspired by initially proposed IBOR fallback mechanisms, we show how deep learning can be used to quickly and accurately compute the {expected median} of a time series at future inference dates with varying amounts of observed data. While the IBOR fallback spreads were ultimately fixed, the technique outlined here showcases the ability of neural networks to tackle financial problems over seemingly impossibly large domains.
{"title":"LIBOR Prompts Quantile Leap: Machine Learning for Quantile Derivatives","authors":"Maxime Bergeron, Ryan Ferguson, V. Lucic, Ivan Sergienko","doi":"10.2139/ssrn.3882160","DOIUrl":"https://doi.org/10.2139/ssrn.3882160","url":null,"abstract":"Inspired by initially proposed IBOR fallback mechanisms, we show how deep learning can be used to quickly and accurately compute the {expected median} of a time series at future inference dates with varying amounts of observed data. While the IBOR fallback spreads were ultimately fixed, the technique outlined here showcases the ability of neural networks to tackle financial problems over seemingly impossibly large domains.","PeriodicalId":251522,"journal":{"name":"Risk Management & Analysis in Financial Institutions eJournal","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133061718","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}
Mikica Drenovak, V. Rankovic, B. Urosevic, R. Jelic
Abstract We develop a novel Mean-Max Drawdown portfolio optimization approach using buy-and-hold portfolios. The optimization is performed utilizing a multi-objective evolutionary algorithm on a sample of S&P 100 constituents. Our optimization procedure provides portfolios with better Mean-Max Drawdown trade-offs compared to relevant benchmarks, regardless of the selected subsamples and market conditions. The superior performance of our approach is particularly pronounced in periods with reversing market trends (i.e. a market rally and a fall in the same subsample).
{"title":"Mean-Maximum Drawdown Optimization of Buy-and-Hold Portfolios Using a Multi-objective Evolutionary Algorithm","authors":"Mikica Drenovak, V. Rankovic, B. Urosevic, R. Jelic","doi":"10.2139/ssrn.3892289","DOIUrl":"https://doi.org/10.2139/ssrn.3892289","url":null,"abstract":"Abstract We develop a novel Mean-Max Drawdown portfolio optimization approach using buy-and-hold portfolios. The optimization is performed utilizing a multi-objective evolutionary algorithm on a sample of S&P 100 constituents. Our optimization procedure provides portfolios with better Mean-Max Drawdown trade-offs compared to relevant benchmarks, regardless of the selected subsamples and market conditions. The superior performance of our approach is particularly pronounced in periods with reversing market trends (i.e. a market rally and a fall in the same subsample).","PeriodicalId":251522,"journal":{"name":"Risk Management & Analysis in Financial Institutions eJournal","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132903386","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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