Drawing on an institutional theoretical perspective, we investigated the impact of the origins of organizational legitimacy on systematic risk using a sample of 358 Brazilian companies between the years 2002 and 2007. We regard three origins of legitimacy – formal-regulatory (presence in premium listings), cultural-cognitive (board of directors), and normative (reputation) legitimacy – to empirically investigate how the company's size and adherence to premium lists moderate other sources of legitimacy. Our results indicate that only under apparently better-quality corporate governance conditions – presence in premium listings – corporate reputation and the board of directors reduce systematic risk. In addition, we show that the effect of reputation on risk is positively moderated by firm size.
{"title":"How Does Legitimacy Operate in Emerging Capital Markets? Investigating the Moderating Effects of Premium Listings and Firm Size on Risk","authors":"Luciano Rossoni, Wesley Mendes-da-Silva","doi":"10.2139/ssrn.2838983","DOIUrl":"https://doi.org/10.2139/ssrn.2838983","url":null,"abstract":"Drawing on an institutional theoretical perspective, we investigated the impact of the origins of organizational legitimacy on systematic risk using a sample of 358 Brazilian companies between the years 2002 and 2007. We regard three origins of legitimacy – formal-regulatory (presence in premium listings), cultural-cognitive (board of directors), and normative (reputation) legitimacy – to empirically investigate how the company's size and adherence to premium lists moderate other sources of legitimacy. Our results indicate that only under apparently better-quality corporate governance conditions – presence in premium listings – corporate reputation and the board of directors reduce systematic risk. In addition, we show that the effect of reputation on risk is positively moderated by firm size.","PeriodicalId":181062,"journal":{"name":"Corporate Governance: Disclosure","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134464963","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 analyses how board classification, board independence, and inside ownership affects US oil-company performance using the oil price collapse of the autumn and winter of 2014 as a natural experiment. Firms with classified boards suffered during the collapse. An important source of value destruction is that classified boards aggravated the impact of corporate risk taking on performance. On the contrary, the greater the ownership level of insiders, the better the firm sustained the crisis. The performance-ownership relationship seems to be non-monotonic. In particular, inside ownership mediates the impact of leverage on performance. As for board independence, it seems to be of no relevance to firm performance.
{"title":"Corporate Governance and Firm Performance: Evidence from the Oil Price Collapse of 2014-15","authors":"Niclas Andrén","doi":"10.2139/ssrn.2835290","DOIUrl":"https://doi.org/10.2139/ssrn.2835290","url":null,"abstract":"This paper analyses how board classification, board independence, and inside ownership affects US oil-company performance using the oil price collapse of the autumn and winter of 2014 as a natural experiment. Firms with classified boards suffered during the collapse. An important source of value destruction is that classified boards aggravated the impact of corporate risk taking on performance. On the contrary, the greater the ownership level of insiders, the better the firm sustained the crisis. The performance-ownership relationship seems to be non-monotonic. In particular, inside ownership mediates the impact of leverage on performance. As for board independence, it seems to be of no relevance to firm performance.","PeriodicalId":181062,"journal":{"name":"Corporate Governance: Disclosure","volume":"30 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130434648","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 analysis the effect of reporting quality on financing and investment. It is important for us to understand the relation among them in order to prepare Indonesian companies for ASEAN Economic Community in 2015. The study examines the effect of financial reporting quality on financing and investment of 15 Indonesian companies with large market capitalization based on the Standard and Poor’s Rating Services in its first survey of the major corporate credit trends in the Association of Southeast Asian Nations (ASEAN). Those companies may still be under-investing in relation to its regional peers. The results suggest that (1) financial reporting quality has negative effect on financing. (2) financial reporting quality has positive effect on investment among companies with higher likelihood of over-investing and negative effect on investment among those with higher likelihood of under-investing. DOI: 10.15408/etk.v16i1.4600
{"title":"The Effect of Financial Reporting Quality on Financing and Investment","authors":"Windy Angela, Rilya Aryancana","doi":"10.15408/ETK.V16I1.4600","DOIUrl":"https://doi.org/10.15408/ETK.V16I1.4600","url":null,"abstract":"This paper analysis the effect of reporting quality on financing and investment. It is important for us to understand the relation among them in order to prepare Indonesian companies for ASEAN Economic Community in 2015. The study examines the effect of financial reporting quality on financing and investment of 15 Indonesian companies with large market capitalization based on the Standard and Poor’s Rating Services in its first survey of the major corporate credit trends in the Association of Southeast Asian Nations (ASEAN). Those companies may still be under-investing in relation to its regional peers. The results suggest that (1) financial reporting quality has negative effect on financing. (2) financial reporting quality has positive effect on investment among companies with higher likelihood of over-investing and negative effect on investment among those with higher likelihood of under-investing. DOI: 10.15408/etk.v16i1.4600","PeriodicalId":181062,"journal":{"name":"Corporate Governance: Disclosure","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116420928","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 analyzes a model of investment and return in an economy characterized by information asymmetry between an investor and a manager. The realized value of the uncertain state of nature is the manager’s private information. The paper first considers an economy where the manager cannot share her private information with the investor. Therefore, dividend payment is the only reputation building tool available to the manager. If the investor’s prior beliefs about the manager’s trustworthiness are sufficiently high, then the manager will return a dividend consistent with the lower possible state of nature having occurred and the investor will revise such beliefs downwards. However, if the beliefs are not so high, then the equilibrium will be mixed strategies.
{"title":"Reputation Effects of Information Sharing","authors":"Radhika Lunawat","doi":"10.2139/ssrn.1508018","DOIUrl":"https://doi.org/10.2139/ssrn.1508018","url":null,"abstract":"This paper analyzes a model of investment and return in an economy characterized by information asymmetry between an investor and a manager. The realized value of the uncertain state of nature is the manager’s private information. The paper first considers an economy where the manager cannot share her private information with the investor. Therefore, dividend payment is the only reputation building tool available to the manager. If the investor’s prior beliefs about the manager’s trustworthiness are sufficiently high, then the manager will return a dividend consistent with the lower possible state of nature having occurred and the investor will revise such beliefs downwards. However, if the beliefs are not so high, then the equilibrium will be mixed strategies.","PeriodicalId":181062,"journal":{"name":"Corporate Governance: Disclosure","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128657254","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}
Purpose- The main purpose of this paper is to examine whether or not Bank Holding Companies (BHCs) took advantage of SFAS’s 133 differential treatment of the changes in the fair value of cash flow hedges to smooth earnings. Design/methodology/approach- The author used a causal-comparative research design featuring an investigation on the Income Smoothing effects of BHCs’ corporate use of derivatives designated as cash flow hedges and discretionary accruals one year after the 2008 amendment of SFAS 133. Findings- The results of this research showed that SFAS133-Accounting Hedgers had smoother earnings than SFAS133-Compliant Hedgers due to derivative use but did not take advantage of the differential treatment of cash flow hedges to manipulate earnings. This study suggests that hedge accounting rules under SFAS 133 fully determined the hedging behavior of SFAS-Accounting Hedgers . To ascertain the implementation of effective hedges SFAS-Accounting Hedgers captured the benefits of hedge accounting while compromised the economic benefits of hedging in an attempt to manage any associated accounting volatility and smooth earnings. Research limitations/implications- This study extends prior research on corporate risk management activities of BHCs and impacts social change by presenting new evidence on the effects of SFAS 133 cash-flow hedges on earnings smoothing. Practical implications- The evidence suggests that corporate governance mechanisms affect earnings management since BHCs withhold discretion with respect to the realization of gains and losses from derivative instruments designated as cash flow hedges. This is an indication that BHCs with an intent to achieve smoother earnings as a leading corporate risk management strategy, have a comparative advantage compared to non-financial institutions to apply hedge accounting since they regularly use derivatives and are more experienced with the implementation of SFAS 133. Originality/value- Although prior studies typically considered derivatives and accruals as substitute proxies in managing reported earnings, the paper’s results suggest that the most significant determinant of earnings smoothing is derivative use for SFAS-Accounting Hedgers and information asymmetry for SFAS133- Compliant Hedgers .
{"title":"The Earnings Smoothing Management Philosophy of BHCs in the SFAS - 133 Framework","authors":"Veliota Drakopoulou","doi":"10.5430/AFR.V5N3P64","DOIUrl":"https://doi.org/10.5430/AFR.V5N3P64","url":null,"abstract":"Purpose- The main purpose of this paper is to examine whether or not Bank Holding Companies (BHCs) took advantage of SFAS’s 133 differential treatment of the changes in the fair value of cash flow hedges to smooth earnings. Design/methodology/approach- The author used a causal-comparative research design featuring an investigation on the Income Smoothing effects of BHCs’ corporate use of derivatives designated as cash flow hedges and discretionary accruals one year after the 2008 amendment of SFAS 133. Findings- The results of this research showed that SFAS133-Accounting Hedgers had smoother earnings than SFAS133-Compliant Hedgers due to derivative use but did not take advantage of the differential treatment of cash flow hedges to manipulate earnings. This study suggests that hedge accounting rules under SFAS 133 fully determined the hedging behavior of SFAS-Accounting Hedgers . To ascertain the implementation of effective hedges SFAS-Accounting Hedgers captured the benefits of hedge accounting while compromised the economic benefits of hedging in an attempt to manage any associated accounting volatility and smooth earnings. Research limitations/implications- This study extends prior research on corporate risk management activities of BHCs and impacts social change by presenting new evidence on the effects of SFAS 133 cash-flow hedges on earnings smoothing. Practical implications- The evidence suggests that corporate governance mechanisms affect earnings management since BHCs withhold discretion with respect to the realization of gains and losses from derivative instruments designated as cash flow hedges. This is an indication that BHCs with an intent to achieve smoother earnings as a leading corporate risk management strategy, have a comparative advantage compared to non-financial institutions to apply hedge accounting since they regularly use derivatives and are more experienced with the implementation of SFAS 133. Originality/value- Although prior studies typically considered derivatives and accruals as substitute proxies in managing reported earnings, the paper’s results suggest that the most significant determinant of earnings smoothing is derivative use for SFAS-Accounting Hedgers and information asymmetry for SFAS133- Compliant Hedgers .","PeriodicalId":181062,"journal":{"name":"Corporate Governance: Disclosure","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121121900","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 apply modern machine learning techniques to characterize disclosure misclassification by public companies. We find that 12-25% of disclosures are misclassified; those concerning material definitive agreements, executive or director turnover, and delistings are most commonly misclassified. Using EDGAR search traffic data, we provide evidence that misclassification successfully reduces investor attention. Through this attention channel, misclassification leads to a significant and persistent impact on absolute market returns. For misclassified filings, search traffic is 4-12% lower and absolute market reactions are 46-79 bps smaller. Consistent with strategic motives, misclassification is more likely for negative news and when market attention is high.
{"title":"Strategic Disclosure Misclassification","authors":"Andrew Bird, S. Karolyi, Paul Ma","doi":"10.2139/ssrn.2778805","DOIUrl":"https://doi.org/10.2139/ssrn.2778805","url":null,"abstract":"We apply modern machine learning techniques to characterize disclosure misclassification by public companies. We find that 12-25% of disclosures are misclassified; those concerning material definitive agreements, executive or director turnover, and delistings are most commonly misclassified. Using EDGAR search traffic data, we provide evidence that misclassification successfully reduces investor attention. Through this attention channel, misclassification leads to a significant and persistent impact on absolute market returns. For misclassified filings, search traffic is 4-12% lower and absolute market reactions are 46-79 bps smaller. Consistent with strategic motives, misclassification is more likely for negative news and when market attention is high.","PeriodicalId":181062,"journal":{"name":"Corporate Governance: Disclosure","volume":"415 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115954145","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 is the result of over twelve months of research, primarily based on direct interviews with experienced chairmen and chairwomen. Evidence shows that when damaging crises hit companies this is frequently due to directors’ “risk blindness”, as a result of failure by boards of directors to have governed risks appropriately. Our hypothesis was that the influence of these two key roles, chairman and CEO, and especially the relationship between them, would be a significant factor in how effective boards would be in avoiding risk blindness and successfully governing strategic risk.As our work progressed, we learned much about the Chair-CEO relationship, including how that relationship needs to be built, how it can be destroyed, and the impact on boards when that occurs. We discovered that risk, especially strategic risk, the degree of trust between the Chair and CEO, and time-related issues were all closely interwoven. We also learned of the natural lifecycle in this critical relationship that in its development and ending can damage board effectiveness, and which poses a paradox for board chairs in fulfilling their critical role.
{"title":"The Chairman and the CEO: The Bearing Point or Odd Couple?","authors":"T. Coyne, N. Britten","doi":"10.2139/ssrn.2804054","DOIUrl":"https://doi.org/10.2139/ssrn.2804054","url":null,"abstract":"This paper is the result of over twelve months of research, primarily based on direct interviews with experienced chairmen and chairwomen. Evidence shows that when damaging crises hit companies this is frequently due to directors’ “risk blindness”, as a result of failure by boards of directors to have governed risks appropriately. Our hypothesis was that the influence of these two key roles, chairman and CEO, and especially the relationship between them, would be a significant factor in how effective boards would be in avoiding risk blindness and successfully governing strategic risk.As our work progressed, we learned much about the Chair-CEO relationship, including how that relationship needs to be built, how it can be destroyed, and the impact on boards when that occurs. We discovered that risk, especially strategic risk, the degree of trust between the Chair and CEO, and time-related issues were all closely interwoven. We also learned of the natural lifecycle in this critical relationship that in its development and ending can damage board effectiveness, and which poses a paradox for board chairs in fulfilling their critical role.","PeriodicalId":181062,"journal":{"name":"Corporate Governance: Disclosure","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128946159","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}
On May 8, 2007, the largest trading loss thus far reported in Sweden was announced by Carnegie Investment Bank, caused by a revaluation of the bank’s options portfolio. Among consequences, the bank’s CEO was forced to resign, the bank was fined a maximum penalty from the Swedish FSA, and three option traders were accused of market price manipulations and exploitation of inefficient internal procedures. Using unique data from the Swedish Economic Crimes Bureau, this paper provides a hands-on account of how market price were dislocated and shows the actual trading patterns behind the dislocations. Internal procedures at the bank failed and the paper provides some important lessons regarding operational risk management practices.
{"title":"Price Dislocations and Risk Management: Lessons from a Large Options Trading Loss","authors":"B. Pramborg, Anders Stenkrona","doi":"10.2139/ssrn.2797870","DOIUrl":"https://doi.org/10.2139/ssrn.2797870","url":null,"abstract":"On May 8, 2007, the largest trading loss thus far reported in Sweden was announced by Carnegie Investment Bank, caused by a revaluation of the bank’s options portfolio. Among consequences, the bank’s CEO was forced to resign, the bank was fined a maximum penalty from the Swedish FSA, and three option traders were accused of market price manipulations and exploitation of inefficient internal procedures. Using unique data from the Swedish Economic Crimes Bureau, this paper provides a hands-on account of how market price were dislocated and shows the actual trading patterns behind the dislocations. Internal procedures at the bank failed and the paper provides some important lessons regarding operational risk management practices.","PeriodicalId":181062,"journal":{"name":"Corporate Governance: Disclosure","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114066780","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 the use of machine learning techniques into building statistically stable systematic allocation strategies. Traditionally, allocation processes usually rely on variations of Markowitz framework such as Mean Variance allocation, Maximum Diversity, Risk Allocation , Value at Risk, Expected Shortfall, in other words convex frontier optimization. Although those methods show some efficiency to allocate assets through the convex efficient frontier, they usually rely deeply on the estimation and the usage of the covariance matrix. Being no stationary and having multiple range memory (ie FIGARCH using Fractional Brownian Motion), the statistical estimation of covariance may lead to biases and errors and in the end, bias conclusions. Very extensive literature in econo-metrics, econo-physics, quantitative allocation cover this problem in order to remedy to the statistical estimation of covariance and his bias and issues.Here, our emphasis is not a new estimator of the covariance matrix, or a variant of Mean Variance framework but an application of Machine Learning techniques to infer no-linear relationships and long range memory between the assets.It has the advantage to remove the linear projection of the assets onto the covariance framework and then capture no-linear relationships between at various time periods.Recent advances in Neural Network, Deep Learning and Machine Learning allows a more efficient modeling of the no-linear statistical relationships between data (ie price, dividends,...). Among them, we can mention Restricted Boltzman Machines, Variationnal Auto-encoders and variations of Recurrent Neural Network, Attention and Highway Long Short Term Memory as well as Factorization Machines for projection on local sub-spaces.Thus, we investigate some of the techniques to develop practical systematic allocation strategies by reducing risks and estimations biases and show the results.
我们研究使用机器学习技术来构建统计稳定的系统分配策略。传统的分配过程通常依赖于Markowitz框架的变体，如Mean Variance allocation、Maximum Diversity、Risk allocation、Value at Risk、Expected short，即凸边界优化。尽管这些方法通过凸有效边界显示出一定的资产配置效率，但它们通常严重依赖于协方差矩阵的估计和使用。由于协方差的统计估计不具有平稳性，并且具有多范围记忆(即使用分数阶布朗运动的FIGARCH)，因此可能导致偏倚和误差，最终得出偏倚结论。为了弥补协方差的统计估计及其偏差和问题，在经济计量学、经济物理学、定量分配等方面都有大量的文献涉及到这个问题。在这里，我们的重点不是协方差矩阵的新估计器，也不是均值方差框架的变体，而是机器学习技术的应用，以推断资产之间的非线性关系和长期记忆。它的优点是将资产的线性投影移到协方差框架上，然后捕获不同时间段之间的非线性关系。神经网络、深度学习和机器学习的最新进展允许对数据之间的非线性统计关系(如价格、股息等)进行更有效的建模。其中，我们可以提到限制玻尔兹曼机，变数自编码器和递归神经网络的变体，注意和高速公路长短期记忆，以及局部子空间上投影的分解机。因此，我们研究了一些技术，通过减少风险和估计偏差来开发实用的系统配置策略，并展示了结果。
{"title":"Application of Machine Learning to Systematic Strategies","authors":"Kevin Noel","doi":"10.2139/ssrn.2837664","DOIUrl":"https://doi.org/10.2139/ssrn.2837664","url":null,"abstract":"We investigate the use of machine learning techniques into building statistically stable systematic allocation strategies. Traditionally, allocation processes usually rely on variations of Markowitz framework such as Mean Variance allocation, Maximum Diversity, Risk Allocation , Value at Risk, Expected Shortfall, in other words convex frontier optimization. Although those methods show some efficiency to allocate assets through the convex efficient frontier, they usually rely deeply on the estimation and the usage of the covariance matrix. Being no stationary and having multiple range memory (ie FIGARCH using Fractional Brownian Motion), the statistical estimation of covariance may lead to biases and errors and in the end, bias conclusions. Very extensive literature in econo-metrics, econo-physics, quantitative allocation cover this problem in order to remedy to the statistical estimation of covariance and his bias and issues.Here, our emphasis is not a new estimator of the covariance matrix, or a variant of Mean Variance framework but an application of Machine Learning techniques to infer no-linear relationships and long range memory between the assets.It has the advantage to remove the linear projection of the assets onto the covariance framework and then capture no-linear relationships between at various time periods.Recent advances in Neural Network, Deep Learning and Machine Learning allows a more efficient modeling of the no-linear statistical relationships between data (ie price, dividends,...). Among them, we can mention Restricted Boltzman Machines, Variationnal Auto-encoders and variations of Recurrent Neural Network, Attention and Highway Long Short Term Memory as well as Factorization Machines for projection on local sub-spaces.Thus, we investigate some of the techniques to develop practical systematic allocation strategies by reducing risks and estimations biases and show the results.","PeriodicalId":181062,"journal":{"name":"Corporate Governance: Disclosure","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115282395","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 provide evidence that the determinants of the primary loan loss indicators reported in financial reports — non-performing loans, the allowance and provision for loan losses, and net loan charge-offs — vary dramatically across real estate, commercial, and consumer loans, because these loan types differ in their homogeneity and collateralization and thus in the measurement of incurred losses under GAAP. Extending Wahlen (1994), we develop and estimate models of the non-discretionary and discretionary determinants of these loan loss indicators by loan type. The estimations indicate that banks’ exercise of discretion over provisions for loan losses is largely limited to heterogeneous commercial loans, a small slice of banks’ loan portfolios, and they provide many insights into the bank-specific and macroeconomic drivers of banks’ loan loss accruals. To demonstrate the increased statistical power and construct validity that results from conducting research on banks’ loan loss accruals by loan type, we show that this approach significantly improves the accuracy of out-of-sample predictions of future net loan charge-offs, more so for samples of banks whose loan portfolio composition varies more from that of the average bank. Our results illustrate the usefulness of disaggregated disclosures of loan loss indicators by loan type for future accounting research.
{"title":"Using Loan Loss Indicators by Loan Type to Sharpen the Evaluation of Banks’ Loan Loss Accruals","authors":"G. Bhat, Joshua A. Lee, Stephen G. Ryan","doi":"10.2139/ssrn.2490670","DOIUrl":"https://doi.org/10.2139/ssrn.2490670","url":null,"abstract":"We provide evidence that the determinants of the primary loan loss indicators reported in financial reports — non-performing loans, the allowance and provision for loan losses, and net loan charge-offs — vary dramatically across real estate, commercial, and consumer loans, because these loan types differ in their homogeneity and collateralization and thus in the measurement of incurred losses under GAAP. Extending Wahlen (1994), we develop and estimate models of the non-discretionary and discretionary determinants of these loan loss indicators by loan type. The estimations indicate that banks’ exercise of discretion over provisions for loan losses is largely limited to heterogeneous commercial loans, a small slice of banks’ loan portfolios, and they provide many insights into the bank-specific and macroeconomic drivers of banks’ loan loss accruals. To demonstrate the increased statistical power and construct validity that results from conducting research on banks’ loan loss accruals by loan type, we show that this approach significantly improves the accuracy of out-of-sample predictions of future net loan charge-offs, more so for samples of banks whose loan portfolio composition varies more from that of the average bank. Our results illustrate the usefulness of disaggregated disclosures of loan loss indicators by loan type for future accounting research.","PeriodicalId":181062,"journal":{"name":"Corporate Governance: Disclosure","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132076183","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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