Bank Accounting Regulations, Enforcement Mechanisms, and Financial Statement Informativeness: Cross-country Evidence
Augustine Duru, Iftekhar Hasan, Liang Song, Yijiang Zhao
Accounting and Business Research,
No. 3,
2020
Abstract
We construct measures of accounting regulations and enforcement mechanisms that are specific to a country's banking industry. Using a sample of major banks in 37 economies, we find that the informativeness of banks’ financial statements, measured by the value relevance of earnings and common equity, is higher in countries with stricter bank accounting regulations and countries with stronger enforcement. These findings suggest that superior bank accounting and enforcement mechanisms enhance the informativeness of banks’ financial statements. In addition, we find that the effects of bank accounting regulations are more pronounced in countries with stronger enforcement in the banking industry, suggesting that enforcement is complementary to bank accounting regulations in achieving higher value relevance of financial statements. Our study has important policy implications for bank regulators.
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The Value of Firm Networks: A Natural Experiment on Board Connections
Ester Faia, Maximilian Mayer, Vincenzo Pezone
CEPR Discussion Papers,
No. 14591,
2020
Abstract
This paper presents causal evidence of the effects of boardroom networks on firm value and compensation policies. We exploit exogenous variation in network centrality arising from a ban on interlocking directorates of Italian financial and insurance companies. We leverage this shock to show that firms whose centrality in the network rises after the reform experience positive abnormal returns around the announcement date and are better hedged against shocks. Information dissemination plays a central role: results are driven by firms that have higher idiosyncratic volatility, low analyst coverage, and more uncertainty surrounding their earnings forecasts. Firms benefit more from boardroom centrality when they are more central in the input-output network, hence more susceptible to upstream shocks, when they are less central in the cross-ownership network, or when they have low profitability or low growth opportunities. Network centrality also results in higher directors' compensation, due to rent sharing and improved executives' outside option, and more similar compensation policies between connected firms.
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Comparing Financial Transparency between For-profit and Nonprofit Suppliers of Public Goods: Evidence from Microfinance
John W. Goodell, Abhinav Goyal, Iftekhar Hasan
Journal of International Financial Markets, Institutions and Money,
January
2020
Abstract
Previous research finds market financing is favored over relationship financing in environments of better governance, since the transaction costs to investors of vetting asymmetric information are thereby reduced. For industries supplying public goods, for-profits rely on market financing, while nonprofits rely on relationships with donors. This suggests that for-profits will be more inclined than nonprofits to improve financial transparency. We examine the impact of for-profit versus nonprofit status on the financial transparency of firms engaged with supplying public goods. There are relatively few industries that have large number of both for-profit and nonprofit firms across countries. However, the microfinance industry provides the opportunity of a large number of both for-profit and nonprofit firms in relatively equal numbers, across a wide array of countries. Consistent with our prediction, we find that financial transparency is positively associated with a for-profit status. Results will be of broad interest both to scholars interested in the roles of transparency and transaction costs on market versus relational financing; as well as to policy makers interested in the impact of for-profit on the supply of public goods, and on the microfinance industry in particular.
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Does Machine Learning Help us Predict Banking Crises?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Journal of Financial Stability,
December
2019
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance metric, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly efficiently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
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Foreign Ownership, Bank Information Environments, and the International Mobility of Corporate Governance
Yiwei Fang, Iftekhar Hasan, Woon Sau Leung, Qingwei Wang
Journal of International Business Studies,
No. 9,
2019
Abstract
This paper investigates how foreign ownership shapes bank information environments. Using a sample of listed banks from 60 countries over 1997–2012, we show that foreign ownership is significantly associated with greater (lower) informativeness (synchronicity) in bank stock prices. We also find that stock returns of foreign-owned banks reflect more information about future earnings. In addition, the positive association between price informativeness and foreign ownership is stronger for foreign-owned banks in countries with stronger governance, stronger banking supervision, and lower monitoring costs. Overall, our evidence suggests that foreign ownership reduces bank opacity by exporting governance, yielding important implications for regulators and governments.
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Should Forecasters Use Real‐time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
German Economic Review,
No. 4,
2019
Abstract
In this paper, we investigate whether differences exist among forecasts using real‐time or latest‐available data to predict gross domestic product (GDP). We employ mixed‐frequency models and real‐time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real‐time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
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Financial Literacy and Self-employment
Aida Ćumurović, Walter Hyll
Journal of Consumer Affairs,
No. 2,
2019
Abstract
In this paper, we study the relationship between financial literacy and self‐employment. We use established financial literacy questions to measure literacy levels. The analysis shows a highly significant and positive correlation between the index and self‐employment. We address the direction of causality by applying instrumental variable techniques based on information about maternal education. We also exploit information on financial support and family background to account for concerns about the exclusion restriction. The results provide support for a positive effect of financial literacy on the probability of being self‐employed. As financial literacy is acquirable, the findings suggest that entrepreneurial activities might be increased by enhancing financial literacy.
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Lock‐in Effects in Relationship Lending: Evidence from DIP Loans
Iftekhar Hasan, Gabriel G. Ramírez, Gaiyan Zhang
Journal of Money, Credit and Banking,
No. 4,
2019
Abstract
Do prior lending relationships result in pass‐through savings (lower interest rates) for borrowers, or do they lock in higher costs for borrowers? Theoretical models suggest that when borrowers experience greater information asymmetry, higher switching costs, and limited access to capital markets, they become locked into higher costs from their existing lenders. Firms in Chapter 11 seeking debtor‐in‐possession (DIP) financing often fit this profile. We investigate the presence of lock‐in effects using a sample of 348 DIP loans. We account for endogeneity using the instrument variable (IV) approach and the Heckman selection model and find consistent evidence that prior lending relationship is associated with higher interest costs and the effect is more severe for stronger existing relationships. Our study provides direct evidence that prior lending relationships do create a lock‐in effect under certain circumstances, such as DIP financing.
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An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance measure, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly effciently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
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Lame-Duck CEOs
Marc Gabarro, Sebastian Gryglewicz, Shuo Xia
SSRN Working Papers,
2018
Abstract
We examine the relationship between protracted CEO successions and stock returns. In protracted successions, an incumbent CEO announces his or her resignation without a known successor, so the incumbent CEO becomes a “lame duck.” We find that 31% of CEO successions from 2005 to 2014 in the S&P 1500 are protracted, during which the incumbent CEO is a lame duck for an average period of about 6 months. During the reign of lame duck CEOs, firms generate an annual four-factor alpha of 11% and exhibit significant positive earnings surprises. Investors’ under-reaction to no news on new CEO information and underestimation of the positive effects of the tournament among the CEO candidates drive our results.
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