Research articles for the 2021-01-23
Bad News for Announcers, Good News for Rivals â€" Are Rivals Fully Seizing Transition-Period Opportunities Following Announcers’ Top Management Turnovers?
SSRN
Research summary. This study analyzes whether and how the disruption of top management turnovers can affect not only turnover firms but also their intra-industry rivals. It thus adds to the literature on both leader life cycles and competitive dynamics. Using a U.S. sample of 857 CEO turnovers, we find a period of relative stagnation for announcing companies following top management turnovers. We also find that intra-industry rivals can use this period to their advantage. Semi-structured interviews with seasoned CEOs, CFOs, and a board member from large publicly listed firms, as well as an extensive news search, support this notion. Intra-industry rivals gain a competitive advantage that can result in positive abnormal stock returns and accounting performance. The intra-industry outperformance is greater for forced turnovers. Managerial summary. The departure of a company’s CEO, forced or not, is usually a disruptive event for a company, as the successor must adapt to the new environment before undertaking any major strategic changes. Rivals can seize an opportunity during the transition period of the announcing company because they remain fully operational. They can thus actively exploit the relative inability of turnover companies to react by, e.g., launching sales initiatives or increasing M&A activity. This interpretation is supported by internal and external evidence. Investors on average also recognize this situation, and stock prices react accordingly.
SSRN
Research summary. This study analyzes whether and how the disruption of top management turnovers can affect not only turnover firms but also their intra-industry rivals. It thus adds to the literature on both leader life cycles and competitive dynamics. Using a U.S. sample of 857 CEO turnovers, we find a period of relative stagnation for announcing companies following top management turnovers. We also find that intra-industry rivals can use this period to their advantage. Semi-structured interviews with seasoned CEOs, CFOs, and a board member from large publicly listed firms, as well as an extensive news search, support this notion. Intra-industry rivals gain a competitive advantage that can result in positive abnormal stock returns and accounting performance. The intra-industry outperformance is greater for forced turnovers. Managerial summary. The departure of a company’s CEO, forced or not, is usually a disruptive event for a company, as the successor must adapt to the new environment before undertaking any major strategic changes. Rivals can seize an opportunity during the transition period of the announcing company because they remain fully operational. They can thus actively exploit the relative inability of turnover companies to react by, e.g., launching sales initiatives or increasing M&A activity. This interpretation is supported by internal and external evidence. Investors on average also recognize this situation, and stock prices react accordingly.
Diversity, Inclusion, and the Dissemination of Ideas: Evidence from the Academic Finance Profession
SSRN
We propose to examine how diversity relates to variation in career outcomes within a single occupation: the occupation of a finance academic. With the support of the American Finance Association (AFA), we surveyed current and recent past members of the AFA on the professional climate in the field of finance. Responses shed light on issues related to workplace culture, diversity, bias, and expectations. By linking these responses to selfâ€reported data on career outcomes, we highlight the importance of: selection, the structure of work, and implicit or explicit biases. We seek to understand how these factors relate to differences in career paths within finance. We highlight that the consequences of an absence of inclusiveness extend beyond the personal to the field as a whole. Finally, we also provide evidence on the ways the COVIDâ€19 pandemic has influenced these issues.
SSRN
We propose to examine how diversity relates to variation in career outcomes within a single occupation: the occupation of a finance academic. With the support of the American Finance Association (AFA), we surveyed current and recent past members of the AFA on the professional climate in the field of finance. Responses shed light on issues related to workplace culture, diversity, bias, and expectations. By linking these responses to selfâ€reported data on career outcomes, we highlight the importance of: selection, the structure of work, and implicit or explicit biases. We seek to understand how these factors relate to differences in career paths within finance. We highlight that the consequences of an absence of inclusiveness extend beyond the personal to the field as a whole. Finally, we also provide evidence on the ways the COVIDâ€19 pandemic has influenced these issues.
Measuring the Default Risk of Small Business Loans: Improved Credit Risk Prediction using Deep Learning
SSRN
This paper suggests using a multilayer artificial neural network (ANN) method, known as deep learning ANN, to predict the probability of default (PD) within the survival analysis framework. Deep learning ANN structures consider hidden interconnections among the covariates determining the PD which can lead to prediction gains compared to parametric statistical methods. The application of the ANN method to a large data set of small business loans demonstrates prediction gains for the method relative to the logit and skewed logit models. These gains mainly concern short term prediction horizons and are more apparent for the type I misclassification error of loan default events, which has important implications for bank loans portfolio management. To identify the effects of covariates on the PD by the ANN structure, the paper proposes a bootstrap sampling method obtaining the distribution of changes of the PD over discrete covariate changes, while controlling for possible interactions among the covariates. We find that the covariates with the most important influence on the PD include the delinquent amount of a loan over its total balance, the payments and the balance of the loan over its installment, as well as the delinquency buckets of a loan. The duration of a loan is also found to be an important factor of default risk.
SSRN
This paper suggests using a multilayer artificial neural network (ANN) method, known as deep learning ANN, to predict the probability of default (PD) within the survival analysis framework. Deep learning ANN structures consider hidden interconnections among the covariates determining the PD which can lead to prediction gains compared to parametric statistical methods. The application of the ANN method to a large data set of small business loans demonstrates prediction gains for the method relative to the logit and skewed logit models. These gains mainly concern short term prediction horizons and are more apparent for the type I misclassification error of loan default events, which has important implications for bank loans portfolio management. To identify the effects of covariates on the PD by the ANN structure, the paper proposes a bootstrap sampling method obtaining the distribution of changes of the PD over discrete covariate changes, while controlling for possible interactions among the covariates. We find that the covariates with the most important influence on the PD include the delinquent amount of a loan over its total balance, the payments and the balance of the loan over its installment, as well as the delinquency buckets of a loan. The duration of a loan is also found to be an important factor of default risk.
Open Banking: Credit Market Competition When Borrowers Own the Data
SSRN
Open banking facilitates data sharing consented by customers who generate the data, with a regulatory goal of promoting competition between traditional banks and challenger fintech entrants. We study lending market competition when sharing banks' customer data enables better borrower screening or targeting by fintech lenders. Open banking could make the entire financial industry better off yet leave all borrowers worse off, even if borrowers could choose whether to share their data. We highlight the importance of equilibrium credit quality inference from borrowers' endogenous sign-up decisions. When data sharing triggers privacy concerns by facilitating exploitative targeted loans, the equilibrium sign-up population can grow with the degree of privacy concerns.
SSRN
Open banking facilitates data sharing consented by customers who generate the data, with a regulatory goal of promoting competition between traditional banks and challenger fintech entrants. We study lending market competition when sharing banks' customer data enables better borrower screening or targeting by fintech lenders. Open banking could make the entire financial industry better off yet leave all borrowers worse off, even if borrowers could choose whether to share their data. We highlight the importance of equilibrium credit quality inference from borrowers' endogenous sign-up decisions. When data sharing triggers privacy concerns by facilitating exploitative targeted loans, the equilibrium sign-up population can grow with the degree of privacy concerns.
The Bank Branch Exit Game
SSRN
We study the process of capacity reduction by multi-plant firms competing in many markets, following a permanent, negative aggregate demand shock. The resulting insight on strategic plant closure is relevant to competition policy. We focus on the measurement of strategic delay in local markets, using data on the closures of bank branches in Spain during the Great Recession. We geolocate each branch and identify its competitors: those that lie within 150 meters. We also cluster the local markets in the same census tract, enabling the use of fixed effects in our regressions. We find that branches with competitors are less likely to close in any given year than branches without, indicative of strategic behavior. Further -only when controlling for possible correlation between demand size and vulnerability to the shock, through fixed effects - the probability of exit is decreasing in the number of competitors. This is the opposite slope of what the literature -unable to use fixed effects - tends to find. We argue that this sign reversal is also rationalizable by a war of attrition. Finally, we confirm that branches are more likely to close if their parent bank has multiplebranches in the same local market.
SSRN
We study the process of capacity reduction by multi-plant firms competing in many markets, following a permanent, negative aggregate demand shock. The resulting insight on strategic plant closure is relevant to competition policy. We focus on the measurement of strategic delay in local markets, using data on the closures of bank branches in Spain during the Great Recession. We geolocate each branch and identify its competitors: those that lie within 150 meters. We also cluster the local markets in the same census tract, enabling the use of fixed effects in our regressions. We find that branches with competitors are less likely to close in any given year than branches without, indicative of strategic behavior. Further -only when controlling for possible correlation between demand size and vulnerability to the shock, through fixed effects - the probability of exit is decreasing in the number of competitors. This is the opposite slope of what the literature -unable to use fixed effects - tends to find. We argue that this sign reversal is also rationalizable by a war of attrition. Finally, we confirm that branches are more likely to close if their parent bank has multiplebranches in the same local market.
What Is the Impact of Mutual Funds' ESG Preferences on Portfolio Firms?
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Mutual funds must publish policies announcing how they generally vote on the different ballot items at the shareholder meetings of their portfolio firms. I manually collect 17,000 of these policies for a sample of 29 of the largest U.S. mutual fund families over 2006-2018. I find that voting policies are a major predictor of funds' voting behavior. Exploiting staggered changes in funds' voting policies, I show that investee companies adopt their mutual fund shareholders' preferred governance provisions. This adoption is the result of mutual fund shareholders' active voting. Announced voting policies also stimulate strategic proposal submissions by non-mutual fund shareholders.
SSRN
Mutual funds must publish policies announcing how they generally vote on the different ballot items at the shareholder meetings of their portfolio firms. I manually collect 17,000 of these policies for a sample of 29 of the largest U.S. mutual fund families over 2006-2018. I find that voting policies are a major predictor of funds' voting behavior. Exploiting staggered changes in funds' voting policies, I show that investee companies adopt their mutual fund shareholders' preferred governance provisions. This adoption is the result of mutual fund shareholders' active voting. Announced voting policies also stimulate strategic proposal submissions by non-mutual fund shareholders.
