Social Connections and Information Leakage: Evidence from Target Stock Price Run-up in Takeovers
Iftekhar Hasan, Lin Tong, An Yan
Journal of Financial Research,
forthcoming
Abstract
Does information leakage in a target's social networks increase its stock price prior to a merger announcement? Evidence reveals that a target with more social connections indeed experiences a higher pre-announcement price run-up. This effect does not exist during or after the merger announcement, or in windows ending two months before the announcement. It is more pronounced among targets with severe asymmetric information, and weaker when the information about the upcoming merger is publicly available prior to the announcement. It is also weaker in expedited deals such as tender offers.
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Advances in Using Vector Autoregressions to Estimate Structural Magnitudes
Christiane Baumeister, James D. Hamilton
Econometric Theory,
No. 3,
2024
Abstract
This paper surveys recent advances in drawing structural conclusions from vector autoregressions (VARs), providing a unified perspective on the role of prior knowledge. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error. We raise concerns from both a frequentist and a Bayesian perspective about the way that results are typically reported for VARs that are set-identified using sign and other restrictions. We call attention to a common but previously unrecognized error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one only knows the effects of a single structural shock.
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The Importance of Credit Demand for Business Cycle Dynamics
Gregor von Schweinitz
IWH Discussion Papers,
No. 21,
2023
Abstract
This paper contributes to a better understanding of the important role that credit demand plays for credit markets and aggregate macroeconomic developments as both a source and transmitter of economic shocks. I am the first to identify a structural credit demand equation together with credit supply, aggregate supply, demand and monetary policy in a Bayesian structural VAR. The model combines informative priors on structural coefficients and multiple external instruments to achieve identification. In order to improve identification of the credit demand shocks, I construct a new granular instrument from regional mortgage origination.
I find that credit demand is quite elastic with respect to contemporaneous macroeconomic conditions, while credit supply is relatively inelastic. I show that credit supply and demand shocks matter for aggregate fluctuations, albeit at different times: credit demand shocks mostly drove the boom prior to the financial crisis, while credit supply shocks were responsible during and after the crisis itself. In an out-of-sample exercise, I find that the Covid pandemic induced a large expansion of credit demand in 2020Q2, which pushed the US economy towards a sustained recovery and helped to avoid a stagflationary scenario in 2022.
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What Explains International Interest Rate Co-Movement?
Annika Camehl, Gregor von Schweinitz
IWH Discussion Papers,
No. 3,
2023
Abstract
We show that global supply and demand shocks are important drivers of interest rate co-movement across seven advanced economies. Beyond that, local structural shocks transmit internationally via aggregate demand channels, and central banks react predominantly to domestic macroeconomic developments: unexpected monetary policy tightening decreases most foreign interest rates, while expansionary local supply and demand shocks increase them. To disentangle determinants of international interest rate co-movement, we use a Bayesian structural panel vector autoregressive model accounting for latent global supply and demand shocks. We identify country-specific structural shocks via informative prior distributions based on a standard theoretical multi-country open economy model.
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Structural Vector Autoregressions with Imperfect Identifying Information
Christiane Baumeister, James D. Hamilton
American Economic Association Papers and Proceedings,
May
2022
Abstract
The problem of identification is often the core challenge of empirical economic research. The traditional approach to identification is to bring in additional information in the form of identifying assumptions, such as restrictions that certain magnitudes have to be zero. In this paper, we suggest that what are usually thought of as identifying assumptions should more generally be described as information that the analyst had about the economic structure before seeing the data. Such information is most naturally represented as a Bayesian prior distribution over certain features of the economic structure.
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Advances in Using Vector Autoregressions to Estimate Structural Magnitudes
Christiane Baumeister, James D. Hamilton
Abstract
This paper discusses drawing structural conclusions from vector autoregressions. We call attention to a common error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one knows only the effects of a single structural shock and the covariance matrix of the reduced-form residuals. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error. We raise concerns about the way that results are typically reported for VARs that are set-identified using sign and other restrictions.
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Credit Allocation when Borrowers are Economically Linked: An Empirical Analysis of Bank Loans to Corporate Customers
Iftekhar Hasan, Kristina Minnick, Kartik Raman
Journal of Corporate Finance,
June
2020
Abstract
Using detailed loan level data, we examine bank lending to corporate customers relying on principal suppliers. Customers experience larger loan spreads, higher intensity of covenants and greater likelihood of requiring collateral when they depend more on the principal supplier for inputs. The positive association between the customer’s loan spread and its dependence on the principal supplier is less pronounced when the bank has a prior loan outstanding with the principal supplier, and when the bank has higher market share in the industry. Longer relationships between the customer and its principal supplier, and between the bank and the principal supplier, mitigate lending constraints. The evidence is consistent with corporate suppliers serving as an informational bridge between the lender and the customer.
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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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Inference in Structural Vector Autoregressions when the Identifying Assumptions are not Fully Believed: Re-evaluating the Role of Monetary Policy in Economic Fluctuations
Christiane Baumeister, James D. Hamilton
Journal of Monetary Economics,
2018
Abstract
Point estimates and error bands for SVARs that are set identified are only justified if the researcher is persuaded that some parameter values are a priori more plausible than others. When such prior information exists, traditional approaches can be generalized to allow for doubts about the identifying assumptions. We use information about both structural coefficients and impacts of shocks and propose a new asymmetric t-distribution for incorporating information about signs in a nondogmatic way. We apply these methods to a three-variable macroeconomic model and conclude that monetary policy shocks are not the major driver of output, inflation, or interest rates.
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Can Lenders Discern Managerial Ability from Luck? Evidence from Bank Loan Contracts
Dien Giau Bui, Yan-Shing Chen, Iftekhar Hasan, Chih-Yung Lin
Journal of Banking and Finance,
2018
Abstract
We investigate the effect of managerial ability versus luck on bank loan contracting. Borrowers showing a persistently superior managerial ability over previous years (more likely due to ability) enjoy a lower loan spread, while borrowers showing a temporary superior managerial ability (more likely due to luck) do not enjoy any spread reduction. This finding suggests that banks can discern ability from luck when pricing a loan. Firms with high-ability managers are more likely to continue their prior lower loan spread. The spread-reduction effect of managerial ability is stronger for firms with weak governance structures or poor stakeholder relationships, corroborating the notion that better managerial ability alleviates borrowers’ agency and information risks. We also find that well governed banks are better able to price governance into their borrowers’ loans, which helps explain why good governance enhances bank value.
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