Signaling, instrumentation, and CFO decision-making

Abstract Building parable economies embedding econometricians, we view alternative estimators (Instrumental variables, fuzzy regression discontinuity, natural experiments, OLS, event studies) from the perspective of privately informed decision-makers, e.g., CFOs. Instrumental variable estimates can be misleading since randomization through observable instruments eliminates signal content arising from discretion. If the goal is informing discretionary decisions, rather than predicting outcomes after forced/mistaken actions, instrumentation is problematic, whereas OLS or event studies can be sufficient. The analysis shows that the utility of alternative estimators hinges upon often neglected assumptions about agent/econometrician information sets, as distinct from exclusion restrictions. We recommend parable economy estimation before real-world IV estimation.

[1]  P. Asquith,et al.  Equity issues and offering dilution , 1986 .

[2]  Stephen A. Ross,et al.  The determination of financial structure: the incentive-signalling approach , 1977 .

[3]  Esther Duflo,et al.  Scaling Up and Evaluation , 2003 .

[5]  Laurent Frésard,et al.  Does Firm Investment Respond to Peers' Investment? , 2016, Manag. Sci..

[6]  P. Veronesi.  How Does Information Quality Affect Stock Returns? , 1999 .

[7]  H. Leland..  Corporate Debt Value, Bond Covenants, and Optimal Capital Structure , 1994, World Scientific Reference on Contingent Claims Analysis in Corporate Finance.

[8]  Ben Seymour,et al.  Accounting for Behavior in Treatment Effects: New Applications for Blind Trials , 2015, PloS one.

[9]  Bruno Ferman,et al.  Understanding mechanisms underlying peer effects: evidence from a field experiment on financial decisions , 2014 .

[10]  Bernard Salanié,et al.  The Economics of Contracts: A Primer , 1997 .

[11]  Carlos A. Molina.  Are Firms Underleveraged? An Examination of the Effect of Leverage on Default Probabilities , 2005 .

[12]  David K. Levine,et al.  Learning in Games and the Interpretation of Natural Experiments , 2019 .

[13]  Alwyn Young.  Consistency without Inference: Instrumental Variables in Practical Application , 2019, European Economic Review.

[14]  François Gourio.  Credit Risk and Disaster Risk , 2011 .

[15]  Jérôme P. Taillard,et al.  What's your Identification Strategy? Innovation in Corporate Finance Research , 2015, Manag. Sci..

[16]  The timing and consequences of seasoned equity offerings: A regression discontinuity approach , 2020 .

[17]  Daniel Paravisini,et al.  Screening on Loan Terms: Evidence from Maturity Choice in Consumer Credit , 2017 .

[18]  D. Romer,et al.  The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks , 2007 .

[19]  Gilles Chemla,et al.  Controls, belief updating, and bias in medical RCTs , 2019, J. Econ. Theory.

[20]  Joshua D. Angrist,et al.  Mostly Harmless Econometrics: An Empiricist's Companion , 2008 .

[21]  A. Banerjee,et al.  A Theory of Experimenters: Robustness, Randomization, and Balance , 2020, American Economic Review.

[22]  Nagpurnanand R. Prabhala,et al.  Conditional Methods in Event-Studies and an Equilibrium Justification for Standard Event-Study Procedures , 1997 .

[23]  William S. Krasker,et al.  Stock Price Movements in Response to Stock Issues under Asymmetric Information , 1986 .

[24]  Liran Einav,et al.  Selection in Insurance Markets: Theory and Empirics in Pictures , 2011, The journal of economic perspectives : a journal of the American Economic Association.