Follow the Leader: Strategic Interaction in Corporate Capital Structure Mark T' Leary Cornell Univer

1
Follow the Leader Strategic Interaction in
Corporate Capital StructureMark T.
LearyCornell University, The Johnson School
Michael R. Roberts University of Pennsylvania,
The Wharton School September 8, 2009
2
Motivation

• Virtually all theory and empirics assume firms
  set financial policy independently of each other
• Theory suggests that firms financing decisions
  may not be made in isolation
• Product market feedback (Brander Lewis, 1986)
• Learning (Conlisk, 1980)
• Signaling (Ross, 1977)
• Empirically industry median leverage is the most
  important observable determinant of capital
  structure
• Survey evidence also suggests competitor behavior
  is important factor (Graham Harvey, 2001)

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Our Study

• Problem impact of peer firm financial policy
  does not have a unique interpretation because of
  reflection problem (Manski, 1993)
• Three potential explanations
• Proxy Effect Firms in same industry have similar
  characteristics (e.g., production technologies,
  investment opportunities)
• Contextual Effect Firms are responding to
  characteristics of other firms in industry (e.g.,
  liquidation values a la Shliefer Vishny, 1992)
• Peer Effect Firms are responding to the
  financing decisions of their peers
• Goal Disentangle these explanations identify
  underlying mechanism

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Key Findings and Messages

• Peer effects exist and are economically large
• 1 SD change in peer firm leverage ? 10 change in
  own-firm leverage
• 40 larger partial effect than next biggest
  determinant
• Peer effect in debt-equity choice drives leverage
  result
• Young, less successful firms mimic financial
  policies of more mature, successful firms
• But not the reverse!
• Product market competition not responsible
• Mixed support for signaling-herding story
• Firms do not operate in a vacuum with respect to
  financial policy
• Theory and empirics should recognize this

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Data

• Intersection of CRSP-Compustat 1965-2006
• Exclude
• Financials SIC in 6000,6999,
• Utilities SIC in 4900, 4999, and
• Government entities SIC in 9000,9999
• Firms undergoing significant acquisitions (aftnt1
  AB)
• Observations with missing data
• Industries with fewer than 10 firms
• Define industry by 3-digit SIC code

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Summary Stats
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Leverage and Industry Leverage

• ?stat signif at 1 , ? stat signif at 5,
  Coefficients scaled by SD(X), Book leverage
  results similar

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Empirical Model Identification Problem

• Model

• Population Regression Function

• Mean Regression of y on X and µg

• Integrate out X

• Solve for equilibrium, E(y µg)

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Identification Problem Intuition

• This is a classic simultaneity problem
• Firm is financial policy affects firm js and
  vice versa
• is endogenous because it is
  simultaneously determined with yigt
• We need an instrument z
• z affects firms js leverage (relevance) but
  does not affect firm is leverage (exclusion)
• We use the lagged idiosyncratic component of
  other firms equity returns

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Condition 1Instrument Relevance

• Financial policy linked to stock prices via
  information asymmetry (Myers and Majluf, 1984)
• Empirically strong link (Loughran and Ritter
  (1995), Baker and Wurgler (2002), Welch (2004))
• Unclear whether idiosyncratic component is linked
  but this is testable

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Condition 2The Exclusion Restriction

• Consider reduced form model
• Identification threat must come from variable
  that is
• Correlated with instrument,
• Correlated with firm is financial policy, and
• Uncorrelated with firm is idiosyncratic return,
  systematic return, and other controls
• E.g., must argue that other firms idiosyncratic
  stock returns are better measures of firm is
  investment opps, default risk, etc., than firm
  is firm characteristics.

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Instrument Construction

• Firm-specific rolling regressions using 2-5 years
  of monthly data
• Coefficients are constant within years
• E.g., Idiosyncratic and expected returns for 1/90
  12/90 for IBM computed by
• Estimation using data form 1/85 to 12/89
• Use estimated coefficients realized factor
  returns to estimate E(r) ?
• Annualize via monthly compounding

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Return Regression Summary Statistics
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Exclusion Restrictions

• We cant test this (and neither can anyone else!)
• But, we can look at the correlation of our
  instrument and firm is characteristics. Should
  be low if not zero, otherwise concern
• No significant correlations between instrument,
  , and any firm characteristics, Xigt-1,
• Regression R2 0
• Correlation between instrument and firm i
  idiosyncratic return is less than 0.05.
• Recall we include firm is idiosyncratic return
  in regression

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2SLS Leverage Results
Contextual effects estimates omitted
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2SLS Policy Results
Contextual effects estimates omitted
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2SLS Leverage Policy ResultsIndustry-Size
Groupings
Contextual effects estimates omitted
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What is the Mechanism Behind the Peer Effects?

• Theory
• Learning May be optimal for firms to mimic
  others if otherwise costly to optimize (Conlisk
  (1980))
• Product Market Feedback Brander and Lewis
  (1986), duopoly model in which both firms choose
  high debt levels to protect themselves from
  aggressive behavior of other. If price predation
  (Bolton and Scharfstein, 1990) or
  under-investment (Chevalier Scharfstein, 1996)
  is severe enough, high levered firms will mimic
  less levered counterparts
• Signaling Pooling equilibrium to avoid costly of
  separation (Ross, 1977, Yilmaz et al, 2009)
• Empirical Strategy Heterogeneity in peer effect

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LearningLeader-Follower

• Do some firms follow others?

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Product Market Competition

• Use industry level avgs to sort industries by
  year

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Signaling

• Sort firms within an industry-year

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Robustness Tests
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Conclusions

• Firms dont make financing decisions in a vacuum
• Decisions of peers has a profound influence on
  capital structure
• Initial evidence suggestive of learning or
  leader-follower behavior
• Theory and empirics need to recognize this
  interaction to understand capital structure
• Better understand precisely how and why firms
  interact