Do Peer Firms Affect Corporate Financial Policy?

Leary and Roberts, JF (2014)

Many CFOs responding to Graham and Harvey’s survey have indicated that peer firm financial policy plays an important role in their decision-making.  A number of empirical papers have shown that average industry leverage seems to be an important determinant of individual firm leverage.  Intuitively, peer firm leverage might be a signal of optimal capital structure and/or investment opportunities.  If a CFO does not have all the answers, he may try to infer them from peer firms’ decisions.

There are two general reasons why a firm’s capital structure decisions might appear to be related to the decisions of peer firms.  The first reason is that the firms may be subject to common factors.  Exposure to these common factors might have led to the firms selecting to become “peer firms” in the first place.  The second reason is that the firm may respond to the characteristics and/or the actions of its peer firms in a causal manner.  The confoundedness of these two reasons lead to what Manski (1993) calls the “reflection problem.”  This is an endogeneity problem that arises when trying to determine the impact of group behavior on the behavior of individuals within the group.

This paper asks, in addition to the title question, whether firms respond to the actions or to the characteristics of peer firms.


The basic idea is to regress firm outcome on peer firm characteristics, perfectly controlling for common variation; or, to measure the impact on firm outcome of an exogenous shock to peer firms. This paper regresses peer firm stock returns on market excess returns and on firm fixed effects over rolling 5-year windows, and uses the residuals as the exogenous shock.  They use (lagged) idiosyncratic stock return as the exogenous shock because it is easy to calculate and can be measured each period, unlike other plausibly exogenous shocks such as natural disasters or CEO death.  The inclusion of industry fixed effects is supposed to account for any common factors, rather than for priced return determinants.

Table 3, Panel A shows the results from regressing several capital structure outcomes on own-firm idiosyncratic stock return and characteristics, average peer-firm idiosyncratic stock return and characteristics, and industry and year fixed effects.

  • Columns 1-2 use market leverage and book leverage as dependent variables
  • Columns 3-4 use first-differences of market and book leverage, with no industry fixed effects, to control for unobserved own-firm characteristics in another way.
  • Columns 5-7 use different types of security issuance as dependent variables.

Table 3, Panel B conducts a number of robustness tests to address a number of concerns.

  • Column 1 replaces lagged own-firm idiosyncratic stock return with lagged and contemporaneous own-firm total stock return. The results are similar. This alleviates the concerns that (1) lagged peer-firm are correlated with own-firm idiosyncratic shock and this is what drives the results, and (2) the asset pricing model is misspecified so that the residuals are biased in some way.
  • Column 2 controls for a bevy of additional variables that the previous literature has presented as determinants of capital structure.
  • Column 3 controls for bank fixed effects.
  • Column 4 includes own-firm lagged leverage ratio to account for possible leverage targeting.
  • Column 5 replaces lagged controls with contemporaneous controls to verify that the results are not due to the idiosyncratic shocks affecting characteristics with a lag.
  • Column 6 adds quadratic and cubic polynomials of the peer-firm and own-firm characteristics to control for misspecification of functional form in the baseline regression.

The authors then redefine peer groups, and use shocks to peer-firm customers, that are not own-firm customers and that are in a different industry, as the exogenous shock.

Finally, they perform a double 5×5 sort on peer-firm idiosyncratic equity shock and peer-firm actions (changes in leverage).  For each group, they calculate the own-firm change in leverage.  The differential changes in own-firm leverage across the quintiles of one variable, controlling for the other variable, shed light on whether firms respond to the characteristics or to the actions of their peers.


  • Firms respond to peer-firms’ financing decisions.
  • Firms respond to peer-firms’ characteristics, but to a lesser degree than to their decisions.
  • Peer firm behavior is a more important determinant than the observable determinants from prior literature.
  • Smaller, younger, and less-successful firms tend to be more influenced by peer firms, while industry leaders appear to be less influenced.


I love that the main findings of the paper can be presented in just one table (table 3)!  The authors use an interesting type of exogenous shock and draw on a variety of finance research to motivate and defend it.  I also love the literature review section(s) of this paper.  It is very well written…or maybe it’s just personal preference for the subject.

This paper is in line with Lemmon, Roberts, and Zender (2008) in questioning the relevance of several decades of capital structure.  If firm peer groups are relatively static, then this could explain the just-mentioned paper’s findings that firm fixed effects are the best determinant of firm capital structure.

Luck Versus Skill in Mutual Fund Returns

Fama and French, JF (2010)

Berk and Green (2004) show that zero alpha and lack of predictability in mutual fund returns is consistent with a simple model of efficient markets, rational investors, and free flow of capital.  They argue that 80% of active managers are skilled enough to achieve returns that at least cover their fees.

The most basic question in the mutual fund literature is whether active fund managers who see high returns are skilled or just lucky.  Researchers commonly test this by looking for persistence in fund returns, since fund managers should not consistently win if winning is purely chance.  However, they also commonly use recent fund history to determine persistence. If mutual fund returns are noisy, then short-term persistence will be hard to identify even if it exists.

The Question:  What is the mean and variance of the true alpha of active fund management?

Fama and French compare the long-run performance of actual funds with simulated fund returns from a world where true alpha is zero, but the characteristics of mutual fund returns are otherwise identical.  They also simulate data with alphas from a known distribution to estimate the standard deviation of true alpha.

Simulation 1: Test the hypothesis that mean alpha equals zero

  1. For each mutual fund, regress the entire sample (1984-2006) of monthly net returns on the FF three-factor model, and examine the CDF of t-statistics for alpha.
  2. Simulate data from a zero-alpha world
    1. Subtract each fund’s estimated long-sample alpha from its monthly returns to get “adjusted” returns from a hypothetical zero-alpha world.
    2. For each fund, randomly draw (with replacement) 273 months from the time series of adjusted returns. This is a time series that could have occurred if true alpha were zero and returns were random.
    3. Regress each fund’s time series on the FF 3-factor model, separate funds into three groups based on AUM, and examine the CDF of alpha t-statistics for each group.
      1. Focus on t-statistics instead of alphas, since errors are likely distributed differently in across time periods and since funds will vary in the number of monthly in which they appear in each simulation.
    4. Repeat 10,000 times, and average to get the distribution of t-statistics from the simulated data.
  3. Repeat for gross returns, then repeat again (for both gross and net returns) with the 4-factor model of Carhart (1997).
  4. Compare the CDF of t-statistics from actual data and from simulated zero-alpha data.
    1. If the distribution from actual data is higher than the simulated data (CDF below that of the simulated data), this will indicate that observed returns did not come from a zero-alpha world—i.e., they are due to skill and not luck.
  5. Also track, at each percentile of the CDF, the percentage of simulations that produce alpha t-statistics lower than the corresponding statistic from the actual data.
    1. If this number is small (large), it is evidence against (for) fund manager skill.

This exercise actually produces 12 cross-sections of simulated returns: three AUM groups, two returns (gross and net) and two asset pricing models.

Simulation 2: Estimate the standard deviation of true alpha.

  1. Adjust each fund’s returns, as before, by subtracting the fund’s long-term estimated alpha.
  2. For each fund, draw an artificial alpha from a normal distribution with mean 0 and standard deviation 0.0, and add this to the fund’s monthly returns.
  3. Draw, with replacement, a time series of 273 months, as before.
  4. Regress each fund’s simulated time series on the FF 3-factor model, separate into AUM groups, and examine the CDF of alpha t-statistics for each group.
  5. Repeat 10,000 times and average to get a simulated distribution of t-statistics for alpha.
  6. Repeat the entire process eight more times, increasing the standard deviation of the normal distribution from which the artificial alphas are drawn by 0.25 each time.
  7. Repeat everything for the 4-factor asset pricing model.
  8. Compare the CDF of t-statistics from the actual data and from each of the 18 simulations
    1. Reject artificial alpha standard deviations that lead to too few simulations producing left tails below actual data, or too many simulations producing right tails below actual data, as unlikely.


  1. Mutual fund managers are skilled enough to produce net alpha only about 1-2% of the time (the CDF of alpha t-statistics from actual data is only below the CDF of simulated data at the 98th or 99th percentiles).
  2. After controlling for momentum, 0% of the monthly returns appear to be due to skill.
  3. Managers can produce gross alpha only 10% of the time in medium and large funds, and 40% of the time for small funds (less than $250mm AUM).
  4. There is evidence of managers producing negative true alpha.
  5. The right tails of the distribution of t-statistics, in both actual and simulated data, is smaller for larger funds, which supports Berk’s and Green’s (2004) assumption about decreasing returns to scale.
    • But the left tails are just as extreme for large funds as for small funds. Fama and French interpret this as violating another Berk and Green assumption that the bad managers are all weeded out before their funds get large.
  6. The true alpha of active management can be fairly approximated by a normal distribution with mean zero and a standard deviation between 0.75 and 1.25.


The takeaway from this paper is that the returns of almost all funds, even the most successful ones, could just be luck.  There are a lot of funds, as probability alone dictates that at least a few must seem consistently skilled.

As usual, Fama and French do careful and serious empirical work, and communicate their arguments clearly and succinctly.  However, they place a small handicap on active managers by assuming that passive strategies incur zero costs.  Passive fees are low, but they aren’t zero.  And your costs are not zero even if you do your own investing.  You have to spend time on Yahoo! Finance, subscribe to the Wall Street Journal, or at least design an algorithm for randomly selecting stocks for your portfolio.

I also think they don’t address a natural extension of the Berk-Green argument.  Berk and Green’s story is that after a mutual fund has high returns, the market infers a skill level and gives him more money.  If the market anticipates an evolution of skill, investors could give money to managers even before they post high returns.  The market might be even more efficient than Berk and Green assumed.  The end of that theoretical story is that managers should produce zero alpha, even if all of them are skilled.  Fama and French do find some evidence that there are managers producing negative alpha, but a complete refutation of Berk and Green would have to present convincing evidence that no managers can produce alpha, even in gross returns.  Fama and French certainly weaken the statement that 80% of managers are skilled, but they aren’t able to drive that number to 0%.

A number of things may be going on.  It could be the case, as Fama and French write, that the skilled managers have moved on from mutual funds to more lucrative careers in hedge funds.  Berk and Green admit that their analysis only applies to open-ended funds, so perhaps skilled managers prefer to close their funds.  Perhaps Fama and French are right in hypothesizing that the entry of hordes of unskilled managers have drowned out the signal from skilled managers.  If active managers use leverage, perhaps they underperform in bad times.  If there were more bad times between 1984 and 2006 than there were between 1975 and 2002, then skill could seem to disappear for that reason.

Challenges in Identifying and Measuring Systemic Risk

Hansen, Lars Peter, “Challenges in Identifying and Measuring Systemic Risk,” SSRN, February 14, 2014,

Purpose & Motivation:  The “great recession” sparked immense interest in “systemic risk,” but that phrase is at risk of becoming a buzzword used to justify policy based on vague notions.  This essay offers perspective on the difficulty of quantifying systemic risk, or the connections between financial markets and the broader economy.

The Problem:  Before something can be rigorously discussed, debated, and analyzed, it must be measured.  The danger in allowing “systemic risk” to remain a qualitative label is that related policy cannot be based on hard data and informed debate.  Such policy is potentially harmful, and always difficult to criticize.

Challenges:  A lot of people are trying to quickly produce measures of systemic risk, to guide policy responses to the 2007-2009 recession.  While the hunt for numbers can be helpful, numbers without theory are difficult to apply.  We need economic theories that tell us which risk measurement techniques are useful for which purposes.

  • Challenge #1: Distinguishing “systematic” from “systemic”
    • “Systematic” risk is an investor’s exposure to macroeconomic shocks. It cannot be eliminated through diversification, and so demands a premium.
      • Macroeconomists try to identify shocks and propose policy that will mitigate their effects.
      • Finance research asks what premium is demanded by each element of systematic risk.
    • “Systemic” risk refers to the markets breaking down altogether. It has multiple interpretations:
      • A bank run – this is a concern of central banks in their position as “lenders of last resort”
      • The susceptibility of a network of financial institutions to shocks that start in one place and spread throughout – understanding this requires knowing which networks are risky and which shocks could start a fire
      • The insolvency of a major financial sector or institution
    • Challenge #2: Quantifying risk and uncertainty
      • Even the best models are inaccurate. Economists face uncertainty when they select between competing models, and agents within those models also face uncertainty.

Current Approaches to Measuring Systemic Risk:

  • Tail Measures look at the relation between the tails of financial institutions’ equity returns. This approach is plagued by the paucity of historical data, and ignores non-public institutions that may be important.
  • Contingent Claims Analysis uses option pricing techniques to value the debt (as a put option) and equity (as a call option) of an entire sector of the financial system. Development of this analysis requires better understanding of the overall market’s risk appetite and of the shocks that demand large risk premia.
  • Network Models look at the degree of interconnectedness between institutions. Rampant endogeneity complicates these models.
  • Dynamic, Stochastic Macroeconomic Models seem promising, but applications to systemic risk are still new.
  • Problems with data collection and sharing – researchers deal with confidentiality issues, biases in data collection, and limitations in the scope of publicly-available data.

Conclusions:  Attempts to examine systemic risk in the financial system face difficulties determining how to model the system, which data to use, and how to measure the data.  However, the high public interest in this topic and the general lack of good research present an alluring challenge.

Introduction to Outlines

One of my mentors when I was applying to schools said his Ph.D. Candidacy Exam (taken at the end of regular coursework) required him to “basically memorize 500 classic papers.”  I have the syllabuses to two of his Ph.D. courses, and there are 378 readings on the Asset Pricing list alone.

To do good research, I need to know what’s already been done and what methods are generally accepted.  I’ll also need to know what all classic papers say to get through my classes.  I will try to get a head start this summer by outlining many of the 75 or so “primary” readings from my mentor’s courses.

My initial goal with these outlines will be just to capture the main ideas, methods, and conclusions of each paper.  I’ll outline the papers in no particular order.  I hope to eventually post commentaries as I become more aware of the academic discussions that surrounded and that build upon these classic papers.