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The Macro Impact of
Short-Termism
Stephen TerryBoston University
1
Concerns with Short-Termism
People act like “children who pick the plums out of their puddingto eat them at once.” Alfred Marshall, 1890
“Anybody can manage short. Anybody can manage long.Balancing those two things is what management is.”Jack Welch, 1998
“The long is short. Investment choice, like other life choices, isbeing re-tuned to a shorter wave-length.”Haldane & Davies, Bank of England, 2011
“In business, the mania over quarterly earnings consumesextraordinary amounts of senior executive time and attention.”Dominic Barton, McKinsey & Co. Managing Director, 2011
2
Firm Profit Bunch above
Short-Term Targets
Note: Forecast errors are Street profits minus median analyst forecasts from a 2-quarter horizon, scaled by firm assetsand expressed as a percentage. The histogram represents a panel of 43,688 firm years, covering 1982-2010 for 7,215firms. 68% of the sample lies within the bounds plotted above, and 13% of firm years have forecast error in themiddle bin. 10% of the sample exhibits exactly zero forecast error. Bin size is 0.05% of firm assets. Discontinuity orsorting is detected in the forecast error distribution at 0 at the 1% level according to the McCrary (2008) statistic.
3
Why Should We Care?
About 90% of managers report pressure to meet profit targets.Dichev, et al. (2013)
Research and development must be expensed from profits. USGAAP (FASB, 2014)
Almost 40% of managers would reject a positive NPV project ifthe project meant missing analyst targets. Graham, et al. (2005)
4
What I Do in This Paper
Estimate a Set of Empirical DiscontinuitiesLower long-term investment growth, discontinuous CEOcompensation and stock returns when just meeting forecasts
Build a Model of Manager Long-Term InvestmentGE endogenous growth, firm profit forecasts, micro profitabilityshocks, manager R&D and paper manipulation choices
Estimate Dynamic Costs of Short-TermismMicro: 25% more volatile R&D, -1% ∆ firm valueMacro: -0.1% lower growth, -0.4% lower welfare
Explore Agency Benefits from Short-Term PressureShirking: social and firm incentives alignedEmpire Building: social and firm incentives misaligned
5
Related Strands of LiteratureShort -Termism and IncentivesStein (1989), Asker, et al. (2014), Bhojraj, et al. (2009), Larkin (2014), Oyer (1998),Liebman and Mahoney (2013), Edmans, et al. (2013), Aghion, et al. (2013), Wolfers(2014), Allan and Dechow (2013), Asch (1990), Kester (1992), Markoff (1990), Mayer(2012), Michie (2001), Dechow and Sloan (1991), Matsunaga and Park (2001), Bushee(1998), Garicano, et al. (2013), Gourio and Roys (2012), Murphy (2001), Jenter andLewellen (2010)Endogenous Growth, Long-Term InvestmentAcemoglu, et al. (2013), Eisfeldt and Papanikolaou (2013), Peters and Taylor (2014),Aghion, et al. (2010), Peters (2013), Lentz and Mortensen (2008), Barlevy (2004,2007), Klette and Kortum (2004), Hall (2004), McGrattan and Prescott (2005, 2010,2014), Corrado, et al. (2006, 2013), Acemoglu and Cao (2010), Kortum and Lerner(2000), Griliches (1990)Quantitative Dynamic Models of Investment, AgencyNikolov and Whited (2013), Glover (2013), Cooper and Haltiwanger (2006), Eberly, etal. (2012), Gourio and Rudanko (2013, 2014), Hennessy and Whited (2005), Taylor(2010), Strebulaev and Whited (2012), Eisfeldt and Rampini (2007, 2008)Profit ManipulationDichev, et al. (2013), Marinovic, et al. (2012), Burgstahler and Chuk (2013), Za-kolyukina (2013), Burgstahler and Eames (2006), Graham, et al. (2005), Hertzberg(2004), Gunny (2010), Bartov, et al. (2002), Roychowdhury (2006), Bange and DeBondt (1998), Baber, et al. (1991), Huang and Marquardt (2013), Cheng and Warfield(2005), Bartov, et al. (2002)
6
Roadmap
Data and Empirical Discontinuities
Model of Profit Pressure and Growth
Estimated Costs of Short-Termism
Agency Benefits from Short-Term Pressure
7
Forecast and Accounting Data
I/B/E/S: contains 3.5 million individual analyst forecasts aswell as “Street” realizations of earnings per share
Compustat, Execucomp, & CRSP: standard public firmaccounting, financial, compensation, and returns data
Merged Sample: panel with long-term investment data onabout 25K observations, 4000 firms, and spanning 1983-2010
Earnings Definition R&D Expensing Coverage Descriptive NSF R&D
8
Discontinuities when Just
Meeting Forecasts(1) (2) (3) (4) (5)
Method Local Linear Local Linear Local Linear Local Linear Local LinearDependent Variable Investment Rate Intangibles Growth R&D Growth CEO Pay Abnormal ReturnsRunning Variable Forecast Error Forecast Error Forecast Error Forecast Error Forecast ErrorCutpoint 0 0 0 0 0
Discontinuity 0.35 -2.66** -2.57* 6.78** 0.64***(0.40) (0.95) (1.44) (2.68) (0.21)
Effects Firm, Year Firm, Year Firm, Year Firm, Year Market-AdjustedYears 1983-2010 1983-2010 1983-2010 1992-2010 1983-2010Firms 3969 3969 3969 2349 7794Observations 23084 23084 23084 17661 48297Relative to Mean 1.4% -27.0% -32.9% 6.78%a 0.64%a
Note: *,**,*** denote 10, 5, 1% significance. The regression discontinuity estimation relies on local linear regressionsand a triangular kernel, with bandwidth chosen via the optimal Imbens and Kalyanaraman (2011) approach. Standarderrors are clustered at the firm level. The estimates represent the mean predicted differences for firms just meetingforecasts relative to firms missing forecasts. Forecast errors are Street earnings minus median analyst forecastsfrom a 2-quarter horizon, scaled by firm assets as a percentage. Investment Rate is the percentage tangible annualinvestment rate. Intangibles growth is annual percent selling, general, and administrative expenditures growth. R&Dgrowth is annual percent research and development expenditure growth. CEO Pay is the log of total pay for theCEO. Abnormal Returns are the cumulative abnormal returns for a firm in a ten-day window to the announcementdate, market adjusting using the returns of the S&P 500. For returns forecasts are drawn from a 1-quarter horizon.a Already in normalized form, so these values duplicate discontinuity estimates.
Bandwidth Checks Placebo Checks BB Check Bunching & Industry Bunching & Time Dynamics
9
Roadmap
Data and Empirical Discontinuities
Model of Profit Pressure and Growth
Estimated Costs of Short-Termism
Agency Benefits from Short-Term Pressure
10
Household and Final Goods
The final goods sector is competitive and straightforward
max{Xjt}j ,LDt
Yt −∫ 1
0
pjtXjtdj − wtLDt
Yt =LDt
β
(1− β)
∫ 1
0
[Qjt(ajt + εjt)]βX1−βjt dj
The household owns the firms with no aggregate uncertainty
maxCt,Bt+1,{Sjt}j
∞∑t=0
ρtC1−σt
1− σ
Ct +Bt+1 +
∫ 1
0
PjtSjtdj = RtBt + wtL+
∫ 1
0
(Pjt +Djt)Sjt−1dj
Technology Scale
11
Firm Shocks & Quality LadderShocks
Persistent profitability shock ajt and transitory profitabilityshock εjt in each period:
ajt = (1− ρa) + ρaajt−1 + ζjt, ζjt ∼ N(0, σ2a)
εjt ∼ N(0, σ2ε)
Quality Ladder
Long-term quality Qjt , R&D investment zjtQjt, andstep size λ > 1 with probability Φ(zjt)
Φ(zjt) = Azαjt
Qjt+1 =
{λQjt, with probability Φ(zjt)max(Qjt, ωQt+1), with probability 1− Φ(zjt)
12
Street Profits & Forecasts
Accounting Profits: Follows FASB Rule No. 2 (1974)
ΠStreetjt = Πv(Qjt, ajt, εjt, pjt)− zjtQjt +mjtQjt
ΠStreetjt = (pjtXjt − ψXjt)− zjtQjt +mjtQjt
πStreetjt ≡ ΠStreetjt
Qjt
Forecasts by Equity Analyst Sector
MSE Loss, Current-Period Information Set
πfjt+1 = E(πStreetjt+1 |πStreetjt
)Follows work on analyst incentives, turnover, and forecast process
Analysts
13
Manager Dynamic Problem
max{zjt,mjt,pjt}t
E
{ ∞∑t=0
(1
R
)tDMjt
}DMjt = θdDjt − ξI(ΠStreet
jt < Πfjt)Qjt
Djt = Πv(Qjt, ajt, εjt, pjt)− zjtQjt − γmm2jtQjt
Manager choice of policy for R&D zjt, manipulation mjt, and price pjt.
Miss CostsThe ξ term combines losses due to manager private costs, firm real costs,or manager compensation cuts.
Manipulation CostsParameter γm indexes difficulty of or disruption from paper manipulation,non-GAAP practices, one-time items, reversals, etc.
Miss Costs Discounting
14
A Recursive Problem and Three
GE Fixed Points
Recursive Problem: normalize the manager objective byaverage quality Qt, then express as an equivalent stationaryBellman equation which holds on a balanced growth path
Three Fixed Points: must iterate to convergence on guessesfor the growth rate as well as an analyst forecast function,which jointly imply a stationary distribution µ(a, q, πf , ε)
1. Interest & growth rates from the HH Euler equation
2. Growth rate is consistent with R&D policies z and µ
3. Forecasts are model-consistent given µ
Recursive 3 Fixed Points Numerical Solution ME Tax Treatment
15
Roadmap
Data and Empirical Discontinuities
Model of Profit Pressure and Growth
Estimated Costs of Short-Termism
Agency Benefits from Short-Term Pressure
16
Fixing Model Parameters
Calibration of Some Common Parameters
Parameter Explanation Source, Value
σ CRRA Hall (2009), 2.0ρ Discount rate Annual interest rate 6%, 0.98β Labor share NIPA, 0.67L Human capital scale Normalization, 1.0α R&D curvature Acemoglu, et al. (2013), 0.5λ Quality step size 25% increment, 1.25ω Quality diffusion boundary Normalization, 0.08θd Manager equity share Nikolov & Whited (2013), 5.1%
GMM Estimation of Remainder
Parameter Explanation Parameter Explanation
ρa Pers. shock autocorr. ξ Miss costσa Pers. shock vol. A R&D levelσε Transitory shock vol. γm Manipulation cost
17
Structural Estimation Procedure
θ = (ρa, σa, σε, A, γm, ξ)′︸ ︷︷ ︸
6×1
m(X) = (V ec(Cov(∆s,∆z,%fe))′, g)′︸ ︷︷ ︸7×1
Overidentified GMM
θ = arg minθ
[m(θ)−m(X)]′W [m(θ)−m(X)]
Implementation
Diagonal weighting matrix, with extra weight on macro growth g.
Robust global stochastic optimization technique (particle swarmoptimization) akin to simulated annealing.
Model Definitions
18
Parameter Estimates
Parameter Explanation Estimate (SE)
ξ Miss costs 0.001 (0.0006)ρa Prof. persistence 0.903 (0.0325)σa Prof. volatility 0.070 (0.0029)σε Transitory shock vol. 0.099 (0.0071)A R&D level 0.256 (0.1168)γm Manipulation cost 0.290 (0.3679)
Note: The parameter estimates above are computed from an unbalanced panel of income statement and forecastdata, with 4,839 firms and 32,597 firm-years from 1982-2010 in the US, together with data on the US per capitareal GDP growth rate. The estimation procedure is standard overidentified GMM, with a moment covariance matrixreflecting time series correlation of the aggregate growth rate using a stationary bootstrap and arbitrary time seriescorrelations within firm-level clusters for all microeconomic moments. Optimization was performed using particleswarm optimization, a stochastic global minimization routine. The weighting matrix is chosen so that the GMMobjective equals the sum of squared percentage deviations, with 10 times extra weight placed on the aggregatemoment. Asymptotics are computed in the number of firms while assuming independence between aggregate andmicroeconomic moments in the data.
Identification Miss Cost Magnitude
19
Estimated Model & Data Moments
Micro Data
% Data Baseline No Targets
σ(R&D Growth) 30.1 27.7 16.1σ(Sales Growth) 25.9 22.0 22.0σ(Fcst. Error) 36.4 24.2 21.8
Corr(R&D Growth, Sales Growth) 0.36 0.41 0.47Corr(R&D Growth, Fcst. Error) -0.001 -0.03 0.05Corr(Sales Growth, Fcst. Error) 0.09 0.29 0.65
Aggregate Growth Rate
% Data Baseline No Targets
g 1.98 2.25 2.31
Note: The data moments from the covariance matrix of sales growth, R&D growth, and forecast errors above arecomputed from the estimation sample composing a panel of US firms in Compustat and I/B/E/S, with 4,839 firmsand 32,597 firm-years from 1982-2010. σ implies standard deviation, “Corr” implies correlation. The aggregategrowth rate is the mean US per capita real GDP annual growth rate. The Baseline moments are computed fromthe stationary distribution of the estimated baseline model, while the No Targets figures are computed from thecounterfactual model stationary distribution with no manager miss cost, i.e. ξ = 0, holding all other parametersfixed at Baseline levels.
20
The Estimated Forecast Error
Distribution is Kinked
−0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.30
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Forecast Error = (Actual − Forecast)/Quality
Den
sity
No Earnings TargetsEstimated Targets
Student Version of MATLAB
Note: The figure above represents the distribution of forecast errors π−πf computed from the stationary distribution
of the balanced growth path associated with the estimated miss cost ξ (in red) and the counterfactual ξ = 0 (inblack). The model is a calibrated version of the Baseline including ex-ante measurement error of targets on the partof firms.
Cross Sectional Bunching Information Release21
Firms Just Meeting Forecasts
Have Lower R&D Growth
(Actual - Forecast)/Quality-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3
R&D
Gro
wth
, %
-10
-5
0
5
10
15
20
25
30
35No Earnings TargetsEstimated Targets
Note: The figure plots average R&D growth in the estimated benchmark model with miss cost ξ (in red) and no miss
costs (in black) conditional upon the forecast error π − πf . R&D growth series were computed from a simulationof 500 firms over 1,000 years each, discarding the first 500 years of data to cleanse initial conditions. The model isa calibrated version of the Baseline including ex-ante measurement error of targets on the part of firms.
Dynamics22
R&D Reacts to Short-Run Shocks
Overall R&D z: ∆ Mean = -0.32%, ∆ St. Dev. = 23.1%
−0.15 −0.1 −0.05 0 0.05 0.1 0.1585
90
95
100
105
110
115M
ean
R&
D (
No
Tar
gets
= 1
00)
Transitory Shock ε
No TargetsEstimated Targets
Student Version of MATLAB
Note: The figure plots the mean R&D policy z in the counterfactual No Targets (in black, with ξ = 0) and Baseline
estimated model (in red, with ξ) conditional upon the value of the transitory profitability shock ε. For readability,the constant mean level of R&D z in the No Targets model is normalized to 100.
23
The Costs of Short-Termism
The economy without earnings pressure has higher growth,welfare, and firm value than the baseline.
% %
g, Targets 2.25 ∆ Welfare 0.44g, No Targets 2.31 ∆ Firm Value 1.03
100-yr ∆Y 5.82
Note: The entries above compare various aggregate quantities across the estimated baseline economy with Targets
(and ξ > 0) and the counterfactual No Targets economy with ξ = 0. The moments are computed from thestationary distributions µ of the respective economies, and comparisons are directly across balanced growth paths.∆ Welfare represents the percentage consumption equivalent variation of ξ = 0 relative to the baseline economy.
The change in firm value is the mean partial equilibrium percent change in firm value when ξ = ξ → ξ = 0 for an
individual firm, averaged over the stationary distribution of ξ. The 100-year change in Y is the percentage differencein output after 100 years from a No Targets growth rate rather than a Targets growth rate, using identical initialconditions.
Magnitudes Welfare Robustness Model Aggregates Forecast Accuracy Forecast Form
24
Roadmap
Data and Empirical Discontinuities
Model of Profit Pressure and Growth
Estimated Costs of Short-Termism
Agency Benefits from Short-Term Pressure
25
Agency Conflicts within Firms
A rich literature ties firm investment to agency conflicts withmanagers, potentially motivating earnings target incentives.
dm = θdd− ξpay(1− θd)I(π < πf )q + ( Manager Private Payoffs )
Shirking: managers may not provide enough unobservable effort(Grossman & Hart 1983, Edmans, Gabaix, & Landier 2009)
s ∈ {0, 1}, Manager Private Payoffs = λssq, Firm Cost = γssΠv
Empire Building: managers may have a taste for scale leading tooverinvestment (Jensen 1986, Nikolov & Whited 2010)
Manager Private Payoffs = λeq
Quantitative Exploration: fix ξpay(1− θd) = ξ, vary λe or λs, γs
26
Targets Can Increase Effort
0.75 1 1.25 1.50
20
40
60
80
100Shirking with Targets
Per
cent
0.75 1 1.25 1.50
5
10
15Shirking Change
0.75 1 1.25 1.5
−1
0
1
2Firm Value Change
Relative Shirking Motive
Per
cent
0.75 1 1.25 1.5
−1
0
1
2
Relative Shirking Motive
Welfare Change
Student Version of MATLAB
Note: Horizontal axis is r(λs) = λs/E(θdΠvγs/q), where γs = 0.075. The top left panel plots the averageshirking level 100Eµs with targets, the top right panel plots the percent difference in shirking from target removal,the bottom left panel plots the average PE percent change in firm value from target removal, and the bottom rightpanel plots the GE total consumption equivalent percent change in social welfare from target removal. Numericalcomparative statics are smoothed using a polynomial approximation.
Lower Firm Cost27
Targets Can Restrain Empires
0.21 0.23 0.25 0.27 0.2914.5
15
15.5
16R&D to Sales with Targets
Per
cent
0.21 0.23 0.25 0.27 0.290
0.5
1
1.5
2R&D to Sales Change
0.21 0.23 0.25 0.27 0.29
−0.2
0
0.2
0.4
Relative Empire Motive
Firm Value Change
Per
cent
0.21 0.23 0.25 0.27 0.29
−1
0
1
2Welfare Change
Relative Empire Motive
Student Version of MATLAB
Note: Horizontal axis is r(λe) = λe/E(θdΠv/q). The top left panel plots the average R&D to sales ratio withtargets, the top right panel plots the percent difference in the R&D to sales ratio from target removal, the bottomleft panel plots the average PE percent change in firm value from target removal, and the bottom right panel plotsthe GE total consumption equivalent percent change in social welfare from target removal. Numerical comparativestatics are smoothed using a polynomial approximation.
Smooth vs. Target28
Conclusion
Estimated a Set of Empirical DiscontinuitiesLower long-term investment growth, discontinuous CEOcompensation and stock returns when just meeting forecasts
Built a Model of Manager Long-Term InvestmentGE endogenous growth, firm earnings forecasts, idiosyncraticprofitability shocks, manager R&D and paper manipulation choices
Estimated Dynamic Costs of Short-TermismMicro: 25% more volatile R&D, -1% ∆ firm valueMacro: -0.1% lower growth, -0.4% lower welfare
Explored Agency Benefits from Short-Term PressureShirking: social and firm incentives alignedEmpire Building: social and firm incentives misaligned
29
Next Steps
NSF R&D MicrodataFull coverage of public and private firm R&D, detailed breakdownof expenditure categories
Business-Cycle ImplicationsShort-term incentives may amplify R&D over the cycle
Some other Related Mechanisms- Finite CEO horizon effects- Firm decision to go public- High hurdle rates within firms- Fixed project repayment horizons
30
Backup Slides
31
Smooth Incentives vs. TargetsThe baseline model assumes threshold or target incentives. Howmuch would firms gain from a smoother incentive structure?
dm = θdd+(1−θd)N∑k=1
β∗k(πf−π)kq+( Manager Private Payoffs )
Optimize Average Firm Value: β∗ = arg maxβ
Eµ (V |β)
% of Firm Value Gains over Targets
Shirking 0.64Empire Building 0.90
Note: The entries are the mean percentage change in firm value from the use of optimal smooth incentives relativeto the use of estimated target incentives, in partial equilibrium. Averages are taken with respect to the unconditionaldistribution of the model given target incentives, and the results are computed assuming a polynomial of degreek = 3. The “Shirking” row imposes agency parameters λs = 0.002 and γs = 0.075, chosen to deliver themaximum firm value gain from targets relative to no incentives (around 1%). The “Empire Building” row imposesagency parameter λe = 0.006, chosen to deliver approximate firm indifference between targets and no incentives.Both are moderate calibrations approximately in the center of the investigated ranges for agency conflict parameters.
Return
32
R&D Dynamics: Data & Model−6
−4−2
02
46
Data
Year
R&D
Gro
wth
, % D
iffer
ence
0 1 2
−10
−50
510
Model
Year
0 1 2
No Earnings TargetsEstimated Targets
Note: Both panels plot in solid lines the estimated discontinuity in annual percentage R&D growth for firms justmeeting relative to just missing analyst forecasts. Year k on the horizontal axis reports estimates based on yeart + k R&D growth and year t forecasts. All estimates are locally and nonparametrically computed using a locallinear regression discontinuity estimator, with a running variable equal to earnings forecast errors normalized by firmassets (data, bandwidth from Imbens and Kalyanaraman (2011)) and firm quality q (model, bandwidth = 0.2). Thedata panel reports 90% pointwise confidence intervals (dotted lines). The model panel reports estimates from the
baseline model (in red, with ξ) and from the model with no earnings targets (in black, with ξ = 0). Data estimatesrely on the baseline Compustat-I/B/E/S discontinuity estimation sample with 23,083 firm-years spanning 1983-2010with 3,969 firms. Model estimates rely on simulation of 500 firms over 1,000 years each, discarding the first 500years of data to cleanse initial conditions.
Return 33
Bunching: Data & Model
Forecast error bunching is correlated across industries in thedata with R&D sensitivity and R&D intensity.
Varying Earnings Pressure
ξ Bunching R&D Sensitivity
0.8ξ 3.67 0.27
ξ 8.45 0.33
1.2ξ 13.14 0.43
Varying R&D Productivity
A Bunching R&D Intensity
0.8A 7.86 3.44
A 8.45 7.36
1.2A 10.13 8.81
Note: The left table reports moments from varying the miss costs ξ around their estimated value ξ, and the right
table reports moments from varying the R&D productivity parameter A around its estimated value A. Bunchingis the ratio of the mass of firm-years just meeting to just missing analyst forecasts, using a bandwidth of 0.2 in
units of firm quality q. R&D sensitivity is the asymptotic limit of the coefficient β from the regression (R&DGrowth)jt = β (Sales Growth)jt + εjt. R&D intensity is the mean ratio of R&D expenditures to firm assets. Forthese cross-industry experiments, the aggregate growth rate and interest rates are held at their baseline values, butthe analyst forecast system re-adjusts to a new fixed point. Results rely on simulation of 500 firms over 1,000 yearseach, discarding the first 500 years of data to cleanse initial conditions.
Return
34
Targets Can Increase Effort
0.75 1 1.25 1.50
20
40
60
80
100Shirking with Targets
Per
cent
0.75 1 1.25 1.50
5
10
15Shirking Change
0.75 1 1.25 1.5
−1
0
1
2Firm Value Change
Relative Shirking Motive
Per
cent
0.75 1 1.25 1.5
−1
0
1
2
Relative Shirking Motive
Welfare Change
Student Version of MATLAB
Note: Horizontal axis is r(λs) = λs/E(θdΠvγs/q), where γs = 0.025. The top left panel plots the averageshirking level 100Eµs with targets, the top right panel plots the percent difference in shirking from target removal,the bottom left panel plots the average PE percent change in firm value from target removal, and the bottom rightpanel plots the GE total consumption equivalent percent change in social welfare from target removal. Numericalcomparative statics are smoothed using a polynomial approximation.
Return35
Some Perspective on the CostsHow do consumption equivalent costs on the order of 0.44%, equal to$51 billion per year in 2013 dollars in the US, compare to otherquantitative welfare cost calculations at the macro level?
Business Cycle Costs ≈ 0.1-1.8% Krusell, et al. (2009)
Gains from Trade ≈ 2.0-2.5% Costinot & Rodriguez-Clare (2013),Melitz & Redding (2013)
Inflation ≈ 1.0% Algan, et al. (2015)
Misinformed Investors ≈ 2.4% Hassan & Mertens (2013)
How does an average firm value cost of around 1% compare to otherquantitative corporate finance calculations at the micro level?
CEO Firing Friction Costs ≈ 3% Taylor (2010)
Cash Agency Costs ≈ 6% Nikolov and Whited (2013)
Return
36
Overlapping Manager ProblemsManager t for firm j has the following life cycle:
1. Born at end of t− 1
2. Evaluates take-it-or-leave-it offer χMjt−1 from t− 1 manager
rjt−1 = arg maxr
(1− r)[−RχMjt−1 + Et−1
(DMjt + χMjt (1− rjt)
)]3. Chooses zjt, mjt, and pjt, as well as offer χMjt to maximize period t
linear utility
maxzjt,mjt,pjt,χMjt
(−RχMjt−1 +DM
jt + χMjt (1− rjt))
4. Dies after consumption in period t
Backward Induction: yields straightforward continuation value pricingfor χMjt and the equivalent discounted manager flow utility maximizationproblem for manager policy stream {zjt,mjt, pjt}t.
Return
37
Linear Forecast Rule
πf = η0 + η1π−1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Lagged Earnings π−1
Cur
rent
Ear
ning
s π
Identity
Linear Forecast πf
Conditional Mean E(π|π−1
)
Student Version of MATLAB
Note: The figure plots the linear forecast of normalized earnings πf , together with the conditional mean of earnings
E(π|πf ), given lagged earnings π−1, with expectations taken over the stationary distribution of the Baseline model.The model was solved via discretization, policy iteration, and nonstochastic simulation.
Return
38
What are Earnings?
Rough DefinitionRevenues- Production Expenses- Nonproduction Expenses- Depreciation- TaxesEarnings
Also known as profits,net income, or thebottom line.
Regulated by GAAPstandards, whichdiffer by country.
US Context: Since SFAS Rule 2 in 1974, US GAAP requiresfull expensing of almost all R&D costs. Broader intangibleinvestment (SG&A costs) are also fully expensed.
Analyst Earnings Forecasts: published before accountingreleases for public firms, followed widely during earnings season
Return 39
Robustness Checks% ∆g ∆Wstat ∆Wdyn ∆W % ∆E (R&D) % ∆σ (R&D)
σa = 0.04 0.11 -1.09 2.51 1.40 7.20 -23.12σa = 0.12 0.06 2.74 1.29 4.06 5.11 -11.20σε = 0.06 0.06 -0.71 1.27 0.55 0.63 -22.42σε = 0.14 0.06 -0.06 1.29 1.23 3.80 -29.31ρa = 0.85 0.06 -1.66 1.36 -0.33 4.17 -45.52ρa = 0.95 0.06 0.22 1.45 1.67 3.98 -10.41A = 0.21 0.05 -0.32 1.25 0.92 2.25 -5.57A = 0.275 0.06 -0.13 1.32 1.19 3.68 -23.05γm = 0.25 0.05 -0.57 1.18 0.61 3.18 -31.37γm = 0.35 0.07 -0.82 1.48 0.65 0.90 -26.13γm =∞ 0.05 -1.04 1.12 0.08 4.50 -54.54
ξ = 0.5ξ 0.05 -1.37 1.04 -0.34 0.17 -22.74
ξ = 2.0ξ 0.13 -0.30 2.96 2.64 6.53 -44.27α = 0.4 0.07 -0.86 1.55 0.68 8.08 -30.93α = 0.6 0.08 -0.15 1.94 1.79 2.07 -25.90β = 0.5 0.07 0.21 1.84 2.06 2.28 -25.01λ = 1.2 0.05 -0.32 1.57 1.25 8.86 -35.29
ω = 1/√
175 = 0.076 0.06 -1.54 1.36 -0.20 3.21 -27.24
ω = 1/√
125 = 0.089 0.05 0.02 1.2 1.22 2.16 -19.69Random Walk Forecast 0.01 1.44 0.15 1.57 1.10 -25.89
Quadratic Fcst 0.07 0.05 1.62 1.67 3.69 -23.85Fcst Bias = 0.01 0.08 -0.80 1.82 1.01 6.05 -31.90
Fcst Bias = −0.01 0.06 -0.86 1.32 0.44 0.32 -23.06Target Measurement Error 0.10 -1.04 2.21 1.15 9.24 -14.09
Baseline 0.06 -0.86 1.32 0.44 0.32 -23.1
Note: The entries above represent percent differences between the counterfactual No Targets and estimated Targetscases. The moments are computed from the stationary distributions µ of the respective economies.
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Identification Summary
6 Parameters: ρa, σa, σε, A, ξ, γm
7 Moments: g, Var(∆z), Var(%fe), Var(∆s),Cov(∆z, ∆s), Cov(∆z, %fe), Cov(∆s, %fe)
A joint and not 1-to-1 mapping. However, Gentzkow and Shapiro (2014)sensitivity estimates recover coefficients of a theoretical regression of GMMparameter estimates on moments over their joint asymptotic distribution.
Gentzkow and Shapiro (2014) Sensitivity Highlights:g → A, innovation arrival to growthVar(∆z), Var(%fe) → σa, R&D reacts to persistent shocksVar(∆s) → σε, transitory profitability feeds through to salesCov(∆z, ∆s) → ρa, R&D reacts to persistent shocksVar(∆z), Cov(∆z, %fe) → ξ, earnings targets cause R&D volatilityCov(∆s, %fe) → γm, paper manipulation dampens passthrough
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Stationary Manager Problem
VM (a, ε, q, πf ) = maxz,m
{θdd− ξI(π < πf )q +
(1 + g
R
)EVM (a′, ε′, q′, πf
′)
}d = β(a+ ε)qL− zq − γmm2
π = β(a+ ε)L− z +m
a′ = (1− ρa) + ρaa+ ζ ′, ζ ′ ∼ N(0, σ2a), ε′ ∼ N(0, σ2
ε)
q′ =
{ λq1+g , with prob. Φ(z) = Azα
max{
q1+g , ω
}, with prob. 1− Φ(z)
πf′
= Eµ (π′|π)
ξ = ξmanager + θdξfirm + (1− θd)ξpay
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42
Three GE Fixed PointsGiven a stationary distribution µ(a, q, πf , ε) for the model,three consistency conditions must hold:
1. Interest rates are endogenous from the HH Euler equation
R =1
ρ(1 + g)σ
2. BGP growth rate aggregates from micro policies
1+g =Q′
Q=
∫φ(z)λqdµ(a, q, πf , ε)
+∫q>ω(1+g)
(1− φ(z))qdµ(a, q, πf , ε)
+∫q≤ω(1+g)
(1− φ(z))ω(1 + g)dµ(a, q, πf , ε)
3. Forecasts are rational projections given current Street π
πf = Eµ(π|π−1)
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Welfare Calculations
On a balanced growth path, consumption equivalent welfarechanges can be decomposed into static vs dynamic components
Total︸ ︷︷ ︸∆W = 0.44
=Cnotargets
Ctargets︸ ︷︷ ︸Static, ∆W static = -0.86
(1− ρ(1 + gtargets)1−σ
1− ρ(1 + gnotargets)1−σ
) 11−σ
︸ ︷︷ ︸Dynamic, ∆W dynamic=1.32
The number ∆W reported in the main slide is converted topercent changes for each of reference.
Note: These calculations reflect comparisons across steady statebalanced growth paths, conservatively assuming that ξ does notrepresent a resource cost.
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Numerical Solution Details
Discretization, policy iteration,nonstochastic simulation ofstationary distribution.
Hybrid bisection andfixed-point iteration on growthrate and polynomial forecastapproximation.
Heavy parallelization inFortran.
Grid Size
I na = 7
I nq = 25
I nπf = 20
I nε = 3
I nz = 15
I nm = 15
Average solution in 8 minutes.
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45
Model Definitions
All growth rates and forecast errors make use of bounded,robust Davis & Haltiwanger (1992)-style rate definitions.
Sales Growth: 2(ajt+εjt)QjtL−(ajt−1+εjt−1)Qjt−1L
(ajt+εjt)QjtL+(ajt−1+εjt−1)Qjt−1L
R&D Growth:
{2zjtQjt−zjt−1Qjt−1
zjtQjt+zjt−1Qjt−1, zjt 6= 0 or zjt−1 6= 0
0, zjt = zjt−1 = 0
% Fcst. Error:
2πjt−πfjt|πjt|+|πfjt|
, πjt 6= 0 or πfjt 6= 0
0, πjt = πfjt = 0
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46
R&D Expensing Guidelines
US GAAP
Policy: fully expensed in general
Exception 1: tangible assets in R&Dprocess with other uses
Exception 2: development costs forsoftware, after feasibility proof, sinceRule 86 in 1985
Source: FASB rules, EY (2011)
EU Rules
EU countries have harmonized throughIFRS standards
Policy: fully expensed in general
Large Exception: optional accrual ofdevelopment costs of successful R&D
Source: IASB rules, EY (2011), PWC(2013), Dinh, et al. (2011)
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47
Aggregate Quantities
On a balanced growth path with normalization of averagequality Qt = 1, aggregate quantities are given by:
Output: Y = β∫aqLdµ(a, q, πf , ε), β = 2β(1−β)
1−β
R&D: Z =∫zqdµ(a, q, πf , ε)
Firm Disruption Costs:Ξfirm = ξfirm
∫I(π < πf )qdµ(a, q, πf , ε)
Manipulation Costs: ACm =∫ACm(m)qdµ(a, q, πf , ε)
Consumption: C = Y − Z −ACm − Ξfirm
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48
Corporate Taxation
ΠTaxjt = Πv(Qjt, ajt, εjt, pjt)− zjtQjt
Taxes to Apply: corporate income tax rate τc
Net Taxes Paid before Dividends
τcΠTaxjt
Numerical Results: assume τc = 35% (US Code)Return
49
Magnitude of Miss Costs
The estimated miss cost parameter is in model units relativeto unobserved firm quality q.
Observable Relative Measures: When a firm misses anearnings target in the estimated model, ξ = 0.001 impliesmanager indifference between an earnings miss and 3.6% offirm period revenues on average.
Taylor (2010): Structurally estimated costs from CEO firingequal to 5.9% of firm assets (≈ 8.9% of revenues in my data)
Zbaracki, et al. (2004): Directly observed price adjustmentcosts around 1.2% of revenues.
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50
Adding Measurement Error
In practice, managers may not perfectly observe their earningstargets when making decisions. I also allow for a version withtwo transitory earnings shocks:
Observed before Policy: εjt ∼ N(0, σ2ε)
Unobserved Target ME: νjt ∼ N(0, σ2ν)
ΠStreetjt = β(ajt + εjt)QjtL+ νjtQjt − zjtQjt +mjtQjt
All other details, including the form of persistent profitabilityshocks ajt, are identical.
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51
Interpreting Miss Costs ξ
ξ = ξmanager + θdξfirm + (1− θd)ξpay
Manager Private Costs ξmanager: career or reputational concerns,earnings call disruption, etc. (Dichev, et al. 2013)
Firm Costs ξfirm: litigation likelihood, stock price, debt covenants,manager time disruption (Graham, et al. 2005, Zbaracki, et al. 2004)
Manager Pay Costs ξpay: earnings target conditional pay (Matsunaga& Park 2001)
The components of ξ aren’t separately identified. For conservatism,benchmark cost calculations set ξ = ξmanager and therefore rebate thedirect effect to firm value and welfare.
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52
A Miss Releases Information
Miss Meet
Mea
n Pr
ofita
bilit
y a
0.85
0.9
0.95
1
1.05
1.1No Earnings TargetsEstimated Targets
Note: The figure above represents the conditional mean of profitability a for firms missing their forecasts (π < πf ),
and firms meeting their forecasts (π ≥ πf ), computed from the stationary distribution of the balanced growth path
associated with both the estimated miss cost ξ (in red) and ξ = 0 (in black). The difference in mean profitabilityfrom missing is −15.1% in the estimated baseline, compared to −13.8% for ξ = 0. The model is a calibratedversion of the Baseline including ex-ante measurement error of targets on the part of firms.
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Final Goods Technology
Technology follows Acemoglu, Akcigit, and Celik (2014), Akcigitand Kerr (2010), and Acemoglu and Cao (2010). Jones andWilliams (2000) notes gross markup tied to inverse capital share.
Target Cost Estimates: robustness to β = 1/2 for smallermarkup than baseline β = 2/3, with similar results
CES Alternative: breaks capital share-markup link, but requiresadditional fixed point, currently infeasible given model structuralestimation with numerical solution and heterogeneous firms
Social vs. Firm: quantitative difference between social vs. firmsurplus important only for empire-building extension, with aqualitative approach for that portion
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Scale Effects
Fixed L generates growth with fixed population size, similar torecent quantitative work by Peters (2013), Acemoglu, Akcigit,Bloom, & Kerr (2013), Akcigit, Hanley, & Serrano-Velarde (2014).
Strong Scale Effects: inconsistent with Jones (1995)
Numerical Robustness: Bloom, Romer, Terry, & Van Reenen(2015) examines a calibrated strong scale effects model withquantitatively similar welfare results to an alternative weak scaleeffects model, with effect driven by discounting and persistence
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Bandwidth Robustness
Note: Solid lines are estimated discontinuities at the zero forecast error cutpoint. Dotted lines are 90% confidenceintervals. The regression discontinuity estimates plotted above range from 50% to 200% of the benchmark bandwidthsfor the regression discontinuity estimates.
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56
Placebo ChecksVariable -0.15% Cutpoint 0.15% Cutpoint
Investment Rate -0.44 0.44(0.37) (0.40)
Intangibles Growth 0.26 -0.55(0.55) (0.53)
R&D Growth 0.81 -0.88(1.00) (0.93)
CEO Pay -3.89 0.39(3.29) (3.66)
Abnormal Returns -0.28 -0.20(0.26) (0.22)
Note: The regression discontinuity estimation relies on local linear regressions and a triangular kernel, with bandwidthchosen via the optimal Imbens and Kalyanaraman (2011) approach. Standard errors are clustered at the firm level.The estimates represent the mean predicted differences for firms just meeting earnings forecast cutpoints relativeto firms just failing to meet forecast cutpoints, for placebo checks at -0.15% and 0.15% forecast errors. Earningsforecast errors are Street earnings minus median analyst forecasts from a 2-quarter horizon, scaled by firm assets as apercentage. Investment Rate is the percentage tangible annual investment rate. Intangibles growth is annual percentselling, general, and administrative expenditures growth. R&D growth is annual percent research and developmentexpenditure growth. CEO Pay is the log of total pay for the CEO. Abnormal Returns are the cumulative abnormalreturns for a firm in a ten-day window to the announcement date, market adjusting using the returns of the S&P500. For returns analyst forecasts are drawn from a 1-quarter horizon.
Return 57
Broad Sample Coverage
with Large Firms
R&D Expenditures: 67% of private US spending, 61% oftotal US spending (2000, NSF)
Employment: 11% of total US employment (2000, BLS)
Sales: 31% of US GDP (2000, BEA)Return
58
Descriptive Statistics
Mean Median Standard Deviation
Assets 4007.7 599.7 15977.9Revenues 3505.3 610.5 11804.5Employment 15.5 3.3 50.8Intangibles 730.7 136.7 2301.4R&D 135.0 14.9 519.9Street Earnings 245.7 32.9 940.2
Note: Assets, Revenues, Intangibles, R&D, and Street Earnings in millions of dollars. Employment in thousands.Intangibles represents selling, general, and administrative expenditures. R&D represents total research and devel-opment expenditures. Statistics computed from the forecast error discontinuity detection sample in the year 2000,covering 920 firms and 217 4-digit SIC industries.
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59
Alternative Forecast Systems
Add Little Accuracy
Baseline Rule: πf = η0 + η1π−1
Higher-Order Terms RMSE New Information Terms RMSE
Mean Only 1.0000 Mean Only 1.0000Add η1π−1 0.8998 Add η1π−1 0.8998
Add η2π2−1 0.8993 Add η2(π−1 − πf−1) 0.8852
Add η3π3−1 0.8993 Add η3z−1 0.8801
Note: All statistics are computed using the stationary distribution µ of the Baseline model, based on a forecast
system of πf = η0 + η1π−1. RMSE is the root mean squared error of a given forecasting rule, i.e. for system
i, RMSEi =
√Eµ(πfi − π
)2, where π
fi is the forecast from system i and π is model Street earnings from
the Baseline. Each column reports the scaled value of RMSEi/RMSE1, where RMSE1 is the RMSE implied by theforecast rule with only a constant or mean prediction. Movement down rows within each column tracks forecastaccuracy improvement when sequentially adding terms to the mean only forecast rule.
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60
Block Bootstrapped Estimates(1) (2) (3) (4) (5)
Method Local Linear Local Linear Local Linear Local Linear Local LinearDependent Variable Investment Rate Intangibles Growth R&D Growth CEO Pay Abnormal ReturnsRunning Variable Forecast Error Forecast Error Forecast Error Forecast Error Forecast ErrorCutpoint 0 0 0 0 0
Discontinuity 0.40 -2.67*** -2.63* 6.89*** 0.67***(0.39) (0.92) (1.56) (2.59) (0.21)
Effects Firm, Year Firm, Year Firm, Year Firm, Year Market-AdjustedYears 1983-2010 1983-2010 1983-2010 1992-2010 1983-2010Firms 3969 3969 3969 2349 7794Observations 23084 23084 23084 17661 48297Relative to Mean 1.0% -27.2% -33.7% 6.89%a 0.67%a
Note: *,**,*** denote 10, 5, 1% significance. The results reflect a block bootstrap procedure. Draws of data blockswere sampled with replacement from the distribution of firms, taking into account within-firm correlation as wellas uncertainty surrounding variable demeaning by firm and year and the estimation of the regression discontinuityitself. The point estimates are the mean, and the standard errors are the standard deviation, over 250 bootstrapreplications. The regression discontinuity estimation relies on local linear regressions and a triangular kernel, withbandwidth chosen via the optimal Imbens and Kalyanaraman (2011) approach. The estimates represent the meanpredicted differences for firms just meeting earnings forecasts relative to firms just missing. Forecast errors are Streetearnings minus median analyst forecasts from a 2-quarter horizon, scaled by firm assets as a percentage. InvestmentRate is the percentage tangible annual investment rate. Intangibles growth is annual percent selling, general, andadministrative expenditures growth. R&D growth is annual percent research and development expenditure growth.CEO Pay is the log of total pay for the CEO. Abnormal Returns are the cumulative abnormal returns for a firm in aten-day window to the announcement date, market adjusting using the returns of the S&P 500. For returns analystforecasts are drawn from a 1-quarter horizon.a Already in normalized form, so these values duplicate discontinuity estimates.
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61
Bunching Across Industries
●●
●
●●
●●
●
●
●
●
●
●●
●
0 20 40 60 800.
00.
20.
40.
6
R&D Sensitivity
R&D
to S
ales
, Ela
stic
ity
Slope = 0.003, R^2 = 0.16
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
0 20 40 60 80
02
46
810
12
R&D Intensity
R&D
/ As
sets
, Per
cent
Slope = 0.06, R^2 = 0.21
●
●
●
●
●
●
●
●●● ●
●
●
●
●
0 20 40 60 80
34
56
78
9
Analyst Coverage
P(Just Meet) / P(Just Miss)
Num
ber o
f Ana
lyst
s
Slope = 0.016, R^2 = 0.078
●
●
●
● ●
●
●
●
●
●
●
●● ●●
0 20 40 60 801.
52.
53.
54.
5
Forecast Dispersion
P(Just Meet) / P(Just Miss)
Inte
rqua
rtile
Ran
ge, P
erce
nt
Slope = −0.01, R^2 = 0.085
Note: Horizontal axis is the ratio of firm-years just meeting to just missing forecasts in a 4-digit SIC industry cell,
based on a 0.05% bandwidth relative to firm assets. Top left panel is the empirical elasticity β from estimates of(R&D Growth)jt = β (Sales Growth)jt + fj + gt + εjt. Top right panel is the median R&D to assets ratio.Bottom left panel is the median number of analysts per firm. Bottom right panel is the median interquartile rangeof analyst forecasts, relative to firm assets. Sample based on the baseline Compustat-I/B/E/S estimation sample
with 23,083 firm-years spanning 1983-2010. Fitted lines, slopes, and R2’s included for reference.
Return62
Bunching Across Time
●
●
●● ●●
●
●
●
●●
●
●●
● ● ●●
●●●
●●●
●
●
●
●
4 6 8 10−2
02
46
Real GDP Growth
Perc
ent
Slope = 0.22, R^2 = 0.062
●
●
●
●
●●
● ●●
●
●
●
●
●●●
●
● ●
●
●●●●
●
●
●●
4 6 8 10
3035
4045
5055
60
Firms Missing Forecast
Perc
ent
Slope = −2.3, R^2 = 0.26
●
●
●
●
●
●●
●
●●
●
●
●
●●●
●
●●
●
●●●●●
●
●●
4 6 8 10
−0.1
5−0
.05
0.05
0.15
Mean Forecast Error
P(Just Meet) / P(Just Miss)
Perc
ent
Slope = 0.014, R^2 = 0.18●
●
●● ●
●
●
●
●
●●
●●●● ●
●●●●●●
●●●
●●●
4 6 8 102
34
56
78
9
Forecast Dispersion
P(Just Meet) / P(Just Miss)
Inte
rqua
rtile
Ran
ge, P
erce
nt
Slope = −0.51, R^2 = 0.23
Note: Horizontal axis is the ratio of firms just meeting to just missing forecasts in a given year, based on a 0.05%bandwidth relative to firm assets. Top left panel is the annualized real GDP growth rate drawn from the US NIPAtables. Top right panel is the percent of firms missing forecasts. Bottom left panel is the mean forecast error relativeto firm assets. Bottom right panel is the median interquartile range of analyst forecasts, relative to firm assets.Sample of years spans 1983-2010 from the baseline Compustat-I/B/E/S discontinuity estimation sample with 23,083
firm-years. Fitted lines, slopes, and R2’s included for reference.
Return 63
Long-Term Investment Dynamics
−6−4
−20
24
6Intangibles Growth
Year
Perc
ent D
iffer
ence
0 1 2
−6−4
−20
24
6
R&D Growth
Year
0 1 2
Note: The solid line is the discontinuity in long-term investment growth for firms just meeting relative to just missinganalyst forecasts. Year k on the horizontal axis reports estimates based on the growth of long-term investment inthe year t + k with forecasts from year t. Intangibles growth and R&D growth are the annual percentage growthrate in selling, general, and administrative expenditures and research and development expenditures, respectively.The estimates are locally and nonparametrically computed using a local linear regression discontinuity estimatorwith bandwidth chosen according to the Imbens and Kalyanaraman (2011) approach. The running variables isforecast error or Street earnings minus median analyst forecasts from a 2-quarter horizon, scaled by firm assets as apercentage. Standard errors are clustered at the firm level, with 90% pointwise confidence intervals plotted in dashedlines. Sample drawn from the baseline Compustat-I/B/E/S discontinuity estimation sample with 23,083 firm-yearsspanning 1983-2010 with 3,969 firms.
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R&D Expenditure Shares
NSF R&D survey includes the universe of public and private firmswith over $1 million in R&D expenditures. Microdata availablefrom US Census Bureau through RDC projects.
% All Industries Manufacturing Large Firms
Salaries 48.4 44.5 43.4Benefits 13.5 13.6 11.1
Temporary Staff 2.5 1.9 3.0Materials 7.7 9.1 7.8
Other 27.9 30.8 34.8
Note: Categories may not sum to 100 due to rounding. Statistics computed from the 2008 National ScienceFoundation and US Census Bureau Business R&D and Innovation Survey, Table 6. Manufacturing includes NAICScodes 31-33. Large firms have more than 25,000 employees. Salaries reflect permanent worker salaries. Benefitsinclude fringe benefits and stock compensation for permanent workers. Materials include the cost of both materialsexpenses and expensed equipment purchases. Other category includes lease and rental payments, depreciation ofequipment with alternative uses, and a residual category.
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65
Research on Equity Analysts
Accuracy & Turnover
Hong and Kubik (2003), Hong and Kacperczyk (2010), Marinovic,et al. (2012), Beyer and Guttman (2012)
Analysts have career incentives for accuracy and high turnoverrates, consistent with single-period rational forecasts assumption.In robustness checks allowed for upward commission bias.
Information Set
Brown, et al. (2015)
Surveyed analyst information sets crucially include current earningsperformance. In robustness checks have relaxed to include R&Dand lagged forecast errors.
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66
