Firm Innovation and Financial Analysis: How Do They Interact?cepr.org/sites/default/files/Peress, Joel pdf.pdf · 2016. 12. 13. · Overview • A model of financial development and

Firm Innovation and Financial Analysis: How Do They Interact?

Jim Goldman

University of Toronto

Firm Innovation and Financial Analysis

Joel Peress

INSEAD, CEPR

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Nb. of analysts

$m

R&D expenditures Analyst coverage

FROM 1997Starts to develop own

proprietary drugs

An example: Barr Laboratories

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UNTIL 1997Pure generic drugs manufacturer


In a Large Sample

Firm Innovation and Financial Analysis 3

Overview• A model of financial development and technological progress

• Main insight: Feedback loop between financial analysis and firm innovation

1. When financiers are better informed about innovation:

Entrepreneurs expect to receive more funding if innovation is successful

Entrepreneurs innovate more

2. Conversely, when entrepreneurs innovate more:

Fin. anticipate a higher return on capital if they manage to identify successful firms

Financiers collect more information about innovation

– Comes from complementarity between productivity A and capital K in production: Y = AK

• Evidence supporting 1. and 2. from two “experiments” that affect a consistent set of firms

– Economically sizeable effect: indirect effect of R&D policy change, operating through analysts’ response, is about one third of the size of its total effect

4Firm Innovation and Financial Analysis

Technologies• Intermediate goods are produced by a continuum of projects indexed by n ∈ [0, 1]

– Kn : project n’s capital stock

– Ãn : project n-specific random productivity shock

– α ∈ [0, 1] : determines the degree of returns to scale

• Final good producers use intermediate goods as inputs

– Y is the total output of all intermediate goods:


• Risk-neutral agents derive utility from the consumption of a final good

• Entrepreneur creates intermediate technologies

– Conceives a continuum of projects

– Chooses the productivity of tech. in both states ( , ) = “innovation effort”

– But cannot influence probability of success (= 0.5)

– Innovation effort has a cost eA( + ) (increasing and convex in A)

– Chooses same effort for all projects (prevents entrepreneur from focusing on one project only)

Agents: Entrepreneur0.5

0.5


• Financier invests his wealth (endowed)

– Allocates Kn units of capital to project n

– Learns quality of project thanks to imperfect signal S

• With proba q, signal reveals successful project accurately (but 1 − q of being wrong)

• q : the “learning effort” chosen by the financier

• Learning effort has a cost eq(q) (increasing and convex in q)

Agents: Financier


Timing


• Period 1

– First: entrepreneur and financier choose cooperatively

• Research effort: A

• Learning effort: q

– Then: financier observes signal S and invests across projects Kn

• Period 2

– Goods are produced and agents consume

Equilibrium Concept1. Market clearing in the intermediate good market:

1. Capital allocation:

2. First-best determination of effort levels:

– Entrepreneur & financier choose efforts cooperatively (before the signal S is observed):


Learning and Research• In equilibrium, learning and research efforts are the unique solutions to the system:

• The financier allocates:

– units of capital to projects deemed successful by its signal

– and to those deemed unsuccessful

• The research effort is increasing in the learning effort. Conversely, the learning effort is increasing in the research effort


Interaction Between Learning and Research • Knowledge about technologies and technological knowledge feed on each other

– q in A: Each unit of capital is more productive the larger the innovation effort A

– A in q : An invention can be applied on a larger scale the larger K

• Effect comes from complementarity between productivity An and capital Kn in production of intermediate goods:

• Growth rate of income (in OLG model):


Quality of the match between projects and K

• The entrepreneur performs more research when the financier learns more

• The financier learns more when the entrepreneur does more research

• Auxiliary predictions on the mechanism

– An increase in learning effort lead to a more dispersed distribution of capital across projects

– An increase in learning or research effort lead to a more dispersed distribution of return on capital across projects

Summary of Main Predictions

12

where ε*A = 1+elast. of e’(A) w.r.t. A

where ε*q = 1+elast. of e’(q) w.r.t. q


Empirical Strategy

• Measurement: Proxies for learning and research efforts

– Research effort proxied by firm’s level of R&D expenditures (“R&D”)

– Learning effort proxied by number of analysts following the firm (“Coverage”)

• Endogeneity: Simultaneity and possible omitted variables

– Two sets of plausibly exogenous shocks

1. R&D shocks: passage of US States R&D tax credits

2. Learning shocks: loss of analysts due to mergers and closures of brokerage houses


R&D Shock: US States R&D Tax Credits• Allow firms to reduce their state tax liability by deducting a portion of R&D

expenditures from their state tax bill

• Followed the implementation of federal tax credits in 1981

• Minnesota started in 1982, 32 other states followed (as of 2006)

• TC rates range from 3% (NE, SC) to 20% (AZ, HI).


Learning Shock : Brokerage Closures and Mergers• Closures of and mergers between brokerage houses which lead to reduction in analyst

coverage:

– Closures that lead to the removal of analysts who are not re-hired by a new broker

– Mergers that lead to the dismissal of redundant analysts who follow the same stocks as analysts working for the other merging entity

• 52 events from Derrien and Kecskes (2013)


Sample Construction• To properly estimate interaction effect, we use a common sample for both experiments

– Compustat firms

– Manufacturing firms for which R&D is material to the business (R&D>0)

– Followed (and shocked) over 1990 – 2006

• In the learning experiment, treatment affects mainly large firms

– To improve accuracy, restrict to firms with sufficient overlap on covariates

• Estimate propensity score for firms in each experiment using industry, sales, profitability variables

• Keep firms with score between 0.1 and 0.9 for both experiments

• End up with around 1000 firms

– Large: median average revenue of $640m

– Investing in R&D: median R&D/assets = 4.4%


Descriptive Statistics

17

Note: one observation per firm (the average over time)


25th 50th 75th N 50th in Compu.

Coverage 3.80 7.50 14.00 1,011 1.70R&D ($m) 6.59 16.32 48.94 1,011 2.26R&D/assets 1.79 4.41 10.21 1,011 3.12Sales ($m) 193.65 637.33 2,630.64 1,011 76.92ROA 4.74 9.33 13.76 1,011 4.01

Specification• Difference-in-differences ; panel regression

• Regression estimated in first-differences

– Accommodates repeated shocks– Removes firm fixed effects

• Symmetric specifications

– R&D shock: Tax credit, s is state– Learning shock: Broker event, b is broker– For both shocks, X includes ln(sales) and loss dummy– Standard errors clustered at industry level


Shocks Validation


Δln(rd) Δln(rd) Δln(rd) Δln(cov) Δln(cov) Δln(cov)TC+t+1 0.016

[0.017]TC+t 0.036** 0.045** 0.042**

[0.015] [0.017] [0.018]TC+t-1 -0.013

[0.011]AN-t+1 0.015

[0.018]AN-t -0.105*** -0.087*** -0.066***

[0.023] [0.024] [0.019]AN-t-1 -0.025*

[0.014]Year FE Yes Yes Yes Yes Yes YesControls No Yes Yes No Yes YesN 9,953 8,337 7,248 9,953 8,337 7,248

Effect of Learning on Innovation – and of Innovation on Learning


Δln(rd) Δln(rd) Δln(rd) Δln(cov) Δln(cov) Δln(cov)TC+t+1 0.002

[0.018]TC+t 0.038** 0.052*** 0.056***

[0.019] [0.019] [0.019]TC+t-1 0.002

[0.016]AN-t+1 -0.011

[0.019]AN-t -0.039*** -0.025** -0.035**

[0.015] [0.012] [0.013]AN-t-1 -0.004

[0.015]Year FE Yes Yes Yes Yes Yes YesControls No Yes Yes No Yes YesN 9,953 8,337 7,248 9,953 8,337 7,248

Auxiliary Predictions of the Model


S.D. MeanF-stat for Variance

Ratio Test, V(before)/V(after)

p-value

Before (2 yr average) R&D tax credit 0.692 0.049 0.65*** 0.003After (2 yr average) R&D tax credit 0.860 -0.055

Distribution of new equity proceeds

S.D. MeanF-stat for Variance

Ratio Test, V(before)/V(after)

p-value

Before (2 yr average) R&D tax credit 0.136 0.024 0.74*** 0.007After (2 yr average) R&D tax credit 0.158 -0.007

Before (2 yr average) broker event 0.118 0.015 1.34** 0.033After (2 yr average) broker event 0.101 -0.017

Distribution of return on assets (RoA )

Quantification of the “Indirect” Effect of Learning on R&D• Sensitivity of R&D to coverage (analyst shock):

– Δln(coverage) = -0.087 x AN- ; Δln(rd) = -0.025 x AN-

Δln(rd)/Δln(coverage) = -2.5%/-8.7% = 28.7%

Δln(rd) = 28.7% x Δln(coverage)

• Indirect effect of tax credit (R&D shock):

– Δln(coverage) = 0.052 x TC+

Indirect effect of TC, operating through analysts’ response: Δln(rd)* = 28.7% x 5.2% = 1.5%

– Total effect of tax credit : Δln(rd) = 0.045 x TC+

– Compare indirect effect to total effect of TC+:

Indirect effect = 1.5%/4.5% = 33.3% of total effect of tax credit


Calibration


• Income speed of convergence:

• Calibrate the model to assess the importance of the parameter.

• Determine 4 parameters (α, β, εa, εq) to compute γ and its components.

Calibration


• α measures how profits are shared between firms:

– α=1 to generate the most skewed distribution of profits.

• εa and εq are derived from ea and eq as estimated in our reduced-form regressions:

– Assume that the economy is initially in steady state and that it is perturbed by a rescaling shock (changes in R&D tax credits or in broker closures) during period T.

– The perturbed economy then converges toward a new steady state.

– Use the model to compute the change in the learning and research efforts from period T to the next, T + 1.

• β measures share of capital in total income: A range of values in [1/3, 2/3].

• Result: Interaction’s contribution to income growth represents a third of the total contributions of learning and R&D. 1/3

Calibration


Summary• A model of financial development and technological progress

• Knowledge about techs. and technological knowledge feed on each other

– Financiers are better informed about inventions⇒ Inventors expect to receive more funding if successful⇒ They innovate more

– Conversely, inventors innovate more⇒ Financiers anticipate a higher return on their capital⇒ They collect more information about inventions

• Qualitatively: Effect supported by the combination of two “experiments”

• Magnitude: Indirect effect of R&D policy change, operating through analysts’ response, is about 1/3 of the size of its total effect


APPENDIX


Stylized Facts Motivating the Model

• Fin. dev. stimulates investments in R&D, R&D contributes to TFP, and TFP contributes to economic growth (Carlin & Mayer (2003), Griliches (1988))

• Fin. dev. also enhances TFP by improving capital efficiency

– Countries with more developed financial sectors allocate capital more efficiently (Wurgler (2000), Bertrand et al. (2005), Galindo et al. (2005), Chari & Henry (2006))

– A more efficient distribution of capital at the micro level translates into higher TFP (Jeong & Townsend (2006), Restuccia & Rogerson (2003) and Hsieh & Klenow (2006))

• Fin. dev. improves capital efficiency by alleviating informational frictions (Rajan & Zingales

(1998), Wurgler (2000), Carlin & Mayer (2003))

• Countries that are sufficiently developed tend to specialize and the degree of specialization is positively related to fin. dev. (Imbs & Wacziarg (2003), Kalemli-Ozcan, Sorensen &

Yosha (2003))


Related Literature • Finance & growth theory shows how frictions limit the efficient use of resources, e.g.:

– Incomplete information (Greenwood & Jovanovic (1990) )

– Project indivisibilities (Acemoglu & Zilibotti (1997))

– Moral hazard (Bhattacharya & Chiesa (1995), De la Fuente & Marin (1996), Acemoglu et al. (2004))

This paper:

– Focus on selection (ex ante info) rather than monitoring (ex post info) – Anticipation that capital will be efficiently allocated encourages innovation

• Empirical literature on finance and innovation

– Effect of finance on innovation (Derrien and Kecskes (2013), Amore et al. (2013), Hombert and Matray (2014))

This paper:

– Empirically evaluate the interplay between R&D and financial analysis29Firm Innovation and Financial Analysis

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Firm Innovation and Financial Analysis: How Do They Interact?cepr.org/sites/default/files/Peress, Joel pdf.pdf · 2016. 12. 13. · Overview • A model of financial development and