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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

Firm Innovation and Financial Analysis

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:

5Firm Innovation and Financial Analysis

• 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

Firm Innovation and Financial Analysis

• 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

Firm Innovation and Financial Analysis

Timing

8Firm Innovation and Financial Analysis

• 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):

9Firm Innovation and Financial Analysis

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

10Firm Innovation and Financial Analysis

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):

11Firm Innovation and Financial Analysis

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

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

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

Firm Innovation and Financial Analysis

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

13Firm Innovation and Financial Analysis

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).

14Firm Innovation and Financial Analysis

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)

15Firm Innovation and Financial Analysis

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%

16Firm Innovation and Financial Analysis

Descriptive Statistics

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

Firm Innovation and Financial Analysis

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

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Shocks Validation

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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

20Firm Innovation and Financial Analysis

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

21Firm Innovation and Financial Analysis

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

22Firm Innovation and Financial Analysis

Calibration

Firm Innovation and Financial Analysis 23

• 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

Firm Innovation and Financial Analysis 24

• 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

Firm Innovation and Financial Analysis 25

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

26Firm Innovation and Financial Analysis

APPENDIX

27Firm Innovation and Financial Analysis

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))

28Firm Innovation and Financial Analysis

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