Real and Financial Industry Booms and Busts

Gerard Hoberg University of MarylandGordon Phillips University of Maryland & NBER

Paper presentation to Insead, May 2007

Insead, May 2007

Motivation

• Industry booms and busts are recurring phenomenon according to WSJ, NY Times

• Railroads 1860s, 1870s• Petroleum exploration, paper mills (70s) • Agriculture commodities (80s, 90s): “Hog

Cycles”• Winchester Disk Drives, 1982-83• Non-store retailing (catalogs), 1991-1992• Internet, Telecommunication, 1998-00

Insead, May 2007

August 9, 2006 NEW YORK (CNNMoney.com) -- Homebuilder Toll

Brothers said the current slump in residential construction is unlike any it has seen in 40 years as it became the latest to warn of a glut in new homes for sale and a slowdown in the closely watched real estate market."… it seems to be the result of an oversupply of inventory and a decline in confidence," he added. "Speculative buyers who spurred demand in 2004 and 2005 are now sellers; builders that built speculative homes must now move their specs; and nervous buyers are canceling contracts for homes already under construction."

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Our Questions• How important are industry booms?• Outcomes: How do firms’ cash flows and

the stock market respond to industry booms?

• Test how competition impacts subsequent outcomes following booms and busts.

• “Excess/high” competition in concentrated industries?Schumpeter (1941), Perry (1984) Mankiw and Whinston (1986)

• Excess competition in competitive industries?Scharfstein (1988), Reinganum (1989), Hou and Robinson (JF, 2005)

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Related Literature

Large literature on reversals, momentum and misvaluation, rational herding and informational cascades.

• Both rational and behavioral explanations.Some examples: De Bondt and Thaler (1985),

Scharfstein and Stein (1990), Welch (1992), Jegadeesh and Titman (1993), Shleifer and Vishny (2003) Rhodes-Kropf and Viswanathan (2004) (rational misvaluation given asymmetric information and mergers)

No industrial organization, microeconomics unspecified.

Insead, May 2007

Related Literature 2Rational Booms and Busts:

Pastor and Veronesi (2005): Switch of uncertainty from idiosyncratic to systematic causes what appear to be bubbles.

DeMarzo, Kaniel, Kremer (2005), “Overinvestment in new technology”, “may invest to the point that its expected return is negative” given consumption insurance. Also Gala (2005).

Irrational Booms and Busts: Neg. Stock returns following high equity issuance:

Baker and Wurgler (2000). Neg. Stock returns following high investment:

Titman, Wei, and Xie (2004) and Polk and Sapienza (2004) for cross-sectional results and Lamont (2000).

Insead, May 2007

Related Literature 3Real Options

Exercise of real options causes changes in risk and expected returns – Carlson, Fisher, Giammarino (2005)

Beta declines faster with demand increases in competitive industries as firms in these industries exercise growth options faster as in Aguerrevere (2006).

When demand decreases during bust, Beta increases given increased operating leverage and failure (rationally) to internalize positive effects of exiting.

Effect of competition on stock markets: How product market competition affects incentives to

gather information, volatility and returns (Gaspar and Massa (2005), Hou and Robinson (2005) and Peress (2006)).

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Conclusions Booms and busts are not just in high tech

industries Market competition & financing are crucial in

understanding outcomes of booms. Transitions out of booms are more likely if

relative investment and new financing are high. In competitive industries:

• Subsequent operating performance and abnormal stock returns are negatively related to valuation metrics & industry new finance. Particularly true in high systematic risk industries.

• Risk changes only partially mitigate (but do not eliminate) these effects. Effects persist in high valuation industries.

• Results consistent with high competition in competitive industries not being internalized by the stock market.

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Contrast in Concentrated Industries

In concentrated industries:

Little evidence of ex post predictability in operating cash flows or stock returns.

Evidence consistent with firms in concentrated industries internalizing the effects of their production on industry

outcomes.

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Importance of competition Why does industry competition have

such a large effect? Cash flows: Economics 101 – produce more

until prices re-equilibrate. Mean reversion quick given elastic supply response.

Stock market: Why do prices especially in the stock market go so high initially? Overoptimism/Consumption hedge /Failure or lack of incentives to anticipate elastic supply response.

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Outline1. Hypotheses2. How we construct our measure of

relative industry valuation or “Booms” and “Busts”

3. Characteristics of Booms and Busts4. Relation between ex post outcomes and

Booms and Busts. We examine ex post: Operating Cash Flows Adjusted or Excess Stock Returns

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Main Hypotheses H1: In concentrated industries with high

valuations, high investment and high financing decrease industry cash flows and stock returns.

H2: In competitive industries with high valuations, high investment and high financing decrease industry cash flows and stock returns.

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Risk-Based Hypotheses“Can we make predictability go away?”

H2b: Changes in stock returns can be explained given changes in sensitivity to priced risk factors / we also add a competition risk factor – Hou and Robinson, Peress.

H2c: During (following) industry booms, systematic risk decreases (increases) more in competitive than in concentrated industries. (Real Options Models)

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Rational Theories of Booms and Busts“Can Rational Models explain our findings?

H3a: Booms have high idiosyncratic risk given technological uncertainty. Subsequent busts have increased systematic risk and decreased idiosyncratic risk (Pastor and Veronesi).

H3b: Consumption Hedging: In industries with high systematic risk, subsequent stock returns will be negatively related to industry investment, valuation and financing (DeMarzo, Kaniel, Kremer).

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Toll Brothers Stock

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Booms and Outcomes

Size of thePre-Boom Boomt Ex post

Estimation period time t char. using Outcomest+1

t-10 to t-1 t-10 to t-1 coefficients =f(Boomt, NFt)_

(1) Valuation Model ex post(Kothari, 2001, RRV, 2005) cash flow, returns

(2) M/B model (3) Simple P/E Model

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Valuation Model

Market ValuationEstimation period t-10 to t-1

Estimated (1) and (2) by industry in logs, save coefficients each industry.

Predict valuation in period t using period t RHS variables.Average and median R2 approx = .75

ijt j,0 j,1 ijt 2,j 3,j ijt 4,j ijt(1) MVequity *BVequity *(abs(NI)) *dummy NegNI *Lev

ijt ijt j,0 1,j 2,j 3,j ijt(2) MVequity /BVequity *(AT) *abs(NI) *dummy NegNI

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Investment Model

Predict InvestmentEstimation period

t-10 to t-1

Estimated by industry, save coefficients each industry.Predict investment in period t using period t RHS

variables.

tj,i,j4,

tj,i,j3,1-tj,i,j2,1-tj,i,j,1j,0tj,i,

NegOIBDPdummy *

))(abs(OIBDP* )(Assets* * )(

lnlnqCapxln

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Relative Valuation /Investment Relative MVi,j,t (RMV)=

ln(MVi,j,t)- Predicted Valuation

(time t char. using coefficients from t-10 to t-1 data)

Gives firm-level relative valuation

RMVi,j,t = ln(MVi,j,t) - PMVi,j,t

Average over firms to get relative industry-level Booms.

Similar procedure for relative industry investment.

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Booms and Outcomes by Industries

Tests:• Are booms and outcomes different in

concentrated and competitive industries?• Herfindahl measured (public firms), predicted

using public and private firms.• We examine changes in operating cash flows

and risk and style-adjusted ex post stock returns.

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Competitive Industries: Booms

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Competitive Industries: Booms - 2

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Outcomes

Ex post Outcomest+1

=f(Boomj,t, NFj,t, RIj,t, Boomi,j,t, NFi,j,t, RIi,j,t,)

ex post

cash flow, returns

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T4: Regressions Predicting Firm-level cash flows: CFi,t+1-CFi,t (industry-year adj.)

(scaled by each period assets)

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T4: Regressions Predicting Firm-level cash flows: CFi,t+1-CFi,t (industry-year adj.)

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T5: Firm-level cash flow regressions (high market risk tercile)

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Characteristic AdjustedMonthly Abnormal Returns

ARi,t = RAWi,t – STYLEi,t for firm i in month t

- Per Daniel/Grinblatt/Titman/Wermers (1997) and Wermers (2004)

- Construct 125 style portfolios, rebalanced July 1 (size NYSE).

- Conditional sorts on Size, Industry Adj. B/M, past 12 month return

Lags: Davis/Frama/French (2000):

- For monthly observations b/t July year t to June year t+1

- Size = CRSP market cap from June of year t

- Accounting data from fiscal years ending in year t-1

- Past 12 month return from June of year t-1 to May of year t

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Abnormal Returns FF 4 factor model+ Mitchell/Stafford Alphas

Step 1: Group firm/years as July year t to June year t+1

Step 2: Get intercepts FF ’93 + MOM (12 monthly obs./regression)

Step 3: Control for non-linearity

- Style matching based on same 125 style portfolios, same lag structure.

UMDSMBLMKTtRawAlpha iiiiiti ,

tititi STYAlphaRAWAlphaMSAlpha ,,,

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T6: Regressions Predicting Firm-level Abnormal Stock Returns: i,t+1

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T7: Return RegressionsHigh Relative Valuation Tercile

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T8: Return RegressionsHigh Market Risk Tercile

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Risk Tests Examine if changes in risk occur

following industry booms. Industry technological adoption: Pastor and

Veronesi (2005) We examine market beta and idiosyncratic risk.

Real Options: Grenadier (2002), Aguerrrevere (2006)

We examine change in total risk.

Do any changes explain abnormal returns?

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Univariate risk changes

Percent of Risk that is Systematic (Standard Deviation)

0.245

0.25

0.255

0.26

0.265

0.27

0.275

0.28

0.285

0.29

0.295

0.3

-5 -4 -3 -2 -1 0 1 2 3 4 5

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Tables 9+10: Regressions Predicting Chg. In Market Risk: Dependent Variable “”

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Tables 9+10: Regressions Predicting Chg. Idio. Risk: Dependent Variable “idio.”

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ΔRisk – Adjusted Abnormal ReturnsGoal: Can Δrisk can explain return patterns?

- Per Daniel/Grinblatt/Titman/Wermers (1997) and Wermers (2004)

ARi,t = RAWi,t – STYLEi,t for firm i in month t

Take residuals from the following in-sample regressions:

tititi LMKTAR ,,, ][][

tititi UMDSMB ,,, ][][

Define above residual as ARΔRi,t and run our previous regression:

ARΔRi,t = + 1 [Valuation] + 2 [Investment] + 3 [Finance] +

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Table 11: Regressions Predicting ΔRisk Adjusted Abnormal Stock Returns? Do

results go away?

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Table 12: Firm-level Quintile Returns

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Annual Profitability: Excluding Internet Yrs Relative Industry-level Valuation Quintile

Returns

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Conclusions

Booms and busts are not just in high tech industries

Market competition & financing are crucial in understanding outcomes of booms.

In competitive industries:• Subsequent operating performance and abnormal stock

returns are negatively related to high relative industry valuation & industry new finance.

• Risk changes only partially mitigate these effects. Effects still present in highest valuation industries.

• Results consistent with stock and product market failure to internalize effects of high competition and investment / consumption hedge as in DeMarzo et. al.

Insead, May 2007

In concentrated industries: Little evidence of ex post predictability in operating

cash flows or stock returns. Evidence consistent with firms in concentrated

industries internalizing the effects of their production on industry outcomes.

Conclusions