testing asymmetric-information asset pricing models · 2016-02-22 · • process still ongoing: on...

Testing asymmetric-information asset pricing models

Bryan Kelly, NYU SternAlexander Ljungqvist, NYU Stern & CEPR

One-slide summary• Aim:

– To empirically examine fundamental driving forces of asset pricing models under asymmetric information (à la Grossman and Stiglitz 80, Hellwig 80, Admati 85, Wang 93, Easley and O’Hara 04 …)

• Predictions:– Info asymmetry ↑

⇒ price ↓, uninformed demand ↓– Channel is liquidity risk: price falls because the asset’s exposure to

liquidity risk increases– Price and demand changes related to fraction of better-informed

investors, supply and liquidity of the asset, payoff uncertainty, and noisiness of the signal

• Identification: – Tricky! Say, regress Δdemand on change in proxy, e.g. Δbid-ask spread.

Omitted variables? Reverse causality?– Our instrument: plausibly exogenous variation in supply of information

caused by restructuring of brokerage research• Results:

– Price and uninformed demand fall; cross-sectional tests support the comp stats (at least for price); loading on a liquidity-risk factor ↑

• Take-aways:– Information asymmetry is priced: it affects asset prices and investor

demands– Liquidity varies as a result of differences in information asymmetry

Literature

Asym. info ⇒ asset pricesAsym. info ⇒ liquidity

Grossman & Stiglitz 80, Hellwig 80, Admati 85, Wang 93, Easley & O’Hara 04 …

Kyle 85, Glosten & Milgrom 85 …

PIN (Easley & O’Hara 92):Easley et al. 96Easley, Hvidkaer & O’Hara 02

Liquidity ⇒ asset prices

Amihud & Mendelson 86, Huang 03, Acharya & Pedersen 05 …

Constantinides 86 …

Brennan & Subrahmanyam 96, Eisfeldt 02, Garleanu & Pedersen 04, Duffie, Garleanu & Pedersen 05 …

Amihud & Mendelson 86, Amihud 02, Hasbrouck & Seppi 01, Jones 01, Bekaert, Harvey & Lundblad 07, Pastor & Stambaugh 03, Eleswarapu 97 …

Intro G-S model Identification Results Conclusions• • • • • • • • • • • •Th

eory

Empi

rical

Ideal experiment

1. Imagine a world where each stock is covered by exactly one analyst:

Intro G-S model Identification Results Conclusions• • • • • • • • • • • •

ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo

2. Randomly ban analysts from researching some stocks. This increases information asymmetry for affected stocks.

3. Measure effect on equilibrium prices and investor demands.

xx x x

x xx x

x

Our approach

We construct a quasi-experiment along these lines:• Compile list of events in which brokerage firms terminate

coverage of large sets of stocks• Coverage terminations are valid instrument if …

1. they correlate with an increase in information asymmetry2. they affect price and demand only through their effect on

information asymmetry• Most coverage terminations don’t qualify

– mostly endogenous, signaling a negative change in analyst’s view of company’s prospects (McNichols and O’Brien 97)

• We identify a subset of 14,939 terminations in 2000-2005 that are plausibly exogenous


Overview

1. Model2. Identification

• Do terminations ⇒↑

information asymmetry?• Are sample terminations exogenous?

3. Testing the model• Changes in price• Changes in informed and uninformed demands• Changes in expected returns

4. Conclusions


The model (1)

• There is a unit mass of investors who trade in period 1 and consume in period 2. Investors have identical initial wealth W0 and CARA utility of consumption of –e–C.

• There are two assets:– a risk-free asset with gross return R– a risky asset with aggregate supply

and payoff

• Investors know θ and can produce a noisy signal s at cost c:

• Assume (X, η, v) are independent.• Investors do not observe X.

),(~ 2xXNX σ

known. and )(0,~ with , 2u θσηηθ Nu +=

),0(~ with , 2vNvvs ση +=


The model (2)

• There may or may not be an analyst who can produce and disseminate the signal, s.

• Assume the analyst disseminates s (in the form of earnings forecasts, research reports, or trading recommendations) for free.

– Brokerage research is “free at the point of use”; it’s funded through indirect revenue streams (soft dollars, investment banking, market-making).

– (See Admati and Pfleiderer 86, Fishman and Hagerty 95, or Veldkamp 06 for why an information producer would disclose his signal rather than trade on it.)

• Equilibrium:– In analyst’s presence, s is public information ⇒ information is symmetric.– In analyst’s absence, some fraction 0 < δ < 1 of investors become informed

⇒ information is asymmetric.– Uninformed investors infer s from P, but only noisily.

• Strategy:– Derive equilibrium prices and demands of informed and uninformed

investors under symmetric and asymmetric information. – Model effects of increase in information asymmetry as changes in

equilibrium prices and demands in response to a coverage termination.


Proposition 1


Corollary 1

Why does price fall? Note that price is linear in s and X:

Interpreting X as trade volume and the coeff. on X as the price impact of trade, we have:


Corollary 2

So, price falls as information asymmetry increases because price becomes more sensitive to liquidity risk. Thus, we have:

In a factor setting, for instance, we would expect the loading on a factor proxying for liquidity risk to increase.


Comparative statics: Price

• Fraction of informed investors

• Mean aggregate supply

• Variance of aggregate supply

• Variance of risky asset payoff


Comparative statics: Price

• Signal noise: U-shaped


Comp. statics: Informed demand


• Fraction of informed investors

• Mean aggregate supply

• Variance of aggregate supply

• Variance of risky asset payoff

Comp. statics: Informed demand

• Signal noise: Inverse U-shaped


Identification

Identification

• Empirical identification of the effects of an increase in information asymmetry on asset prices and investor demands requires an instrument

• The instrument must – correlate with an increase in information

asymmetry – but not otherwise correlate with price or

demands


Identification problem

Variation in coverage generally not exogenous – coverage decisions reflect analyst’s opinion of future

performance (McNichols and O’Brien 1997)– terminations often = implicit sell recommendations

(Scherbina 2008)– drop coverage because institutions have lost

interest (Xu 2006)

↓

Information(endogenous)

Signal of poor prospects

↓

Asset value,change in

fundamentals/beliefs


Identification strategy (1)

Equity research business model under immense pressure:• Sell-side research relies on cross-subsidy from IB or MM/trading (“soft $”)• Increased competition for order flow, pressure on commissions

– E.g., on Nasdaq, ECNs slashed fees as competition for declining volume increased in 2000-2003

• New rules separating IB and research– Jul 2002: NASD Rule 2711, amendment to NYSE Rule 472– April 2003: Spitzer’s “Global Settlement”

• New rules on soft dollar commissions– Jul 2006: SEC tightens safe-harbor rule under Section 28(e) of 1934

Act• Pressure to “unbundle” research and execution

– E.g., nearly 90% of larger buyside firms stopped or decreased use of soft dollars between 2004 and 2005 (BusinessWire, 6/1/05)

– 2004/5: MFS Investment Management, Janus, and Fidelity agree unbundling deals. E.g. Fidelity paid Lehman $7m p.a. for research and was charged 2-2.5¢ per share for execution (average blended commission is about 3.6¢)




Larry Tabb, CEO, TABB Group:

“The equity research model is not only broken, it is destroyed. In trying to clean up the research process, we have thrown out the baby with the bath water. Broker research always has been suspect - the way incentives and soft dollars are moving - but it's becoming more difficult to fund any equity research, broker-sponsored or independent.” (Wall Street & Technology, November 18, 2005)


Restructuring of equity research industry:• At least 20 brokerage firms exited research in 2000-2005

– Both large firms (e.g., Wells Fargo, 8/2005) and smaller outfits (e.g., Schwab's Soundview Capital Markets division, 10/2004)

– Both retail-oriented firms (e.g., First Montauk, 2/2004) and institutional brokerage houses (e.g., Emerald Research, 7/2001)

– Both brokers with generalist coverage (e.g., ABN Amro, 3/2002) and those with specialist coverage (e.g., Conning & Co., 10/2001)

• Process still ongoing: On 6/6/07, Prudential announced exit from research

• More than 150 firms downsized, including– Goldman Sachs, Morgan Stanley, and JPMorgan cut research staffing by 15-

25% in 2003Often fired sector teams (as opposed to individual analysts):

– May 23, 2003: Citigroup dropped coverage of 8 of 43 sectors (metals & mining, utilities, healthcare, life sciences, specialty chemicals, industrials, telecom equipment/wireless, airlines)

– Focus on sector terminations, not individual analyst departures


Are terminations public events?

NASD Rule 2711(f)(5):“If a member intends to terminate its research coverage of a subject company, notice of this termination must be made. The member must make available a final research report on the subject company using the means of dissemination equivalent to those it ordinarily uses to provide the customer with its research reports on the subject company. The report must be comparable in scope and detail to prior research reports and must include a final recommendation or rating, unless it is impracticable for the member to produce a comparable report (e.g., if the research analyst covering the subject company or sector has left the member or if the member terminates coverage of the industry or sector). If it is impracticable to produce a final recommendation or rating, the final research report must disclose the member's rationale for the decision to terminate coverage.”

Quickly disseminated to the wider market (or at least to DJ/marketwatch.com).


Sample

• Identify 14,939 stocks experiencing coverage terminations for exogenous reasons:– closure of research department (1,793)– sector (rather than stock) terminations (13,146)

• (Also have sample of 53,092 “endogenous” terminations.)

• The 14,939 terminations exclude:– stocks that might have been dropped anyway (“implicit sells”), i.e.

stocks delisted within 3 months– stocks reinitiated by same analyst elsewhere or other analyst at

same broker, within 3 months– REITs, ADRs, non-common stocks, closed-end funds, no CRSP data

• A little larger, more liquid, and less volatile than mean/median CRSP company, but very similar to universe of stocks with analyst coverage in Reuters and I/B/E/S


Do terminations →

asym. info↑?


Are terminations exogenous?

• Test validity of exogeneity assumption– Why did brokerage firms close?

– Do sector terminations reflect past or future performance or broker-specific reasons?

– Are terminations informative of future performance changes?


Exogeneity: Closures

Closure-related terminations really should be exogenous– Hard to argue that brokerage firms quit research to mask negative information about

companies their analysts covered

Press releases hint at exogenous reasons:

“With the brokerage industry facing some of the most far-reaching regulatory changes within the last 30 years, including SEC rulings regarding the use of soft dollars and possibly the unbundling of research and trading costs, we could not see the economics working in our favor without substantial additional investment.” (Dow Jones, 6/28/05)

George K. Baum closed because “Neither the retail brokerage nor equity capital markets divisions made money in the past two years.” (Knight Ridder Tribune, 10/13/00)

ABN Amro closed because “We're withdrawing from businesses in which we're strategically ill-positioned and cannot create a sustainable profit stream, whether the market turns around or not.” (Chicago Tribune, 3/26/02)

All our results are robust to just using closures


Exogeneity: Sector terminations

• Test if brokers terminate sectors whose characteristics correlate with variables of interest (i.e., with price or demand)

• When restructuring, which sectors does a broker drop?– McFadden choice model: at time t, broker chooses which nt of the

Nt sectors it covers to drop (conditional on restructuring)– Define a sector using GICS (Bhojraj, Lee, and Oler (2003))– Dep. var. = 1 if sector terminated, 0 otherwise

• Relate sector choice to profit drivers– Sector performance (prior and future stock performance, prior and

future earnings surprises)– Sector characteristics (proxies for brokerage commissions and IB

revenue, return volatility, # other brokers with coverage)– Analyst characteristics (all-stars, forecast accuracy, seniority,

tenure with broker)


Exogeneity: Sector terminations

Results (Table III)• No relation to prior or future sector performance →

supports exogeneity• Controls behave as expected: Less likely to drop a sector that…

…generates high brokerage commissions or IB revenue…is covered by all-stars or analysts with good forecasting ability


Do terminations forecast performance changes?

• Suppose at time t, a broker drops coverage on a stock. Does this signal private information about future prospects?

• If termination is informative (“endogenous”) …– specifically, say drop is due to negative information about t+1

earnings that is not yet reflected in the consensus earnings forecast dated t–1 (i.e., before analysts knew of the coverage drop)

– … then when earnings are eventually announced, they fall short of consensus on average, resulting in a negative earnings surprise

• If termination is uninformative (“exogenous”) …– … then earnings will not differ systematically from consensus

• Results using a dynamic-panel estimator:


Results

Change in price

< 0


Notes:• CARs are shown in percent.• To correct for after-hours announcements, we use time stamps to determine 1st trading day after the announcement where

available. • For the market model, we report both the parametric Boehmer, Musumeci, and Poulsen (1991) standardized cross-sectional test

and Cowan’s (1992) non-parametric generalized sign test statistic (separated by “/”). • For the Fama-French model, we report Brown and Warner’s (1980) “crude dependence adjustment” t-test and the generalized sign

test (separated by “/”).

Change in price: Robustness


Change in price: By coverage


Quarterly change in institutional investors’ holdings, based on mean fraction of total stock outstanding held in aggregate by institutional investors filing 13f reports in the quarter before and the quarter after a termination. Data from CDA/Spectrum.

Change in informed demand


> 0

Tests of comparative statics

Empirical proxies• Fraction of informed investors

– fraction of TSO held by institutional investors (from 13f filings), quarter before termination

– size of broker’s institutional and retail sales forces• Mean aggregate supply

– mean daily log turnover, 6 months ending 1 month before termination• Aggregate supply uncertainty

– s.d. daily log turnover, 6 months ending 1 month before termination• Payoff uncertainty

– s.d. quarterly earnings growth (over same quarter the previous year, to account for seasonality). We require at least 2 quarters of data to calculate, and cap it at 20 quarters.

• Signal noise– number of remaining analysts– analyst quality measures– dispersion of analyst forecasts (mean of s.d. of analysts’ quarterly EPS

forecasts over year before drop, scaled by BV(assets); level and square)


Comp. statics: Price

Notes:

OLS with:• firm fixed effects• year effects

CARs are in %

Broker/dealer datafrom SIA Factbook.


Comp. statics: Demand


Notes:

OLS with:• firm fixed effects• year effects

Broker/dealer datafrom SIA Factbook.

Testing Corollary 2

We estimate

and report difference-in-difference coefficients . See Pastor andStambaugh (2003, p. 665, equations (11)-(13)).


Using mean factor realizationsfor our sample period, expected returnsincrease by 14 and 44 basis points p.a.

Summary

• Asymmetric-information asset pricing models suggest ↑

in info asymmetry ⇒ ↓

in price, ↑

in informed demand• Coverage terminations = ↑

in info asymmetry– appears true in the data– test requires exogenous terminations; we argue they are

• Findings:– price falls, informed demand increases– cross-sectionally, price fall is larger…

• … the fewer shares held by institutional (informed?) investors• … the greater is mean aggregate supply• … the greater is aggregate supply risk• … the more uncertain is the asset payoff• … the noisier is the signal

– expected returns become more sensitive to Pastor and Stambaugh’s LIQ factor (as well as to HML and SMB)


testing asymmetric-information asset pricing models · 2016-02-22 · • process still ongoing: on...

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