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Journal of Finance
Does Stock Liquidity Enhance or Impede Firm Innovation?
Vivian Fang, Carlson School of Management, University of Minnesota
Xuan Tian, Kelly School of Business, Indiana University
Sheri Tice, A.B. Freeman School of Business, Tulane University
2
Motivation

There is a longstanding academic and policy debate about the
effect of liquidity on managerial myopia.

Managerial myopia is defined as the underinvestment in long-term, intangible
projects for the purpose of meeting short-term goals.
Motivation (Cont’d)
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First view

High stock liquidity exacerbates managerial myopia.

Stein (1988, 1989) and Shleifer and Summers (1988)
In
the presence of information asymmetry between managers and investors,
takeover pressure could induce managerial myopia.
Takeover
Liquidity

pressure also gives managers less power over shareholders.
increases the probability of a hostile takeover (Kyle and Vila 1991).
Porter (1992) and Bhide (1993)
Long-term,
intangible investments tend to depress short-term earnings.
“Non-dedicated
High
owners” may exit in response to depressed earnings.
liquidity encourages “Wall Street Walk”, resulting in a drop in price.
Motivation (Cont’d)
4
Second view

High stock liquidity mitigates managerial myopia.

Maug (1998)


Predicts more monitoring activities by blockholders in highly liquid
firms.
Admati and Pfleiderer (2009), Edmans (2009) and Edmans and Manso
(2011)

A blockholder actively collects information about fundamental value
and impounds it into stock price through trading in a liquid market.

Efficient stock price reflects fundamental information rather than shortterm earnings.
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Research Question

“Managerial myopia is difficult to test because it results in
underinvestment in activities that are difficult to observe.”
Stein (2003)


Lack of innovation is a critical symptom of managerial myopia.
We use observable innovation outputs (patents and citations) to
capture managerial myopia (or lack thereof).
Does stock liquidity enhance or impede firm innovation?
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Main Findings


We show a negative causal effect of stock liquidity on
innovation productivity, using a diff-in-diff approach.

Surrounding decimalization of the minimum tick size in 2000-2001

Surrounding the shift in the minimum tick size in 1997

More pronounced for pilot firms that converted to decimals in 2000
Possible mechanisms: firms with a larger, exogenous
increase in liquidity surrounding decimalization have

A high probability of facing a hostile takeover

An increase in the holdings of non-dedicated institutional investors
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Contribution

We shed light on the longstanding academic and policy debate.

This is the first paper in the literature that provides causal
evidence that stock liquidity impedes firm innovation.

We uncover a previously under-identified adverse consequence
of regulatory effort to enhance stock liquidity.
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Contribution (Cont’d)

Add to the literature on managerial myopia and innovation


Aghion, Van Reenen, and Zingales (2013); Lerner, Sorensen, and Stromberg
(2011); Ferreira, Manso, and Silva (2012); Atanassov (2012); Chemmanur and
Tian (2012)
Add to the literature linking liquidity to firm outcomes

Fang, Noe, and Tice (2009); Bharath, Jayaraman, and Nagar (2012); Norli,
Ostergaard, and Schindele (2010); Edmans, Fang, and Zur (2013)
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Sample Selection

Data sources





NBER patent database: Patent and citation data
TAQ database: Intraday trades and quotes
Thomson’s 13f database and Brian Bushee’s website: Institutional
ownership and classification
Compustat: Financial data
We end up with

39,469 firm-year observations between 1994 and 2005
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Key Variables


Measuring innovation productivity (Hall, Jaffe, and Trajtenberg 2001, 2005)

Number of filed patents (and eventually granted), adjusted for
application-grant lag: INNOV_PATt+n

Number of non-self citations each patent receives, adjusted for
citation-lag: INNOV_CITEt+n
Measuring stock liquidity (Fang, Noe and Tice 2009)

A stock illiquidity measure: ILLIQt , log of the annual RESPRD
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Control Variables










LN_MV, the natural logarithm of firm market capitalization
ROA, return-on-assets ratio
RDTA, R&D expenditure over total assets
PPETA, net PPE scaled by total assets
LEV, total debt to total assets ratio
CAPEXTA, capital expenditure scaled by total assets
HINDEX (HINDEX2), Herfindahl index based on annual sales
Q, Tobin’s Q
KZINDEX, Kaplan and Zingales (1997) five-variable KZ index
LN_AGE, the natural logarithm of one plus the number of years the
firm is listed on Compustat.
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Summary Statistics
Variable
5%
25%
Median
Mean
75%
95%
SD
N
INNOV_PAT
0.000
0.000
0.000
0.792
0.000
5.549
1.950
39,469
INNOV_CITE
0.000
0.000
0.000
0.620
0.000
3.386
1.192
39,469
ILLIQ
-6.573
-5.363
-4.377
-4.482
-3.557
-2.644
1.208
39,469
LN_MV
2.457
4.103
5.468
5.604
6.963
9.254
2.036
39,469
RDTA
0.000
0.000
0.000
0.055
0.061
0.250
0.129
39,469
ROA
-0.295
0.044
0.114
0.078
0.172
0.284
0.185
39,469
PPETA
0.016
0.089
0.209
0.285
0.430
0.789
0.242
39,469
LEV
0.000
0.018
0.171
0.209
0.339
0.591
0.202
39,469
CAPEXTA
0.003
0.021
0.043
0.062
0.079
0.194
0.063
39,469
HINDEX
0.000
0.016
0.067
0.142
0.192
0.548
0.192
39,469
Q
0.784
1.075
1.458
2.112
2.331
5.822
1.862
39,469
KZINDEX
-42.38
-5.353
-0.682
-9.000
0.894
2.627
31.12
39,469
LN_AGE
0.693
1.609
2.303
2.292
3.091
3.807
0.979
39,469
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OLD Regressions
The baseline (with # of patents as D.V.) has the following specification:
INNOV_PATi,t+n = a + bILLIQi,t + cLN_MVi,t + dRDTAi,t
+ eROAi,t + fPPETAi,t + gLEVi,t + hCAPEXTAi,t
+ iHINDEXi,t +jHINDEX2i,t + kQi,t + lKZINDEXi,t
+ mLN_AGEi,t +YRt +FIRMi + errori,t
(1)
Innovation measured by INNOV_PAT
Dependent Variables
ILLIQt
Year and Firm FE
Number of Obs. Used
Adjusted R2
INNOV_PATt+1
INNOV_PATt+2
INNOV_PATt+3
0.141***
(0.020)
Included
39,469
0.839
0.168***
(0.023)
Included
33,098
0.844
0.170***
(0.026)
Included
27,363
0.849
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OLS Regressions (Cont’d)
The baseline (with # of citations as D.V.) has the following specification:
INNOV_CITEi,t+n = a + bILLIQi,t + cLN_MVi,t + dRDTAi,t
+ eROAi,t + fPPETAi,t + gLEVi,t + hCAPEXTAi,t
+ iHINDEXi,t +jHINDEX2i,t + kQi,t + lKZINDEXi,t
+ mLN_AGEi,t +YRt +FIRMi + errori,t
(2)
Innovation measured by INNOV_CITE
Dependent Variables
ILLIQt
Year and Firm FE
Number of Obs. Used
Adjusted R2
INNOV_CITEt+1 INNOV_CITEt+2 INNOV_CITEt+3
0.104***
(0.015)
Included
39,469
0.652
0.106***
(0.016)
Included
33,098
0.653
0.106***
(0.019)
Included
27,363
0.653
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Identification
Diff-in-Diff analysis using decimalization




We make use of an exogenous shock to stock liquidity: decimalization
Based on ΔRESPRDt-1 to t+1, we sort 3,375 sample firms into terciles.
We match firm from the top and from the bottom tercile, using a one-toone nearest neighbor propensity score matching, without replacement.
PAT (CITE): sum of # of patents (citations per patent) in the 3-year
window before or after decimalization.
Mean treatment
difference
(after - before)
PAT
CITE
Mean DiD
estimator
(treat - control)
T-statistics for
DiD estimator
-5.169
Mean control
difference
(after before)
-1.682
-3.487**
-2.265
(1.103)
(1.074)
(1.540)
-11.14
-8.522
-2.616**
(0.986)
(0.884)
(1.324)
-1.976
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Identification (Cont’d)
Diff-in-Diff analysis using decimalization

The figure on the left (right) shows the average innovation captured by
the mean number of patents (citations per patent) for treatment and
control firms, from 3 years before decimalization to 3 years after
decimalization. Decimalization year is denoted as year 0.
4
4
2
CITE
PAT
3
2
1
Treatment
Control
Treatment
Control
0
-3
-2
-1
0
Year
1
2
3
0
-3
-2
-1
0
Year
1
2
3
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Identification (Cont’d)
Diff-in-Diff analysis using the 1997 shock



We make use of another exogenous shock to stock liquidity: the shift in
minimum tick size from 1/8th to 1/16th in 1997
Similarly, based on ΔRESPRDt-1 to t+1, we sort sample firms into terciles
and apply a one-to-one nearest neighbor propensity score matching.
PAT (CITE): sum of # of patents (citations per patent) in the 3-year
window before or after decimalization.
Mean treatment
difference
(after - before)
PAT
CITE
Mean DiD
estimator
(treat - control)
T-statistics for
DiD estimator
-1.973
Mean control
difference
(after before)
2.621
-4.595**
-1.976
(0.797)
(2.185)
(2.326)
-9.065
-4.360
-4.706**
(1.806)
(1.189)
(2.162)
-2.177
Identification (Cont’d)
Diff-in-Diff analysis comparing pilot and non-pilot
stocks

The conversion on the NYSE was completed in 4 phases.

Phase 1: 7 issues in July 2000

Phase 2: 52 firms (representing 57 issues), starting
September 25, 2000

Phase 3: Additional 94 securities on December 4, 2000

Phase 4: the rest of the non-pilot securities listed on the
NYSE in January 2001
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Identification (Cont’d)
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Diff-in-Diff analysis comparing pilot and non-pilot
stocks



PILOT: a dummy to indicate pilot firms (phase 1-3)
YR_2000: equals 1 for year 2000 and 0 for year 1999
PILOT×YR_2000 is an interaction term of the two.
INNOV_PATi,t+1 (INNOV_CITEi,t+1) = a + bPILOTi × YR_2000 + c PILOTi + d
YR_2000 + e’CONTROLSi,t + INDj + errori,t
(3)
Dependent Variables
PILOTi×YR_2000
PILOTi
YR_2000
Controls and Industry FE
Number of Obs. Used
Adjusted R2
(1)
INNOV_PATt+1
-0.485**
(0.213)
0.289
(0.243)
-0.014
(0.165)
Included
2,160
0.550
(2)
INNOV_CITEt+1
-0.309*
(0.164)
0.313*
(0.166)
0.091
(0.097)
Included
2,160
0.481
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Underlying mechanism
Takeover pressure

Stein (1988, 1989): In the presence of information asymmetry,
shareholders tend to undervalue the stocks of companies investing
in innovative projects. This leads to a higher probability of the firm
facing a hostile takeover. In view of this, managers tend to put more
effort in short-term projects that offer quicker returns instead of
investing in long-term innovative projects.

Shleifer and Summers (1988): managers have less power over
shareholders when takeover threats are high, which leads to fewer
incentives to invest in activities with only long-run payoffs.

Kyle and Vila (1991) argue that high liquidity increases a firm’s
exposure to takeovers.

Thus, takeover exposures could be an underlying economic
mechanism through which stock liquidity impedes firm innovation.
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Underlying mechanism (Cont’d)
Takeover pressure

Hostile Takeover is the average probability of being a target in a hostile takeover, in the
three-year window before or after decimalization, with the probability being the predicted
value of TARGET based on the coefficients estimated in the logit regression of TARGETi,t+1 =
a + bQi,t + cPPETAi,t + dLN_CASHi,t + eBLOCKi,t + fLN_MVi,t + gINDMA_DUMi,t + hLEVi,t
+ iROAi,t+ YRt + INDj + errori,t, where TARGET is a dummy variable equal to one if the
company is target of an attempted or completed hostile acquisition and zero otherwise.
Similarly, All Takeover is the average probability of being a target in any takeover, in the
three-year window before or after decimalization.
Hostile Takeovers
All Takeovers
Mean
Mean control
treatment
difference
difference
(after -before)
(after - before)
0.212
0.035
(0.021)
(0.024)
0.040
0.019
(0.008)
(0.009)
Mean DiD
estimator
(treat - control)
T-statistics for
DiD estimator
0.177***
(0.032)
0.022*
(0.012)
5.036
1.828
Underlying mechanism (Cont’d)
22
Non-dedicated institutional investors

Porter (1992) argues that investment in long-term, intangible assets
tends to depress short-term earnings. He stresses that transient
shareholders may exit in response to a low quarterly earnings report
and quasi-indexers have little or no incentives to monitor. If
managers have incentives to keep the stock price high, they may cut
investment in long-term projects to boost short-term profits.

This effect should be more pronounced when liquidity is high
because high liquidity makes it easier to exit (Bhide (1993)).

Bushee (1998) highlights the possibility of cutting R&D expenditures
as a way to reverse earnings decline, especially when transient
institutional ownership is high.

Thus, pressure exerted by non-dedicated institutional investors could
be another channel through which liquidity impedes firm innovation.
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Underlying mechanism (Cont’d)
Non-dedicated institutional investors

Bushee (1998, 2001) classification:
Transient: relatively high diversification, high turnover, frequent momentum trading
Quasi-indexer: high diversification, low turnover, index type buy-and-hold strategies
Dedicated: high concentration, low turnover, no trading sensitivity to current earnings

TRAPCT, QUAPCT, and DEDPCT is the average institutional holdings (%) held by
transient institutional investors, quasi-indexers, and dedicated institutional investors in the
3-year window before or after decimalization
TRAPCT
QUAPCT
DEDPCT
Mean treatment
difference
(after - before)
0.040
(0.004)
0.064
(0.005)
0.013
(0.002)
Mean control
difference
(after -before)
-0.012
(0.002)
0.009
(0.004)
0.007
(0.003)
Mean DiD
estimator
(treat - control)
0.052***
(0.005)
0.055***
(0.006)
0.005
(0.003)
T-statistics for
DiD estimator
11.42
9.127
1.586
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Other Robustness Checks

We estimate Eq. (1) within each of the Fama-French 12 industry


Positive coefficient on ILLIQ in 11 industries and 6 are significant
The OLS results are robust to

Using different liquidity proxies including relative quoted spread,
Amihud (2002) illiquidity ratio, and PIN measure of Easley,
Kiefer, and O'Hara (1997)

Controlling for M&A deal size or removing firms involved in M&As

Partitioning sample into size quartiles

Interacting liquidity with time effects
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Conclusion

Using a diff-in-diff approach and exploiting the variation in stock
liquidity generated by two exogenous shocks (the decimalization of
the minimum tick size in 2001 and the shift in minimum tick size from
$1/8th to $1/16th in 1997), we show stock liquidity has a causal
negative effect on firm innovation.

There are least two possible underlying mechanisms.

High liquidity makes firms more prone to hostile takeover pressure.

High liquidity attracts transient investors who trade frequently to chase
current profits or quasi-indexers who follow passive indexing strategies
and fail to govern.

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