1. Don’t Hide Your Light under a Bushel: Innovative Originality and Stock Returns
David Hirshleifer
UC Irvine
Po-Hsuan Hsu
University of Hong Kong
Dongmei Li
University of South Carolina

2. Motivation – Psychological Evidence
Limited attention:
Individuals pay less attention to information that is hard to process
View toward complex signals:
People are more suspicious about more complex signals
They view the subject riskier than it actually is
e.g., Alter and Oppenheimer (2006), Song and Schwarz (2008, 2009, 2010)

3. Motivation – Valuation of Innovation
Hard
technical uncertainty
long road from concept to actual profits
market risk and competitor risk
Evidence suggesting market misvaluation of innovation proxies
Chan, Lakonishok, and Sougiannis (2001), Eberhart, Maxwell, and Siddique (2004), Hirshleifer, Hsu, and Li (2012), Cohen, Diether, and Malloy (2012), among others

4. Question and Hypotheses
Does market fully impound originality of a firm’s innovative activities (IO)?
If IO is + indicator of future fundamentals, limited attention 
(in spirit of models of Hirshleifer & Teoh 2003, Hirshleifer, Lim & Teoh 2011)
Higher IO predicts higher abnormal returns
Predictability is stronger among firms with
More valuation uncertainty
Lower investor attention
Higher sensitivity of fundamentals to IO

5. Empirical Findings IO and fundamentals IO and future abnormal returns
High-IO firms have lower contemporaneous ROA/ROE
High-IO predicts significantly higher future ROA/ROE
IO and future abnormal returns
High IO predicts significantly higher future abnormal returns
Predictability much stronger among younger, more opaque, lower investor attention, larger, and growth firms

6. Measure of Innovative Originality
Popular view of invention  recombinant search
E.g., Gilfillan 1935; Schumpeter 1939; Basalla 1988; Weitzman 1996; Henderson and Clark 1990.
Invention comes from
Combining technological components in novel manner
Reconfiguring existing combinations
Example/analogy:
Steamship — combination of boat/steam engine
Behavioral finance

7. Empirical Proxy of Innovative Originality
Breadth/Diversity of knowledge drawn upon by an invention
Patent level
# tech classes contained in a patent’s reference list (N)
Firm level
Average N across a firm’s recently granted patents

8. Empirical Proxy of IO (cont’d)
Common in innovation/corporate finance literature
E.g., Trajtenberg, Henderson & Jaffe (1997), Hall, Jaffe & Trajtenberg (2001), Lerner, Sorensen & Strömberg (2011), and Custodio, Ferreira & Matos (2013).
Additional supporting evidence
High IO predicts higher future citations received per patent and better future fundamentals

9. Proxies of Conditioning Variables
Valuation uncertainty (VU)
Firm age (Kumar 2009): an inverse proxy
Opacity of financial reports (Hutton, Marcus & Tehranian 2009)
VU Index = Standardized opacity – Standardized age
Investor attention
Analyst-to-shareholder ratio (ATS)
PEAD/Earnings surprise (Hirshleifer, Lim & Teoh 2009): an inverse proxy
Attention Index = Standardized ATS – Standardized PEAD/Earnings surprise

10. Proxies of Conditioning Variables (cont’d)
Sensitivity of fundamentals to IO
Size
Large firms benefit more from high IO
E.g., Schumpeter (1950); Acs and Audretsch (1987); Cohen, Levin & Mowery (1987)
Book-to-Market (BTM)
Values of growth firms (low BTM) derive more from IO
Sensitivity Index = Standardized size – Standardized BTM
Verification of the validity of these proxies:
Regressing future ROA/ROE on IO
Significantly higher slopes on IO among large firms, low BTM firms.

11. Empirical Test Design Portfolio sorts Fama-MacBeth regressions
Industry- and characteristics-adjusted returns
Alphas relative to standard risk factor models
Fama-MacBeth regressions
Industry effects
Other return predictors such as:
Size, book-to-market, momentum, innovative efficiency (IE), patents, R&D intensity, institutional ownership, stock illiquidity, short-term return reversal, asset growth, net stock issuance, capital investment, ROA, idiosyncratic volatility (IVOL) and skewness.

12. IO of Selected Industries
Industry (FF48)
Mean
Stdev
Min
P30
P50
P70
Max
Healthcare
7.01
4.48
1.00
4.60
6.00
8.00
27.00
Medical equipment
7.38
4.91
4.47
6.04
8.35
43.92
Pharmaceutical products
6.59
4.41
4.33
5.50
7.00
42.47
Chemicals
6.53
3.83
4.61
5.80
7.25
38.00
Machinery
5.95
3.38
4.00
5.12
6.93
27.25
Electrical equipment
5.78
3.92
3.71
5.00
6.50
49.06
Automobiles and trucks
5.10
2.45
4.92
5.75
20.50
Aircraft
4.93
2.08
3.74
4.75
5.77
12.65
Shipbuilding, railroad equipment
5.54
2.63
2.00
4.66
5.90
14.40
Telecommunications
6.32
4.16
5.03
6.38
31.00
Personal services
4.73
4.22
2.50
24.00
Business services
7.88
5.43
6.60
8.83
66.00
Computers
6.26
3.75
4.08
36.61
Electronic equipment
5.92
3.82
46.50
Measuring and control equipment
6.25
5.33
7.26
31.23
Transportation
4.42
9.00
22.00

13. Summary Statistics of IO Portfolios
IO Rank
Obs IO
Size (mn)
BTM
ROA (%)
ROE (%) No
3270
642
0.83
1.68
1.00
Low
419
3.02
1154
0.74
1.32
1.50
Middle
550
5.37
4334
0.70
3.05
4.40
High
409
9.78
2033
0.64
0.29
0.80

14. IO and Next Year’s ROE Model 1 Model 2 Estimate (%) t-stat IO 1.42
Estimate (%)
t-stat IO
1.42
(9.03)
0.94
(7.96)
ROE
25.01
(24.32)
24.89
(24.36)
ΔROE
-4.48
(-11.03)
-4.41
(-10.93)
MTB
-1.60
(-2.02)
-1.56
(-1.95)
AdvEx
0.72
(4.75)
0.71
(4.65)
CapEx
0.58
(1.59)
0.62
(1.67)
R&D
-2.38
(-7.04)
-2.52
(-7.32) IE
1.10
(5.35) R2
0.37
Results even stronger for next five-year average ROE and are robust for ROA.

15. Return Predictive Power of IO
IO Rank
Exret
Ind-adjret
Char-adjret
Alpha
MKT
SMB
HML
UMD No
0.60
-0.04
-0.03
-0.13
0.98
0.18
0.07
0.03
(2.38)
(-1.10)
(-0.13)
(-2.07)
(59.50)
(7.03)
(2.54)
(1.37)
Low
0.48
-0.12
1.00
0.17
(1.74)
(-1.73)
(-0.53)
(-1.49)
(40.60)
(4.96)
(-2.54)
(-1.02)
Middle
0.73
0.16
0.97
-0.15
-0.17
-0.02
(2.83)
(0.76)
(0.31)
(2.49)
(53.59)
(-6.07)
(-5.75)
High
0.80
0.09
0.19
0.24
0.95
0.01
(3.09)
(1.86)
(2.74)
(44.26)
(0.21)
(-3.08)
(-1.64)
High-Low
0.32
0.22
0.37
-0.05
-0.16
-0.01
(2.62)
(2.43)
(2.67)
(3.12)
(-1.58)
(-3.39)
(-0.26)
(-0.34)
Results robust to controlling for other risk factors.

16. Valuation Uncertainty (VU) and Return Predictive Power of IO
Age
Exret
Ind-adjret
Char-adjret 4F
4F + IMC
4F + INV
4F + LIQ
4F + EMI
Young
0.75
0.68
0.80
0.81
0.82
0.89
0.83
0.79
(2.83)
(2.79)
(3.05)
(2.66)
(2.60)
(2.90)
(2.69)
Old
0.20
0.08
0.21
0.25
0.28
0.16
(1.50)
(0.87)
(1.58)
(1.78)
(1.72)
(1.70)
(1.92)
(1.10)
Opacity
High
0.57
0.33
0.62
0.66
(1.81)
(1.19)
(1.99)
(2.19)
(2.15)
(2.13)
(1.84)
(1.86)
Low
0.27
0.14
0.41
0.29
0.30
0.26
(1.25)
(0.90)
(1.07)
(1.09)
(0.97)
(1.20)
(1.11)
VU Index
0.69
0.61
0.72
0.70
0.71
0.64
(2.39)
(2.32)
(2.54)
(2.04)
(2.01)
(2.23)
(1.93)
0.19
0.11
(1.47)
(1.17)
(1.60)
(1.88)
(1.83)
(2.07)
(1.37)
Returns, alphas for high-low IO portfolios

17. Investor Attention and Return Predictive Power of IO
Attention proxied by analyst-to-shareholder ratio (ATS)
ATS
Exret
Ind-adjret
Char-adjret 4F
4F + IMC
4F + INV
4F + LIQ
4F + EMI
Low
0.34
0.23
0.33
0.39
0.37
0.42
0.32
(2.61)
(2.46)
(2.57)
(2.96)
(2.86)
(2.79)
(3.11)
(2.36)
High
0.12
0.07
0.18
0.14
(0.72)
(0.43)
(1.11)
(0.67)
(0.76)
(1.01)
(0.77)
(0.65)
Attention proxied by Post-Earnings-Announcement-Drift / Earning Surprise (PEAD)
PEAD
0.83
0.64
0.68
0.75
0.73
0.72
0.67
(3.96)
(3.47)
(3.17)
(3.30)
(3.25)
(3.14)
(3.09)
(2.94)
0.04
0.28
0.40
0.20
(0.53)
(0.19)
(1.04)
(1.12)
(1.03)
(1.28)
(0.66)
Attention proxied by the composite index
Index
0.60
0.54
0.70
0.69
0.71
0.56
(3.61)
(3.26)
(3.24)
(4.25)
(4.17)
(4.16)
(4.32)
(3.54)
0.06
0.13
0.15
(0.36)
(0.46)
(0.75)
(0.70)
(0.87)

18. Sensitivity of Future Profitability to IO and Return Predictive Power of IO
Size
Exret
Ind-adjret
Char-adjret 4F
4F + IMC
4F + INV
4F + LIQ
4F + EMI
Big
0.46
0.35
0.42
0.53
0.52
0.56
0.41
(2.86)
(3.11)
(2.70)
(3.19)
(3.08)
(3.12)
(3.32)
(2.50)
Small
0.00
-0.06
0.03
-0.08
-0.07
(-0.03)
(-0.61)
(0.34)
(-0.81)
(-0.76)
(-0.65)
(-0.68)
(-0.60)
BTM
Low
0.25
0.34
0.31
(2.44)
(2.35)
(2.32)
(2.87)
(2.79)
(2.84)
(2.14)
High
0.11
0.09
0.16
0.21
(1.38)
(0.82)
(0.51)
(0.68)
(0.70)
(0.49)
(0.92)
(0.94)
Sensitivity Index
0.27
0.36
0.40
0.44
(2.45)
(2.56)
(2.52)
(2.76)
(2.67)
(2.69)
(2.90)
(2.06)
0.05
-0.01
0.07
0.06
(0.36)
(0.23)
(-0.04)
(0.47)
(0.42)
(0.48)
(0.41)
(0.75)

19. Return predictive power of IO not driven by difference in IO spreads.
Average IO
Firm Age
Opacity
VU Index
IO Rank
Young
Old
High
Low
2.97
3.05
3.54
3.61
2.98
3.04
Middle
5.39
5.36
6.37
6.38
10.33
9.32
12.03
11.40
10.38
9.33
Analyst-to-Shareholder
PEAD/Earnings Surprise
Attention Index
3.08
3.03
3.56
3.43
3.06
5.37
5.82
5.79
9.26
10.06
10.08
10.37
9.75
9.57
Size
Book-to-Market
Sensitivity Index
Big
Small
3.36
2.95
3.11
2.91
5.35
5.38
8.48
10.13
9.90
9.61
9.67
9.99
Return predictive power of IO not driven by difference in IO spreads.

20. Average Size (in Millions)
Firm Age
Opacity
VU Index
IO Rank
Young
Old
High
Low
532
1754
1233
2290
565
1679
Middle
872
6287
4224
9182
1048
6062
581
3356
2066
4403
704
3100
Analyst-to-Shareholder
PEAD/Earnings Surprise
Attention Index
2073
861
3320
1994
1255
1589
7873
1568
8554
5448
4546
5280
3777
1025
4549
2210
2161
2589
Size
Book-to-Market
Sensitivity Index
Big
Small
10413
131
1550
707
2016
125
20405
146
6101
1885
6542
155
15578
136
2699
1007
3228
122
Return predictive power of IO not driven by small firms.

21. IE vs. IO Average IO IE Rank IO Exret Ind-adjret Char-adjret 4F
4F + IMC
4F + INV
4F + LIQ
4F + EMI L
High-Low
0.14
0.12
0.05
0.16
0.21
0.13
(0.66)
(0.71)
(0.24)
(0.78)
(0.79)
(0.99)
(0.61) M
0.08
0.18
0.22
0.20
0.28
0.15
(0.42)
(0.77)
(0.95)
(1.00)
(0.87)
(1.22)
(0.68) H
0.75
0.63
0.67
0.58
0.55
0.64
0.53
0.69
(2.43)
(2.20)
(2.12)
(2.02)
(1.90)
(2.21)
(1.76)
(2.31)
Average IO
IO Rank
IE Rank L M H
H-L
2.94
5.36
9.69
6.74
3.32
5.34
9.02
5.70
3.26
5.43
9.96
6.71

22. Fama-MacBeth Regressions (I)IO
Control Variables  
Model 1
0.16
BTM, size, momentum, institutional ownership, illiquidity, short-term return reversal
(7.22)
Model 2
0.08
Above variables plus innovative efficiency, patents-to-assets, R&D/ME, ROA, investment, net stock issuance, idiosyncratic volatility, skewness, sales diversity 
(2.93)

23. Fama-MacBeth Regressions (II)
Firm Age
Opacity
VU Index IO
Young
Old
High
Low
High-Low
Slope
0.12
0.06
0.17
-0.01
0.04
0.13
t-stat
(2.56)
(1.90)
(3.16)
(-0.21)
(4.62)
(1.85)
(3.15)
Analyst-to-Shareholder
PEAD/Earnings Surprise
Attention Index
Low-High
0.08
0.02
0.11
-0.02
(1.99)
(0.46)
(1.74)
(0.64)
(2.81)
(-0.44)
(2.41)
Size
Book-to-Market
Sensitivity Index
Big
Small
0.22
0.05
(3.03)
(2.90)
(0.89)
(3.47)
(1.30)
(2.29)
IO effect much stronger among firms with more valuation uncertainty, lower investor attention, and higher sensitivity of profitability to IO.

24. Additional Robustness Tests
Fama-French five-factor model (market, size, value, investment, profitability factors)
Subperiod analysis
Transaction costs
Alternative IO measure based on generalized Herfindahl index

25. Alternative Explanations
Risk
Information asymmetry
Financing constraints
Obsolescence
Investment-specific technological change

26. Summary and Conclusion
We document a positive relation between firms’ IO and future fundamentals and abnormal returns.
Relation stronger among firms with
Lower investor attention
Higher valuation uncertainty
Higher sensitivity of future profitability to IO.
Overall, evidence suggests that IO is a useful input for firm valuation, and evidence is consistent with limited investor attention and skepticism of complexity.