We find that innovative efficiency (IE), patents or citations scaled by research and development expenditures, is a strong positive predictor of future returns after controlling for firm characteristics and risk. The IE-return relation is associated with the loading on a mispricing factor, and the high Sharpe ratio of the Efficient Minus Inefficient (EMI) portfolio suggests that mispricing plays an important role. Further tests based upon attention and uncertainty proxies suggest that limited attention contributes to the effect. The high weight of the EMI portfolio return in the tangency portfolio suggests that IE captures incremental pricing effects relative to well-known factors. Link to the paper: https://ssrn.com/abstract=1799675

1. Electronic copy available at: https://ssrn.com/abstract=3191073
Innovative Efficiency and Stock Returns
David Hirshleifer, UC Irvine
Po-Hsuan Hsu, University of Hong Kong
Dongmei Li, University of South Carolina

2. Electronic copy available at: https://ssrn.com/abstract=3191073
Motivation — Limited Attention
• Literature has shown that limited attention can lead to
under-reaction and return predictability; especially for
information that is less salient or harder to process (e.g.,
statistical information vs. narratives).
• Theoretical: Hirshleifer & Teoh (2003); Peng & Xiong
(2006); Hirshleifer, Lim, and Teoh (2011)
• Empirical: Klibanoff, Lamont, and Wizman (1998);
Huberman and Regev (2001); Hong, Torous, and
Valkanov (2007), DellaVigna and Pollet (2009);
Hirshleifer, Lim, and Teoh (2009); Hou, Peng, and Xiong
(2009); etc.

3. Why do we care about efficiency?
• Efficiency in innovation is an important driver for firms’
value
• The Economist (1990) notes that “American industry
went on an R&D spending spree, with few big successes
to show for it.”
• According to Booz &
Co., the most
creative firms may
not spend the most
in R&D

4. Spend more or spend smartly?
• In the biotech/pharmaceutical industry, small companies
spend in aggregate around $28 billion annually on R&D,
which is much lower than the $50 billion R&D spending for
large companies (http://www.burrillandco.com/content/BT08_execSum.pdf)
• However, the share of approved new drugs from large
companies has gradually declined from roughly 75% since
the early 1980s to nearly 35% in 2008 (Munos 2009)
• At the same time, the share attributable to small
companies has jumped from 23% to nearly 65% during the
same period

5. Does the market actively impound innovative efficiency?
• Innovative efficiency, patents or citations per dollar of R&D,
is fairly hard-to-process and non-salient
– compared, e.g., to a story about why the iPhone 4s will or
won’t be a success or a news article on the potential
outcome of clinical phase trials for a drug candidate
• Information about innovations is especially hard to process
– requires industry expertise and theory of how the economic
fundamentals of a firm or its industry are changing
– long road from R&D to patents to marketable products
– resulting profit highly uncertain and long deferred
• These characteristics suggest that investors tend to
underreact to the information in innovative efficiency
 IE will positively predict future returns.

6. Summary of findings
• Innovative efficiency (IE) positively predicts future
operating performance, market valuation, and stock
returns controlling for characteristics and risks.
• Mispricing helps explain the IE-return relation:
– High IE firms exhibit significantly higher risk-adjusted
returns.
– The IE-return relation is stronger among firms that
receive low investor attention and have high valuation
uncertainty.
– The IE factors have very high ex-post Sharpe ratios.

7. Summary of findings (cont’d)
• The IE factors capture incremental pricing
effects relative to well-known factors.
–The IE factors substantially increase the Sharpe
ratio of the ex-post tangency portfolio when
combined with other well-known factors.
–The weights on the IE factors in the tangency
portfolio are very high.

8. Measures of IE (1)---Patents/RDC
• Patents granted in year t scaled by R&D capital in year
t–2
– The two-year lag reflects the average gap between
patent application year and grant year.
• Following Chan, Lakonishok, and Sougiannis (2001), we
compute R&D capital assuming an annual depreciation
rate of 20%.
• This measure is premised on cumulative R&D expenses
over the prior five years contributing to successful
patent applications.

9. Measures of IE (2)---Citations/RD
• Adjusted citations received in year t by patents granted
over the previous five years (t-1 to t-5) scaled by the sum of
matching R&D expenses:
• This measure is premised on contemporaneous R&D
expenses contributing to patent applications.

10. IE and Future ROA and Cash Flows (regressions)

11. IE and Market Valuation (regressions)

12. IE and Future Stock Returns (portfolio analysis)

13. IE and Future Stock Returns (regressions)
A. IE = Patents/RDC B. IE = Citations/RD
ln(1+IE) 0.08 0.04 0.11 0.07
(4.44) (1.81) (5.13) (2.92)
ln(1+RD/ME) 0.13 0.13
(3.28) (3.22)
Substantial R&D Growth 0.12 0.12
(1.08) (1.11)
ln(1+PAT/ME) 0.05 0.05
(1.78) (2.29)
Quartile rank of ∆APC 0.07 0.05
(2.65) (1.94)
ln(Size) –0.30 –0.30 –0.30 –0.30
(–3.06) (–3.13) (–3.13) (–3.14)
ln(BTM) 0.39 0.37 0.39 0.37
(6.76) (6.32) (6.73) (6.34)
Momentum –0.05 –0.06 –0.05 –0.06
(–0.48) (–0.60) (–0.47) (–0.60)
ln(1+AD/ME) 0.06 0.05 0.06 0.06
(1.81) (1.74) (1.81) (1.76)
ln(1+CapEx/ME) –0.11 –0.12 –0.11 –0.12
(–3.09) (–3.25) (–3.09) (–3.25)
ROA 0.15 0.16 0.15 0.15
(2.96) (3.20) (2.93) (3.16)
Asset Growth –0.26 –0.24 –0.26 –0.24
(–6.36) (–5.89) (–6.32) (–5.89)
Net Stock Issuance –0.12 –0.14 –0.12 –0.14
(–2.54) (–3.26) (–2.56) (–3.30)
Institutional Ownership 0.14 0.14 0.14 0.14
(3.00) (2.90) (2.94) (2.89)

14. How does the IE-return relation vary with investor attention?
(subsamples regressions)
The IE-return relation is stronger among firms with low
investor attention.
A. IE = Patents/RDC B. IE = Citations/RD
IE only Augmented IE only Augmented
Micro size 0.09 0.09 0.13 0.07
(3.58) (2.32) (4.42) (2.34)
Small size 0.09 0.08 0.09 0.07
(3.17) (1.87) (2.81) (1.80)
Big size 0.05 0.01 0.04 0.02
(2.07) (0.32) (1.49) (0.61)
Low AC 0.09 0.06 0.10 0.08
(2.64) (1.62) (3.07) (2.21)
High AC 0.06 0.04 0.07 0.05
(2.61) (1.53) (2.72) (1.65)

15. How does the IE-return relation vary with valuation uncertainty?
(subsamples regressions)
The IE-return relation is stronger among hard-to-value stocks.
A. IE = Patents/RDC B. IE = Citations/RD
IE only Augmented IE only Augmented
Young age 0.12 0.07 0.14 0.11
(4.33) (1.96) (4.86) (3.26)
Old age 0.02 –0.00 0.05 0.02
(0.71) (–0.63) (2.10) (0.91)
High turnover 0.10 0.08 0.10 0.07
(4.12) (2.74) (4.12) (2.37)
Low turnover 0.03 –0.00 0.09 0.06
(1.09) (–0.91) (3.05) (1.79)
High IVOL 0.09 0.07 0.14 0.08
(4.66) (3.18) (4.19) (2.08)
Low IVOL 0.08 0.02 0.05 0.03
(2.80) (0.59) (2.49) (1.34)

16. Summary statistics of the Efficient-Minus-
Inefficient (EMI) and other factors
1. EMI1 is based on Patents/RDC, and EMI2 is based on Citations/RD.
2. EMI1 has the second highest ex-post Sharpe ratio due to its low
standard deviation and high mean returns.
EMI1 EMI2 MKT SMB HML INV ROE MOM UMO RDG ∆APC
RD/
ME
PAT/
ME
Mean 0.41 0.26 0.68 0.07 0.37 0.36 0.86 0.83 0.87 0.21 0.00 0.45 0.31
Stdev 1.75 1.80 4.31 3.26 3.05 1.78 3.72 4.27 3.26 3.04 1.54 3.73 1.65
t-stat 4.13 2.60 2.77 0.37 2.15 3.60 4.06 3.43 4.74 1.24 0.04 2.12 3.31
Sharpe
Ratio 0.23 0.15 0.16 0.02 0.12 0.20 0.23 0.19 0.27 0.07 0.00 0.12 0.19

17. The EMI and the market factor returns over time
1. The EMI1 (EMI2) returns are positive for 23 (21) out of 27 years.
2. EMI1 appears to be a good hedge against market downturns.
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
EMI1
MKT
EMI2

18. Correlations of factors

19. Ex post tangency portfolios with EMI

20. Conclusion
• Greater innovative efficiency  better future
operating performance, market valuation, and stock
returns.
• Mispricing may help explain the IE-return relation.
– Existing risk factor models cannot explain this relation.
– Stronger IE effect among firms with less investor
attention and higher valuation uncertainty.
– High Sharpe ratio of the EMI factors.

21. Conclusion (cont’d)
• EMI captures pricing effect above and beyond those
captured by the other well-known factors.
• Investors should consider IE for firm valuation and
trading strategies.