QUANTUM TERMINAL REPORT
ICU MEDICAL, INC
 
     
 

Snapshot


About ICU MEDICAL, INC [ICUI.NASDAQ]


ICU Medical, Inc. develops, manufactures, and sells medical devices used in vascular therapy, critical care, and oncology applications worldwide. It offers infusion therapy products comprising a tube running from a bottle or plastic bag containing a solution to a catheter inserted in a patient's vein. The company's infusion therapy products include needlefree connector products under the MicroClave, MicroClave Clear, and NanoClave brands; Neutron catheter patency connectors; SwabCap disinfecting caps; Tego hemodialysis connectors; NovaCath and SuperCath peripheral intravenous (IV) catheters; and ChemoLock and ChemoClave closed system transfer devices, as well as Diana hazardous drug compounding system for the preparation of hazardous drugs. It also provides IV therapy and diluents, including sodium chloride, dextrose, balanced electrolyte solutions, lactated ringer's, ringer's, mannitol, sodium chloride/dextrose, and sterile water; and irrigation/urologics, such as sodium chloride irrigation, sterile water irrigation, physiologic solutions, ringer's irrigation, acetic acid irrigation, glycine irrigation, sorbitol-mannitol irrigation, flexible containers, and pour bottle options. In addition, the company offers infusion pumps under the Plum 360 and LifeCare PCA brands; Cogent 2-in-1 and CardioFlo hemodynamic monitoring systems; TDQ and OptiQ cardiac output monitoring catheters; Transpac blood pressure transducers; and SafeSet closed blood sampling and conservation systems. Further, it provides ICU Medical Mednet, an enterprise medication management platform that connects smart pumps to hospital's electronic health records, asset tracking systems, and alarm notification platforms. The company sells its products to acute care hospitals, wholesalers, ambulatory clinics, and alternate site facilities, such as clinics, home health care providers, and long-term care facilities. ICU Medical, Inc. was founded in 1984 and is headquartered in San Clemente, California.


Patenting Organisations
EXCELSIOR MEDICAL CORPORATION (55)  ICU MEDICAL (588)  PURSUIT VASCULAR (21)  TANGENT MEDICAL TECHNOLOGIES (14)  


 FUNDAMENTAL INFORMATION

General

ICU Medical, Inc., together with its subsidiaries, develops, manufactures, and sells medical devices used in vascular therapy and critical care applications worldwide. The company's infusion therapy products include needlefree connector products under the MicroClave, MicroClave Clear, and NanoClave brands; Neutron catheter patency connectors; SwabCap disinfecting caps; Tego hemodialysis connectors; NovaCath and SuperCath peripheral intravenous (IV) catheters; ClearGuard HD, an antimicrobial barrier cap for hemodialysis catheters; and ChemoLock and ChemoClave closed system transfer devices, as well as Diana hazardous drug compounding system for the preparation of hazardous drugs. It also provides IV therapy and diluents, including sodium chloride, dextrose, balanced electrolyte solutions, lactated ringer's, ringer's, mannitol, sodium chloride/dextrose, and sterile water; and irrigation, such as sodium chloride irrigation, sterile water irrigation, physiologic solutions, ringer's irrigation, acetic acid irrigation, glycine irrigation, sorbitol-mannitol irrigation, flexible containers, and pour bottle options. In addition, the company offers infusion pumps under the Plum 360 and LifeCare PCA brands; Cogent 2-in-1 and CardioFlo hemodynamic monitoring systems; TDQ and OptiQ cardiac output monitoring catheters; TriOx venous oximetry catheters; Transpac blood pressure transducers; and SafeSet closed blood sampling and conservation systems. Further, it provides ICU Medical Mednet, an enterprise medication management platform that connects smart pumps to hospital's electronic health records, asset tracking systems, and alarm notification platforms, as well as related professional services. The company sells its products to acute care hospitals, wholesalers, ambulatory clinics, and alternate site facilities, such as clinics, home health care providers, and long-term care facilities. ICU Medical, Inc. was founded in 1984 and is headquartered in San Clemente, California.

Full Name:ICU Medical, Inc
Website:http://www.icumed.com
Country:USA
Address:951 Calle Amanecer, San Clemente, CA, United States, 92673
Phone:949 366 2183
Full Time Employees:8,000
Stock Code & Exchange:ICUI.NASDAQ
ISIN:US44930G1076
CUSIP:44930G107
CIK:0000883984
Employer-Id Number:33-0022692
Currency:US Dollar - USD
Sector:Healthcare
Industry:Medical Instruments & Supplies
Gic Sector:Health Care
Gic Group:Health Care Equipment & Services
Gic Industry:Health Care Equipment & Supplies
Gic Sub Industry:Health Care Supplies
Officers:
  • Mr. Vivek Jain (CEO & Chairman), born 1972
  • Mr. Christian B. Voigtlander (Chief Operating Officer), born 1968
  • Ms. Virginia Ruth Sanzone (Corp. VP, Gen. Counsel & Sec.), born 1976
  • Mr. Daniel Woolson (Corp. VP & GM of Infusion Systems), born 1977
  • Mr. Brian Michael Bonnell (CFO & Treasurer), born 1974
  • Mr. Kevin J. McGrody (Chief Accounting Officer), born NA
  • Mr. Steven C. Riggs (Exec. Officer of Special Project Opertions), born 1959

    Highlights

    Market Capitalization MM:4,145.87
    EBITDA:231,203,008.00
    PE Ratio:52.28
    PEG Ratio:5.23
    Wall Street Target Price:224.00
    Book Value:69.26
    Dividend / Share:0.00
    Dividend Yield:0.000000
    Earnings / Share:3.78
    EPS Estimate Current Year:6.88
    EPS Estimate Next Year:7.35
    EPS Estimate Next Quarter:1.56
    EPS Estimate Current Quarter:1.31
    Most Recent Quarter:2020-09-30
    Profit Margin:0.06
    Operating Margin TTM:0.12
    Return On Assets TTM:0.05
    Return On Equity TTM:0.06
    Revenue TTM:1,266,076,032.00
    Revenue Per Share TTM:60.79
    Quarterly Revenue Growth YOY:0.04 %
    Gross Profit TTM:488,164,000.00
    Diluted Eps TTM:3.78
    Quarterly Earnings Growth YOY:-0.07 %


    Valuation

    Trailing PE:52.28
    Forward PE:22.88
    Price-Sales TTM:3.15
    Price-Book MRQ:2.67
    Enterprise Value - Revenue:2.82
    Enterprise Value - Ebitda:19.51


    Share Statistics

    Shares Outstanding:20,981,100.00
    Shares Float:19,608,123.00
    Percent Insiders:6.54 %
    Percent Institutions:94.48 %
    Shares Short:262,974.00
    Shares Short (Prior Month):259,562.00
    Short Ratio:2.01
    Short Percent Outstanding:0.01 %
    Short Percent Float:0.02 %


    Technicals

    Beta:0.70
    52 Week High:236.51
    52 Week Low:158.01
    50 Day Moving Average:189.32
    200 Day Moving Average:188.44


    Splits & Dividends

    Forward Annual Dividend Rate:0.00
    Forward Annual Dividend Yield:0.00
    Payout Ratio:0.00
    Dividend Date:0000-00-00
    Ex Dividend Date:0000-00-00
    Last Split Factor:3:2
    Last Split Date:2002-03-18
    Number Of Dividends 2002:1


    Analyst Ratings

    Rating:0.00
    Target Price:0.00
    Strong Buy:0.00
    Buy:0.00
    Hold:0.00
    Sell:0.00
    Strong Sell:0.00


    ESG Ratings

    Rating Date:
    Total ESG:0.00
    Total ESG Percentile:0.00
    Environment Score:0.00
    Environment Score Percentile:0.00
    Social Score:0.00
    Social Score Percentile:0.00
    Governance Score:0.00
    Governance Score Percentile:0.00
    Controversy Level:0.00


    Financials

    INCOME STATEMENTS (USD MM)
    DATE REVENUE COSTS OF REVENUE GROSS PROFIT OPEX R&D EBIT
    2019-12-31 1,266.21 794.34 471.86 364.50 48.61 115.26
    2018-12-31 1,400.04 830.01 570.03 1,211.03 52.87 97.82
    2017-12-31 1,292.61 866.52 426.10 1,221.72 51.25 119.90
    2016-12-31 379.37 177.97 201.40 280.36 12.96 118.07
    2015-12-31 341.67 160.87 180.80 112.23 15.71 69.70
    2014-12-31 309.26 157.86 151.40 112.36 18.33 39.79
    2013-12-31 313.72 158.98 154.73 102.78 12.41 52.71
    2012-12-31 316.87 160.36 156.51 95.23 10.63 61.84
    2011-12-31 302.20 159.84 142.35 77.13 8.59 66.42
    2010-12-31 284.58 153.99 130.59 81.31 4.68 49.41
    2009-12-31 231.51 122.70 108.82 70.85 2.65 39.15
    2008-12-31 204.73 100.69 104.04 0.00 4.82 0.00
    2007-12-31 188.14 98.10 90.04 0.00 8.11 0.00
    2006-12-31 201.61 106.70 94.92 0.00 7.66 0.00
    2005-12-31 157.53 78.43 79.10 0.00 4.82 0.00
    2004-12-31 75.55 30.36 45.20 0.00 3.38 0.00
    2003-12-31 107.35 41.08 66.27 0.00 1.76 0.00
    2002-12-31 87.81 31.18 56.63 0.00 1.47 0.00
    2001-12-31 69.06 23.90 45.16 0.00 1.19 0.00
    2000-12-31 56.19 19.18 37.02 0.00 1.48 0.00
    BALANCE SHEETS (USD MM)
    DATE ASSETS INTANGIBLES CURRENT ASSETS CASH ACCOUNTS PAYABLE CURRENT LIABILTIES LIABILITIES
    2019-12-31 1,692.38 211.41 882.20 268.67 246.41 248.47 297.84
    2018-12-31 1,585.39 133.42 927.04 344.78 120.47 249.29 321.74
    2017-12-31 1,496.95 143.75 864.66 290.07 78.23 210.29 298.70
    2016-12-31 704.69 22.38 569.10 445.08 14.64 40.54 44.53
    2015-12-31 626.83 23.94 505.01 336.16 42.62 42.62 46.95
    2014-12-31 541.10 7.06 437.21 275.81 28.73 28.73 32.85
    2013-12-31 499.64 8.49 394.30 226.02 26.89 26.89 34.92
    2012-12-31 428.51 9.95 325.50 146.90 29.12 29.12 37.66
    2011-12-31 361.11 11.42 260.41 99.59 29.31 29.31 40.54
    2010-12-31 312.23 14.81 207.83 78.85 25.51 25.76 37.94
    2009-12-31 309.15 16.78 207.94 51.25 31.31 33.70 44.15
    2008-12-31 283.43 10.78 0.00 55.70 7.88 21.96 30.40
    2007-12-31 242.59 11.88 0.00 7.87 8.44 21.48 28.69
    2006-12-31 244.25 9.78 0.00 13.15 8.13 15.92 19.00
    2005-12-31 204.54 10.96 0.00 6.85 5.08 13.89 14.42
    2004-12-31 164.77 2.78 0.00 5.62 2.69 7.45 7.45
    2003-12-31 164.29 4.17 0.00 1.79 3.05 8.29 8.29
    2002-12-31 157.03 3.35 0.00 4.17 5.05 11.65 11.65
    2001-12-31 117.34 0.35 0.00 3.90 2.40 10.67 10.67
    2000-12-31 92.86 0.35 0.00 1.95 1.69 9.48 9.48
    CASH FLOWS (USD MM)
    DATE CF FROM INVESTING CF FROM FINANCING NET INCOME CASH CHG CF FROM OPERATING DIVIDENDS PAID NET INCOME CHG
    2019-12-31 -166.89 -10.91 101.04 -75.88 101.92 0.00 2.59
    2018-12-31 -103.37 8.02 28.79 54.71 160.22 0.00 89.12
    2017-12-31 -266.87 -44.35 68.64 -155.01 154.42 0.00 -31.90
    2016-12-31 16.28 2.47 63.08 108.92 89.94 0.00 16.56
    2015-12-31 -11.24 25.01 44.99 68.63 54.87 0.00 15.97
    2014-12-31 -21.75 19.35 26.34 58.24 60.64 0.00 9.27
    2013-12-31 -13.12 24.39 40.42 77.00 65.73 0.00 6.25
    2012-12-31 -41.37 21.63 41.28 46.53 66.27 0.00 5.76
    2011-12-31 -45.73 2.13 44.67 20.89 64.49 0.00 4.62
    2010-12-31 19.63 -22.88 30.93 29.85 33.10 0.00 4.25
    2009-12-31 -35.23 -17.69 26.56 -4.31 48.61 0.00 2.71
    2008-12-31 3.61 13.98 24.30 0.00 30.23 0.00 0.00
    2007-12-31 0.00 0.00 23.08 0.00 41.51 0.00 0.00
    2006-12-31 0.00 9.28 25.66 0.00 31.61 0.00 0.00
    2005-12-31 0.00 7.66 20.27 0.00 27.37 0.00 0.00
    2004-12-31 0.00 0.00 5.00 0.00 25.28 0.00 0.00
    2003-12-31 0.00 0.00 22.30 0.00 22.83 0.00 0.00
    2002-12-31 0.00 8.72 19.68 0.00 28.10 0.00 0.00
    2001-12-31 0.00 4.15 15.39 0.00 24.33 0.00 0.00
    2000-12-31 0.00 3.58 11.79 0.00 12.76 0.00 0.00



  •  KNOWLEDGE

    Knowledge Profile

    The Knowledge Graph is a model of a knowledge domain created by subject-matter experts with the help of intelligent machine learning algorithms. It provides a structure and common interface for innovation data and enables the creation of smart multilateral relations throughout patent and IP data. Structured as an additional virtual data layer, the Knowledge Graph lies on top of our databases or data sets to link all data together at scale – be it structured or unstructured.

    The following knowledge graph shows the top 10 technological topics the company is operating in, ranked by patent strength (based on OECD's recommended Patent Quality Composite Index). This provides a good overview of the innovative profile.

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    Competitors Referencing and Applying

    Knowing who your competitors are, and what they are offering, can help you to make your products, services and marketing stand out. It will enable you to set your prices competitively and help you to respond to rival marketing campaigns with your own initiatives. You can use this knowledge to create marketing strategies that take advantage of your competitors' weaknesses, and improve your own business performance. You can also assess any threats posed by both new entrants to your market and current competitors. This knowledge will help you to be realistic about how successful you can be.

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    Knowledge Universe (Graph)

    This section displays the cluster structure of patents in the citation space. A two-dimensional representation of the corporation, its applicants, its strongest technological topics, its patents, patents citing them and applicants owning these citing patents. Sizes of the nodes represent the different patent strengths (based on OECD ratings). Data is clustered by the strongest fields and relationships between them can be recognized by the naked eye. The resulting visual clusters show the dependencies between them, whereas local densities and gravity is used to structure the graph.

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     INTELLECTUAL PROPERTY

    Patent Filing Activity

    Historical patent filing activity over time.

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    Emerging IP (Word-Cloud)

    A word cloud is a popular visualization of words typically associated with Internet keywords and text data. They are most commonly used to highlight popular or trending terms based on frequency of use and prominence. A word cloud is a beautiful, informative image that communicates much in a single glance. In this case, a word cloud has been created utilizing the words found in the strongest patent filings of the company in the period over the last 5 years. This shows the currently most important terms of their technology.

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    Strongest Patents (Based on OECD Strength)

    The strongest patents. Patent quality indicators try to capture both the technological and the economic value of innovations, and are typically based on patent citations, claims, patent renewals and patent family size. They are considered meaningful measures of research productivity and are found to be correlated with the social and private value of the patented inventions. The difference in average patent quality across firms is generally associated with the market evaluation of firms. The patent quality indicator is a composite indicator based on four to six dimensions of patents’ underlying quality: forward citations; patent family size; number of claims; generality index; plus backward citations and grant lag. It builds on Lanjouw and Shankerman (2004) and incorporates the generality measure, and a measure accounting for the length of the examination process (i.e. the grant lag index). All components are normalised according to patent cohorts stratified by year and technological field and are given equal importance (no weights).

    This patent quality measures aim to facilitate analysis both at the level of the individual patent and at the aggregate patent portfolio level. They are intended to help addressing a number of policy- relevant questions, for example, related to: firms innovation strategies and performance; enterprise dynamics, including the drivers of enterprise creation and of mergers and acquisitions; the determinants of productivity; the financing of innovative enterprises; the output of R&D activities and the returns to R&D investments; the depreciation of R&D; the output of universities and of public research organisations.

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    Patent Quality Index

    Number of forward citations (up to 5 years after publication); patent family size; number of claims; the patent generality index; number of backward citations; and the grant lag index. Only granted patents are covered by the index. Patent Quality Index(high) contains all stocks in the top 75% percentile.


    Most Recent Patent Filings

    The most recent patents associated with this organisation:

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     RATINGS & INDICATORS

    Stock Chart

    In a proprietary research paper we demonstrate that analysing the intangible assets of a company in great detail allows building a model to accurately forecast a company’s ability to convert its intellectual property into stock value in the following quarter. We also demonstrate that a company’s IP impact correlates significantly with its future stock returns. Our paper offers a new innovation-related measure, IPI, which is contemporaneously associated with market valuation and predicts future operating performance and stock returns. Existing studies relating to innovation and market performance focus on the effects of either the input (R&D) or the output (patents) of innovation separately. Our study differs in focusing on Artificial Intelligence based innovative efficiency as a ratio of innovative output to input, based on the idea that efficiency should be highly value relevant. We find that the predictive power of AI innovation is incremental to that of other innovation related variables such as R&D intensity, significant R&D growth, patent counts and citations.

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    The Current Impact Index (CII) is calculated with the mean value of all forward citations of identified IP publications of a company, published within the previous 365 days, scaled by the mean value of all forward citations of identified AI-IP publications of all companies from the same industry, published within the previous 365 days. This measure expresses the technological impact of a company’s IP compared to its industry peers. For example, a value of 1.10 would mean that the company’s current AI-IP impact is 10% higher than its peers at the day of the calculation. Calculated monthly.

    IP to Market Value Indicator (IPI)

    To create a robust IP to market value model, we incorporate multiple parameters like R&D expenses, IP, CII and revenue and EBITDA into the model. In this paper we apply a Long Short-Term Memory (LSTM) recurrent neural network. We frame the supervised learning problem as predicting the stock prices at the current month (t) given the CII values at the prior 24 time steps (2 years), the R&D expenses at the prior 4 time steps and the revenues at the prior 4 time steps (4 months). As the OLS regression above showed, lag times for up to 2 years before can be an important measurement. The input values have been normalized and chosen because of the evidently strong correlation between R&D expenses, intellectual property and resulting revenues. For each of the companies, a dedicated Long Short-Term Memory (LSTM) recurrent neural network is being trained. Calculated monthly.


    Indicators & Recommendations

    In a proprietary research paper we demonstrate that analysing the intangible assets of a company in great detail allows building a model to accurately forecast a company’s ability to convert its intellectual property into stock value in the following quarter. We also demonstrate that a company’s IP impact correlates significantly with its future stock returns. Our paper offers a new innovation-related measure, IPI, which is contemporaneously associated with market valuation and predicts future operating performance and stock returns. Existing studies relating to innovation and market performance focus on the effects of either the input (R&D) or the output (patents) of innovation separately. Our study differs in focusing on Artificial Intelligence based innovative efficiency as a ratio of innovative output to input, based on the idea that efficiency should be highly value relevant. We find that the predictive power of AI innovation is incremental to that of other innovation related variables such as R&D intensity, significant R&D growth, patent counts and citations.

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    Current Impact Index (CII) (0-1: below sector; >1: above sector)

    The Current Impact Index (CII) is calculated with the mean value of all forward citations of identified IP publications of a company, published within the previous 365 days, scaled by the mean value of all forward citations of identified AI-IP publications of all companies from the same industry, published within the previous 365 days. This measure expresses the technological impact of a company’s IP compared to its industry peers. For example, a value of 1.10 would mean that the company’s current AI-IP impact is 10% higher than its peers at the day of the calculation. Calculated monthly.

    IP to Market Value Indicator (IPI)

    To create a robust IP to market value model, we incorporate multiple parameters like R&D expenses, IP, CII and revenue and EBITDA into the model. In this paper we apply a Long Short-Term Memory (LSTM) recurrent neural network. We frame the supervised learning problem as predicting the stock prices at the current month (t) given the CII values at the prior 24 time steps (2 years), the R&D expenses at the prior 4 time steps and the revenues at the prior 4 time steps (4 months). As the OLS regression above showed, lag times for up to 2 years before can be an important measurement. The input values have been normalized and chosen because of the evidently strong correlation between R&D expenses, intellectual property and resulting revenues. For each of the companies, a dedicated Long Short-Term Memory (LSTM) recurrent neural network is being trained. Calculated monthly.


    OECD Based IP Ranking

    Patent quality indicators try to capture both the technological and the economic value of innovations, and are typically based on patent citations, claims, patent renewals and patent family size. They are considered meaningful measures of research productivity and are found to be correlated with the social and private value of the patented inventions. The difference in average patent quality across firms is generally associated with the market evaluation of firms. The patent quality indicator is a composite indicator based on four to six dimensions of patents’ underlying quality: forward citations; patent family size; number of claims; generality index; plus backward citations and grant lag. It builds on Lanjouw and Shankerman (2004) and incorporates the generality measure, and a measure accounting for the length of the examination process (i.e. the grant lag index). All components are normalised according to patent cohorts stratified by year and technological field and are given equal importance (no weights).

    This patent quality measures aim to facilitate analysis both at the level of the individual patent and at the aggregate patent portfolio level. They are intended to help addressing a number of policy- relevant questions, for example, related to: firms innovation strategies and performance; enterprise dynamics, including the drivers of enterprise creation and of mergers and acquisitions; the determinants of productivity; the financing of innovative enterprises; the output of R&D activities and the returns to R&D investments; the depreciation of R&D; the output of universities and of public research organisations.

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    Patent Quality Index

    Number of forward citations (up to 5 years after publication); patent family size; number of claims; the patent generality index; number of backward citations; and the grant lag index. Only granted patents are covered by the index. Patent Quality Index(high) contains all stocks in the top 75% percentile.


    Harvard Ability Index

    We find that the market consistently misvalues innovation in an ex-ante, predictable way. Specifically, the market does not take into account the information in firms’ past R&D abilities. Firms that have been successful in the past and that invest heavily in R&D as a percentage of sales (“GoodR&D” firms) earn substantially higher future stock returns than firms that invest identical amounts in R&D, but that have poor past track records (“BadR&D” firms). A portfolio of GoodR&D firms earns equal- and value-weighted excess returns of 135 basis points per month (t=2.76) and 122 basis points per month (t=2.61), and 4-factor alphas of 90 basis points per month (t=3.11) and 78 basis points per month (t=2.27), respectively. In contrast, the portfolio of firms with poor past track records but that invests the same amount of R&D (BadR&D) earns -15 basis points per month in 4-factor value-weighted alpha (t=0.56). The spread portfolio that takes identical high R&D-level portfolios, but exploits differences in past track records, has a 4-factor alpha of 93 basis points per month (t=2.30) or over 11% per year. Returns to the “GoodR&D” (and spread) portfolios are large and significant in the first year, and then returns remain slightly positive but basically plateau in the second and third years, with no reversal. This suggests that we are not capturing a form of overreaction, but instead that the embedded information regarding innovation that the market is misvaluing is important for fundamental firm value.

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    Ability (0-4; low to high)

    “Ability” is meant to capture how good a firm is at turning R&D expenditures into something the firm values. We have run our tests using a number of measures of what the firm “values” and our results are robust to the various measures we have tried. We compute firm “Ability” by running rolling firm-by-firm regressions of firm-level sales growth (defined as log(Sales t/Sales t-1)) on lagged R&D (R&D t-j/Sales t-j; where j=1,2,3,4,5). We run separate regressions for 5 different lags of R&D (i.e., R&D from years t-1, t-2, t-3, t-4, and t-5); we then take the average of these five R&D regression coefficients as our measure of ability. The abilities for all companies are clustered Quintiles (0-4). Ability(high) contains all stocks in the top quintile (4).

    R&D to Revenue (0-9; low to high)

    We scale R&D by sales. R&D(low) contains all stocks below the 30th percentile in R&D (but who have R&D greater than zero), and R&D(high) contains all stocks above the 70th percentile (>7) in R&D . We compute firm-year ability using the annual average of the rolling regression coefficients of sales growth on 5 lags of R&D (scaled by sales).


    Innovative Efficiency

    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.

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    Patents/RDC

    Patents/RDC is defined as the ratio of firm i’s patents granted in year t (Patentsi,t) scaled by its R&D capital [the 5-year cumulative R&D expenses assuming an annual depreciation rate of 20% as in Chan, Lakonishok, and Sougiannis (2001) and Lev, Sarath, and Sougiannis (2005)] in fiscal year ending in year t􏰀2. We allow a 2-year gap between R&D capital and patents granted as it takes, on average, 2 years for the USPTO to grant a patent application (Hall, Jaffe, and Trajtenberg, 2001). The use of cumulative R&D expenses as the denomi- nator in this IE measure is premised on R&D expenses over the preceding 5 years all contributing to successful patent applications filed in year t􏰀2 (which are approved in year t with the 2-year application-grant lag). Patents/RDC(high) contains all stocks in the top 66% percentile.

    Citations/RD

    to make sure the IE measure is fully observable to investors at the time of their investments, we follow the literature by using the citations received in the year before the forecast period by a firm’s patents that were granted over the previous 5 years. Specifically, we define Citations/RD in year t as the adjusted number of citations occurring in year t to firm i’s patents granted over the previous 5 years scaled by the sum of corresponding R&D expenses. The denominator, the sum of R&D expenses in years t􏰀3 to t􏰀7, is premised on only contemporaneous R&D expenses contributing to successful patent applications and a 2-year application-grant lag. For example, for patents granted in year t􏰀1, we assume the associated innovative input is R&D expenses incurred in year t􏰀3. We also set missing R&D to zero in computing the denominator. Citations/RD(high) contains all stocks in the top 66% percentile.


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