Accounting undergraduate Honors theses: Does analyst experience affect their understanding of non financial information? An analysis of the relation between patent information and analyst

This study examines whether analyst experience affects the relation between patent information and analyst forecast errors. U.S. Generally Accepted Accounting Principles require that firms expense all in-house research and development (R&D) costs. This means that even when R&D activities produce intangible assets with future economic benefits, firms cannot capitalize R&D costs as assets. Consequently, financial statements are largely deficient in the information they provide regarding the output of R&D activities.

University of Arkansas, Fayetteville

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Theses and Dissertations

8-2013

Does Analyst Experience Affect Their

Understanding of Non-Financial Information? An Analysis of the Relation between Patent

Information and Analyst Forecast Errors

Taiwhun Taylor Joo

University of Arkansas, Fayetteville

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Joo, Taiwhun Taylor, "Does Analyst Experience Affect Their Understanding of Non-Financial Information? An Analysis of the Relation

between Patent Information and Analyst Forecast Errors" (2013) Theses and Dissertations 833.

http://scholarworks.uark.edu/etd/833

Does Analyst Experience Affect Their Understanding of Non-Financial Information?

An Analysis of the Relation between Patent Information and Analyst Forecast Errors

Does Analyst Experience Affect Their Understanding of Non-Financial Information?

An Analysis of the Relation between Patent Information and Analyst Forecast Errors

A dissertation submitted in partial fulfillment

of the requirements for the degree of Doctor of Philosophy in Business Administration

By

Taiwhun T Joo Brigham Young University Bachelor of Science in Accounting, 2009 Brigham Young University Master of Accountancy, 2009

August 2013 University of Arkansas

This dissertation is approved for recommendations to the Graduate Council

Dr James Myers

Dissertation Director

Dr Linda Myers Dr Vernon Richardson

Committee Member Committee Member

Dr Junhee Han

ABSTRACT

This study examines whether analyst experience affects the relation between patent information and analyst forecast errors U.S Generally Accepted Accounting Principles require that firms expense all in-house research and development (R&D) costs This means that even when R&D activities produce intangible assets with future economic benefits, firms cannot capitalize R&D costs as assets Consequently, financial statements are largely deficient in the information they provide regarding the output of R&D activities However, patent information is one type of non-financial information about R&D output that is publicly available

Using updated patent data, I confirm the results of prior studies that find a positive

association between patent citations and future firm performance I also confirm the positive association between the absolute value of analyst forecast errors and patent citations Next, in my main tests, I examine whether analyst experience affects the relation between patent information and analyst forecast errors (absolute value and signed) I find that analysts with more experience are not better at incorporating patent information to make more accurate earnings forecasts Instead, they incorporate patent information to make more optimistic earnings forecasts than analysts with less experience My findings should be of interest to standard setters in deciding whether to require firms to disclose patent information because this information should be useful

to investors and analysts

ACKNOWLEDGEMENTS

Special thanks are due to my dissertation committee members and other professors in the Accounting Department for all of their comments and suggestions, as well as support Also, a special thanks goes out to my family and classmates in the Accounting Department for their support, especially to Dr E Scott Johnson and Lauren Dreher for their friendship

TABLE OF CONTENTS

1 INTRODUCTION 1

2 BACKGROUNDS AND HYPOTHESIS DEVELOPMENT 4

2.1 Patents 4

2.2 R&D and Patent Information 5

2.3 Patent Information and Future Firm Performance 8

2.4 Patent Information, Forecast Revisions, and Forecast Errors 9

2.5 Analyst Experience and the Relation between Patents Information and Forecast Errors 9

3 SAMPLE SELECTION AND DATA 11

3.1 Sample Selection 11

3.2 Patent Measures 11

4 METHODOOLGY 13

4.1 Patent Information and Future Firm Performance 13

4.2 Patent Information, Forecast Revisions, and Forecast Errors 15

4.3 Main Tests: Analyst Experience and the Relation between Patents Information and Forecast Errors Patent Information, Forecast Revisions, and Forecast Errors 17

5 RESULTS 20

5.1 Descriptive Statistics 20

5.2 Patent Information and Future Firm Performance 20

5.3 Patent Information, Forecast Revisions, and Forecast Errors 21

5.4 Analyst Experience and the Relation between Patents Information and Forecast Errors 22 6 ADDITIONAL TESTS 23

6.1 Alternative Scaling for Patent Measures 23

6.2 Other Types of Experience 24

6.3 R&D Capital 27

6.4 Regulation Fair Disclosure 28

6.5 Forecasting Horizons 29

6.6 Industry Effects 31

6.7 Truncating the Sample Period 31

7 CONCLUSION 32

8 REFERENCES 35

9 APPENDIX 39

10 TABLES 41

1 INTRODUCTION

This study examines whether analyst experience affects their ability to understand the impact of patents on future earnings Statement of Financial Accounting Standard (SFAS) No 2,

Accounting for Research and Development Costs, requires firms to expense all in-house research

and development (R&D) costs as they are incurred In other words, United States (U.S.)

Generally Accepted Accounting Principles (GAAP) treat investments in R&D as expenses, and R&D costs are not capitalized on the balance sheets This full expensing treatment of R&D investments can weaken the link between current and future earnings Therefore, analysts may use non-financial information to supplement accounting information (Amir et al., 2003) Using a sample of firms with patents granted from 1994 through 2005, this study examines the relation between patent information and analyst forecast errors, and investigates whether analyst

experience affects this relation

Some patents are more economically valuable than others Trajtenberg (1990) finds that the number of citations that a firm’s patents receive from follow-up patents is a better proxy for R&D output than is the number of patents Patent citations arise when follow-up patents cite previous patents.1 Prior studies find that firm value is positively associated with patent citations (Hirschey et al., 2001; Hall et al., 2005) Other studies find a positive relation between patent citations and future firm performance (Gu, 2005; Pandit et al., 2011) Before I examine the effect

of analyst experience on the relation between patent information and forecast errors, I confirm that the findings of these prior studies exist for my sample Using updated patent data, I find that the number of patent citations is positively associated with future earnings.2 After confirming

1

that firms with more patent citations have better future performance, I also confirm that analysts,

on average, make less accurate earnings forecasts for firms with more patent citations

The inherent difficulty in estimating the value of intangible assets is the main reason that U.S GAAP requires firms to expense the full costs of in-house R&D as incurred (FASB, 1974) Furthermore, active and transparent markets for intangible assets are limited (Gu and Wang, 2005) This lack of information about the value of R&D output might encourage financial

analysts to use information not disclosed in financial reports, such as patent data available

through the United States Patents and Trademarks Office (USPTO), when forecasting future earnings However, even though patent information from USPTO is publicly available, analysts may be unable to fully understand the implications of patents for future earnings due to the uncertainty associated with new technologies (Aboody and Lev, 1998; Amir et al., 2003)

Consistent with this, Amir et al (2003) find that analyst forecasts are less accurate and more optimistic in industries that are more R&D intensive Using the model from Amir et al (2003), I confirm that the analyst consensus earnings forecast is less accurate for firms with more patent citations I do not find evidence, however, that patent citations are related to forecast optimism

This leads to my main research question Do analysts learn from their experience in using patent information to make more accurate earnings forecasts?Prior studies find that, on average, more experienced analysts make more accurate earnings forecasts (Mikhail et al., 1997; Clement, 1999) In addition, Drake and Myers (2011) find that analysts’ general experience is associated with less accrual-related over-optimism, even when controlling for analysts’ firm-specific

experience They measure general experience as the number of prior years in which an analyst makes earnings forecasts for any firm, and firm-specific experience is the number of prior years

in which an analyst makes earnings forecasts for a given firm

In this study, I focus on firm-specific experience because patents are unique across

firms.3 If patent information is difficult to incorporate into forecasts, even with experience, I expect analyst experience to have no effect on the relation between patent information and

forecast errors If analysts with more experience have a better understanding of the implications

of patents for future earnings, I expect analysts with more experience to make more accurate earnings forecasts I find, however, that analysts with more experience are not better at using patent information to make more accurate earnings forecasts Interestingly, analysts with more experience appear to use patent information to make more optimistic earnings forecasts Overall,

I conclude that analysts with more experience have some understanding of the benefits of patents, but this understanding does not lead to more accurate earnings forecasts

My study contributes to the existing R&D literature My finding suggests that analysts with more experience have some understanding of patent information However, this

understanding is limited in that it allows more experienced analysts to make more optimistic, but not more accurate, earnings forecasts Regulators may consider requiring firms to disclose more information about the output of R&D activities because this information about R&D outputs appears to be useful and no other publicly available information on the output of R&D activities

is widely available Other voluntary disclosures on the benefits of R&D output, such as patent licensing income, is rare even among large firms investing heavily in R&D (Gu et al., 2004) Perhaps because of this lack of information, Cohen et al (2013) find that the market does not value past R&D activities appropriately

My study also contributes to the analyst forecast literature by showing that financial analysts do not fully incorporate all of the available information about patents when making

3

earnings forecasts Bradshaw, Richardson, and Sloan (2001) find that analysts are

over-optimistic in valuing accruals Given that prior studies observe a positive relation between patent information and future firm performance, one would expect analyst forecasts to incorporate patent information to compensate for the lack of other R&D output information However, I confirm that patent citations are associated with less accurate earnings forecasts Furthermore, I find that analysts with more experience are not better at making more accurate earnings forecasts

by incorporating patent information, although they do seem to better understand that patents lead

to improved future performance

The remainder of this paper is organized as follows Section 2 discusses previous

research and presents my hypotheses Section 3 describes the data, and Section 4 explains my methodology Section 5 presents the results from my analyses, and Section 6 presents results of additional tests Section 7 concludes

2 BACKGROUND AND HYPOTHESIS DEVELOPMENT

2.1 Patents

A patent is intellectual property that gives an inventor the right to “exclude others from making, using, offering for sale, or selling the invention throughout the United States or

importing the invention into the United States” for a specified time.4,5

Intellectual property rights are critical to the modern economy The Copyright and Patent Clause of the United States

Constitution allows the United States Congress to exercise enumerated power “to promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the

4

See 35 U.S.C 154 (a)(1) (2006)

5

exclusive Right to their respective Writings and Discoveries” (U.S Constitution, Article I, Section 8) Not only are individuals guaranteed property rights for tangible assets, but also for intangible assets (i.e., the individuals’ ideas) This right for inventions gives individuals and firms incentives to invest time and effort in innovative discoveries In exchange, the inventor discloses information about the invention to the public The USPTO assigns patents to individual inventors or to the firms for whom the inventors work Thus, the patent rights encourage firms to invest in innovative technologies by promising these firms exclusive rights to use the patents, given that the legal system is able to enforce these rights Lieberman and Montgomery (1988) highlight success in patents as a mechanism by which a firm establishes technology leadership, which leads to a first-mover advantage However, there is currently no requirement for firms to disclose information about patents or R&D activities in their financial statements

2.2 R&D and Patent Information

The prevalence of private R&D activities makes this study relevant In the past half century, while the investment in R&D activities as a proportion of the U.S Gross Domestic Product has remained steadily at 2-3%, the proportion of private R&D has increased and

government-sponsored R&D has decreased (Cohen et al 2013) Cohen et al (2013) suggest that the responsibility for allocating resources in R&D is on the private sector rather than on the government Therefore, the ability of market participants to understand the benefits of R&D activities and to allocate resources accordingly is important

The motivation for my study starts with patent information and its association with future firm performance Consider a firm that incurs costs to conduct in-house R&D activities These

activities produce intangible assets that have some economic value.6 This paper refers to these intangible assets as “R&D capital.” Currently, U.S GAAP does not allow firms to capitalize any in-house R&D costs (SFAS No 2); therefore, even if a firm were successful in R&D, the

financial statements would not reflect any R&D capital on their balance sheet Instead, the full amount of R&D costs incurred during the year would show up on the firm’s income statement as R&D expense

If U.S GAAP allowed firms to capitalize R&D costs, there would be significant

subjectivity in determining how much of R&D costs should be capitalized This subjectivity and uncertainty about the future implications of R&D capital are the main reasons why the Financial Accounting Standards Board (FASB) requires full expensing treatment Thus, financial

statements contain only information about the input of R&D activities (in the form of R&D expense), and lack information about the value of the output from R&D activities Rong (2012) suggests that market participants lack access to a considerable amount of information about R&D activities because management chooses to not disclose this information Currently, there is

no requirement for firms to disclose the estimated value of R&D capital, and there is no active market for many of these intangible assets Perhaps because of this lack of information, Cohen et

al (2013) find that the market generally misvalues R&D investment However, sophisticated market participants, such as analysts, may compensate for this lack of information about the value of R&D capital with non-financial information (Amir et al., 2003) One type of non-

financial information analysts can use is patent information because patents are the output of R&D activities and are publicly available from the USPTO

6

Patent information may be informative to market participants in a number of ways First,

if a person can understand the potential impact of a patented technology, patent information may shed light on future sales of a product or on the reduced cost of manufacturing a product Other information patents contain is the potential for royalty income Second, market participants may use patent information to estimate potential royalty income Gu et al (2004) find that royalty income is more persistent than earnings However, the authors state that voluntary disclosures about royalty income from patent licensing are uncommon Lastly, Cohen et al (2013) suggest that past information about R&D successes is informative about potential future success of R&D They find that stock returns of firms with higher ratios of R&D expense to sales in the past perform better in the future than do firms with the same dollar amount of R&D expense, but lower ratios of R&D expense to sales

My patent measures count the number of citations that firms’ patents subsequently

receive More important patents are likely to be cited more often, while less important or obscure patents are likely to receive fewer citations.7 Empirical studies find that the number of citations patents receive is a proxy for the patent’s importance or innovativeness (Trajtenberg, 1990; Harhoff et al., 1999; Hall et al., 2005) Trajtenberg (1990) follow prices and subsequent patent citations for Computed Tomography (CT) scanners to examine the value of innovations He finds that patents for CT scanners that command higher prices, receive more subsequent citations than patents for CT scanners that command lower prices Harhoff et al (1999) survey German firms with patents The authors ask the firms to value the firms’ patents and follow the subsequent citations for those patents They find that the number of patent citations is positively associated with the value of patents assigned by the firms Rong (2012) also suggests that the number of

7

patent citations is a signal of economic importance I use patent citations as information about the firms’ R&D success

2.3 Patent Information and Future Firm Performance

Before examining whether analysts’ experience improves their understanding of patent information in making earnings forecasts, I test whether the number of patent citations is useful for predicting future earnings This answers the question of whether a parsimonious count of patent information contains information related to the success of R&D activities and future firm performance In prior research, Hirschey et al (2001) and Hall et al (2005) find a significant and positive association between patent citations and Tobin’s q, and Gu (2005) and Pandit et al (2011) find that patent citations are positively associated with future earnings

I construct three patent citation measures to proxy for information about the output of R&D activities The first measure is the total number of citations received in the current year The number of patent citations in the current year is a proxy for the economic importance of a firm’s patents or a signal of potential royalty income from patent licensing The second measure counts patent citations over the past five years This measure is a proxy for the information about

a firm’s patent portfolio The third measure is the number of citations received in the past years,

in the current year, and in the future years Although the number of citations a patent receives in

the future is not observable ex ante, if analysts understand the future implications of specific

patents, this measure may be a good measure of the overall economic importance of patents I use future earnings to measure future performance and confirm that prior findings – that patent citations are positively associated with future firm performance – exist in my sample Additional tests using return on assets (ROA) as a measure of firm performance also confirm these findings

2.4 Patent Information, Forecast Revisions, and Forecast Errors

Next, I test whether patent information is associated with analyst forecast revisions and forecast errors (both absolute value and signed) Patent information may provide analysts with additional information about R&D capital If this is the case, I expect patent information to be associated with analyst forecast revisions I also expect patent information to be associated with more accurate earnings forecasts Finally, I expect patent information to be associated with more optimistic earnings forecasts, given the positive relation between patent information and future firm performance Alternatively, if patent information is difficult to understand and does not provide analysts with additional information about R&D capital, I expect to find no association between patent information and forecast revisions I also expect patent information to be

associated with less accurate earnings forecasts, and do not expect any difference in signed forecast errors

A few prior studies examine the relation between patent information and analyst forecast errors Using a sample of observations from 1983 through 1999, Gu (2005) finds that analysts,

on average, underestimate the future implications of patent citations In a follow-up study, Gu and Wang (2005) find that more innovative patents are associated with less accurate earnings forecasts.8

2.5 Analyst Experience and the Relation between Patent Information and Forecast Errors

This leads to my main research question How does an individual analyst’s experience affect the relation between patent information and analyst forecast errors? Learning-by-doing theory predicts that people learn to perform tasks better as they gain more experience related to the tasks (Arrow, 1962; Anzai and Simon, 1979) Arrow (1962) models how experience

increases labor productivity over time and Anzai and Simon (1979) study how people learn to perform a task during a problem-solving process Test subjects in Anzai and Simon’s experiment are given the task of solving the Tower of Hanoi puzzle The authors document how their

subjects learn by producing strategies and adapting

In the accounting literature, an experimental study by Shelton (1999) finds that more experienced auditors (both audit managers and partners) place less weight on irrelevant

information than do less experienced auditors (i.e., audit seniors) In addition, prior studies find that more experienced analysts make more accurate earnings forecasts (Mikhail et al., 1997, 2003; Clement, 1999) In my study, on the one hand, more experienced analysts may be able to better understand the future implications and benefits of patents On the other hand, the

implications and benefits of R&D capital are inherently difficult to estimate, which is why the FASB does not allow firms to capitalize in-house R&D costs in the first place Thus, an analyst’s experience may not allow him to overcome the inherent difficulty in estimating the future

implications and benefits of patents In this study, I focus on firm-specific experience because of the uniqueness of each firm’s patents My first hypothesis, stated in the alternative form, is:

H1: More experienced analysts are better at incorporating patent information to make more accurate earnings forecasts

My second hypothesis relates to how analyst experience affects signed forecast errors Given that patent information is positively related to future performance, I expect analysts who understand this relation to make more optimistic earnings forecasts than those who do not Furthermore, analysts may learn to better understand this relation with experience On the one hand, if more experienced analysts better understand the positive relation between patent

information and future earnings, I expect their earnings forecasts to be more optimistic than

earnings forecasts of less experienced analysts On the other hand, if more experienced analysts

do not understand the positive relation between patent information and future earnings any better than less experienced analysts, I do not expect their earnings forecasts to be more optimistic

H2: More experienced analysts are better at incorporating patent information to make more optimistic earnings forecasts than less experienced analysts

3 SAMPLE SELECTION AND DATA

3.1 Sample Selection

I examine earnings forecasts from 1994 to 2005 Beginning in 1994 ensures that the analyst earnings forecast data are consistent throughout the sample period because I/B/E/S changed its method of calculating actual earnings per share (EPS) in the early 1990s (Abarbanell and Lehavy, 2007; Drake and Myers, 2011) I exclude from my sample industries and firms that

do not have any patent After eliminating observations with insufficient data from the Compustat Annual file, stock return data from the CRSP Monthly file, and analyst data from the I/B/E/S Detailed file between 1994 and 2005, my sample consists of 36,278 analyst-firm-year

observations This sample includes 4,364 unique analysts, 1,576 unique firms, and 206 unique industries (3-digit SIC code)

3.2 Patent Measures

I construct my patent measures using publicly available data from the National Bureau of Economic Research (NBER) Patent Data Project Web site Specifically, I use the following datasets: [1] the dataset of records for every patent (more than 3.2 million observations from

1976 through 2006), [2] the dataset that matches patents to assignee codes, [3] the dataset that matches assignee codes to firm codes (GVKEY in Compustat), and [4] the dataset that matches

cited patents to citing patents I merge the four datasets into observations with firm identifiers, fiscal years, cited patent identifiers, and citing patent identifiers I then construct three measures

of patent information:

1 Number of new patent citations in the current year;

2 Number of patent citations in the past five years;

3 Number of total citations (over a five-year rolling period and in the future) related to patents granted in the past five years

In prior studies, to control for the number of citations patents usually receive in a given field, the number of citations is generally scaled by the average number of citations received in the

industry (Hirschey et al., 2001) or in the technology classification (Gu, 2005; Pandit et al., 2011)

I scale each measure by the median value for the 3-digit SIC code industry and year.9, 10 This scaling adjusts the patent measures to reflect the number of patent citations compared to other firms in the same industry in the same year (Hirschey et al., 2001)

The first measure counts the number of citations a firm’s patents receive in a given year Trajtenberg (1990) finds that the number of citations received is better for R&D output than the

Scaling by the industry benchmark is consistent with Hirschey et al (2001) Other prior

studies (Gu, 2005; Pandit et al., 2011) adjust their measures by means of each USPTO

subcategory year Using these means does not change my results I use industry-year instead of subcategory-year for two reasons: (1) Table 1 of Gu (2005) shows an example of Patents with their IDs and Patent Subcategories I find discrepancies between the Patent Subcategories

presented and the USPTO Classification from the actual patent documents Rather, the Patent Subcategory numbers seem to come from the manner in which the NBER categorized the patent, not the USPTO (2) Patents can be listed under multiple subcategories An example illustrates both of these reasonings: Table 1 Panel A (Gu, 2005) shows a patent ID of 4911173 with a Patent Subcategory of 32 The original patent document (available on the USPTO via Google Patent) shows that the correct U.S Classification is 128/662.06 and 128/4 The subcategory

number of a firm’s patents In other studies, the number of citations captures the relative

importance of patents a firm holds (Gu, 2005; Gu and Wang, 2005; Pandit et al., 2011) The reasoning is that more important or innovative patents receive more citations from follow-up patents than less important or obscure patents The second patent measure is the sum of the number of all citations received in the past five years I use this measure that includes citations received in the previous years as well as the current year because the information about previous R&D success may be relevant this year (Cohen et al., 2013), The third measure is the number of citations in the past five years and in the future related to the past five years’ patents The

reasoning for using patents granted in the past five years follows the finding in Lev and

Sougiannis (1996) that R&D capital (the sum of R&D expenses assuming straight-line

depreciation) is value relevant, on average, for five years I use these three patent citation

measures as information about the firms’ patents whether this information is a signal of

economic importance for future royalty income or for future R&D success

4 METHODOLOGY

4.1 Patent Information and Future Firm Performance

Before examining how analyst experience affects the relation between patent information and forecast errors, I confirm the association between patent citations and future firm

performance found by prior studies (Gu, 2005; Pandit et al., 2011) If patents guarantee firms exclusive rights to discoveries and inventions, I expect firms with more patents to have better future firm performance I follow the future earnings model in Cao et al (2011) and estimate all regressions in this study using ordinary least squares I control for heteroscedasticity using Roger’s standard errors and by clustering the residuals by firm (Petersen, 2009):

Ei,t+1 = α0 + α1 PATENTi,t +α2 Ei,t + α3 (D1 * Ei,t) + α4 ΔEi,t + α5 (D2 * ΔEi,t)

+ α6 ΔBVi,t-1 + α7 (D3 * ΔBVi,t-1) + α8 ΔDi,t + α9 BMi,t + α10 ΔRDCAPi,t + α10 RD_EXPi,t + εi,t

[1] where

Ei,t+1 = operating income after depreciation for firm i in t+1 scaled by the

market value of equity at the end of year t;

PATENT = one of the following six patent measures:

1 Number of new patent citations in the current year,

2 Number of patent citations in the past five years,

3 Number of total citations (over a five-year rolling period and in the future) related to patents granted in the past five years;

Ei,t = operating income after depreciation for firm i in t scaled by market value

of equity at the end of year t;

D1 = an indicator set to 1 if Ei,t is negative, otherwise 0;

ΔEi,t = change in operating income after depreciation for firm i from t-1 to t

scaled by market value of equity at the end of year t;

D2 = an indicator set to 1 if ΔEi,t is negative, otherwise 0;

ΔBVi,t-1 = the change in book value of equity for firm i from year t-2 to year t-1

scaled by market value of equity at the end of year t-1;

D3 = an indicator set to 1 if ΔBVi,t-1 is negative, otherwise 0;

ΔDi,t = change in dividends for firm i from year t-1 to year t scaled by market

value of equity at the end of year t-1;

BMi,t = book value of equity divided by market value of equity for firm i in

year t;

ΔRDCAPi,t = change in R&D capital R&D capital is calculated as (R&D expense in

year t * 0.9 + R&D expense in year t-1 * 0.7 + R&D expense in year t-2 * 0.5 + R&D expense in year t-3 * 0.3 + R&D expense in year t-4 * 0.1) scaled by market value of equity at the end of year t-1;

RD_EXPi,t = R&D expense in year t

I also use an alternative measure of future firm performance ROAi,t+1 is defined as

operating income after depreciation for firm i in t+1 scaled by total assets at the end of year t:

ROAi,t+1 = α0 + α1 PATENTi,t +α2 Ei,t + α3 (D1 * Ei,t) + α4 ΔEi,t + α5 (D2 * ΔEi,t)

+ α6 ΔBVi,t-1 + α7 (D3 * ΔBVi,t-1) + α8 ΔDi,t + α9 BMi,t + α10 ΔRDCAPi,t + α10 RD_EXPi,t + εi,t

GM%i,t+1 = α0 + α1 PATENTi,t +α2 Ei,t + α3 (D1 * Ei,t) + α4 ΔEi,t + α5 (D2 * ΔEi,t)

+ α6 ΔBVi,t-1 + α7 (D3 * ΔBVi,t-1) + α8 ΔDi,t + α9 BMi,t + α10 ΔRDCAPi,t + α10 RD_EXPi,t + εi,t

where

GM%i,t+1 = sales minus cost of goods sold divided by sales for firm i in t+1;

All other variables are as defined above

In order to maximize the number of observations, I extend the sample period back to 1988

because these tests do not include analyst characteristics variables

4.2 Patent Information, Forecast Revisions, and Forecast Errors

Next, I test whether patent information is associated with analyst forecast revisions and forecast errors For analyst forecast revisions, I follow Cao et al (2011):

FR i,t+1= α0 + α1 PATENTi,t +α2 Ei,t + α3 (D1 * Ei,t) + α4 ΔEi,t + α5 (D2 * ΔEi,t)

+ α6 ΔBVi,t-1 + α7 (D3 * ΔBVi,t-1) + α8 ΔDi,t + α9 BMi,t + α10 ΔRDCAPi,t + α10 RD_EXPi,t + εi,t

where

FRi,t+1 = the earnings forecast revision measure calculated as earnings forecast for t+1

for firm i following the announcement of year t earnings minus the earnings forecast for t+1 for firm i following the announcement of year t-1 earnings scaled

by the absolute value of the latter;

All other variables are as defined above

I expect the coefficient on PATENT to be statistically significant if patent information is

informative to analysts in forecasting and revising future earnings estimates A positive (negative) and significant coefficient means analysts revise their earnings forecasts optimistically

(pessimistically) with patent information A statistically insignificant coefficient means that analysts do not find patent information to be informative or relevant in revising their forecasts

Next, I use the model from Amir et al (2003) to test for an association between patent information and analyst forecast errors Following Amir et al (2003), I use the absolute value of forecast errors (forecast accuracy) and signed forecast errors as my dependent variables Larger absolute values of forecast errors represent less accurate forecasts, while small absolute values represent more accurate forecasts More negative signed forecast errors represent more optimistic earnings forecasts, while more positive signed forecast errors represent more pessimistic

earnings forecasts

Abs(FEi,t+1) = α0 + α1 PATENTi,t + α2 R&DIntensityi,t + α3 SIZEi,t + α4 CVERi,t

+ α5 ln(AGE i,t )+ εi,t

[5]

FE i,t+1 = α0 + α1 PATENTi,t + α2 R&DIntensityi,t + α3 SIZEi,t + α4 CVERi,t

+ α5 ln(AGE i,t )+ εi,t

[6] where

Abs(FE i,t+1) = forecast accuracy calculated as the actual t+1 earnings minus the

earnings forecast for year t+1 earnings most immediately after the year t

earnings announcement scaled by the stock price at the year t fiscal year end;

FE i,t+1 = signed forecast errors calculated as the actual t+1 earnings minus the

earnings forecast for year t+1 earnings most immediately after the year t earnings announcement scaled by the stock price at the year t fiscal year end;

RDIntensityi,t = the R&D intensity calculated as R&D capital divided by the sum of

book value of equity and R&D capital R&D capital is calculated as (R&D expense in year t * 0.9 + R&D expense in year t-1 * 0.7 + R&D expense

in year 2 * 0.5 + R&D expense in year 3 *0.3 + R&D expense in year

t-4 * 0.1);

SIZEi,t = the natural log of market value of equity;

CVERi,t = the coefficient of variation for earnings calculated as the standard error

of the past five annual earnings divided by the absolute value of mean earnings;

Ln(AGEi,t) = firm age calculated as the natural log of the number of continuous year

observations on Compustat

If analysts misunderstand (understand) the positive future firm performance implications

of patent information, I expect the coefficient on PATENT to be positive (negative) and

significant for Equation [5] For Equation [6], I expect the coefficient on PATENT to be negative (positive) and significant if analysts are optimistic (pessimistic) about patent information

4.3 Main Tests: Analyst Experience and the Relation between Patents and Forecast Errors

To test my main hypotheses, I follow the Drake and Myers (2011) model I rank all continuous independent variables into deciles and standardize them to be between 0 and 1

(denoted by a superscript R), consistent with prior studies (Bradshaw et al., 2001; Collins et al., 2003; Drake and Myers, 2011) I start with the forecast error variables as dependent variables

My independent variables are patent measure, analyst firm-specific experience and the

interaction term between the two variables:

Abs(FE i,j,t+1) or FE i,j,t+1 = α0 + α1RPatenti,t + α2RPatenti,j,t * α3RFexpi,j,t + α3RFexpi,j,t

where

Abs(FE i,j,t+1) = absolute value of the actual t+1 earnings minus the earnings forecast for

year t+1 earnings most immediately after the year t earnings announcement for firm i and analyst j scaled by the stock price at the year

t fiscal year end;

FE i,j,t+1 = actual t+1 earnings minus the earnings forecast for year t+1 earnings

most immediately after the year t earnings announcement for firm i and analyst j scaled by the stock price at the year t fiscal year end;

Patenti,t = One of six patent measures as defined previously;

Fexpi,j,t = number of years analyst j has issued earnings forecasts for firm i in

I/B/E/S

Next, I add control variables for financial information:

Abs(FE i,j,t+1) or FE i,j,t+1 = α0 + α1RPatenti,t + α2RPatenti,t * α3RFexpi,j,t + α3RFexpi,j,t

+ α4RWCaccri,t + α5 RWCcfoi,t + α6R Abs(FE i,j,t) or FE i,j,t + α7 Lossi,t + α8 EquityOffi,t + α9RBTMi,t + α10RRecomi,t + α11RReti,t + α12RNumest + α13RSizei,t + α14RAgei,t + ε i,t

[8] where

WCaccri,t = working capital accruals calculated as Increase in A/R + Increase in

Inventory + Decrease in A/P and Accrued Liabilities + Decrease in Accrued Income Taxes + Increase (Decrease) in Other Assets (Liabilities), scaled by average assets;

WCcfo i,t = working capital cash flow calculated as Earnings before Interest, Taxes,

Depreciation, and Amortization minus working capital accruals, scaled by average total assets;

Abs(FE i,j,t) or FE i,j,t = the lag of either Abs(FE i,j,t+1) or FE i,j,t+1;

Lossi,t = 1 if Income before Extraordinary Items is less than 0, otherwise 0;

EquityOffi,t = equity offering indicator set to 1 if sales of common and preferred stock

are greater than purchases of common and preferred stock by more than 5%

of total assets, otherwise 0;

BTMi,t = the book-to-market ratio;

Recomi,t = the analyst's outstanding recommendation for the firm at the time of

earnings forecast from I/B/E/S;

Reti,t = 12-month buy-and-hold stock return up to the month prior to earnings

announcement;

Numesti,t = the number of analysts following the firm as of the earnings

announcement month in I/B/E/S;

Sizei,t = total assets;

Agei,t = firm age measured as the number of consecutive years to date the firm

appears in the Compustat Annual Database

Lastly, I control for other analyst characteristics such as portfolio complexity, brokerage size, and forecast horizon I also include industry and year dummies:

Abs(FE i,j,t+1) or FE i,j,t+1 = α0 + α1RPatenti,t + α2RPatenti,t * α3RFexpi,j,t

+ α4RPatenti,t * RBsizei,j,t + α5RPatenti,t * RNoFirmsi,j,t + α6RPatenti,t * RNoSIC2i,j,t + α7RPatenti,t * RFhori,j,t + α8RFexpi,j,t+ α9RBsizei,j,t + α10RNoFirmsi,j,t + α11RNoSIC2i,j,t + α12RFhori,j,t + α13RWCaccri,t + α14 RWCcfoi,t + α15R Abs(FE i,j,t)/FE i,j,t

+ α16 Lossi,t + α17 EquityOffi,t + α18RBTMi,t + α19RRecomi,t + α20R

Reti,t + α21RNumesti,t + α22RSizei,t + α24RAge i,t + Industry_dummies + Year_dummies +ε i,j,t [9] where

Bsizei,j,t = brokerage size measured as the number of analysts belonging to a

brokerage firm in year t in I/B/E/S;

NoFirmsi,j,t = the number of firms followed by analyst j in year t;

NoSIC2i,j,t = the number of industries (2-digit SIC) followed by analyst j in year t;

Fhori,j,t = the forecast horizon measured as the number of days between the

forecast issuance date and the fiscal year end date for year t+1

My main variable of interest is the interaction term Patent*Fexp If more experienced analysts make more accurate (more optimistic) forecasts by incorporating patent information, I expect the

coefficient to be negative and significant when the dependent variable is the absolute (signed) value of the forecast error

5 RESULTS

5.1 Descriptive Statistics

Table 1 shows the descriptive statistics The mean of Fexp is 4.03, which shows that analysts, on average, have firm-specific experience of 4.03 years The three patent measures have means just below 2 and medians around 0.5 Considering that I scale these measures by the industry averages of patent citations, this indicates that there are firms in certain industries with many more patent citations than other firms in the industry, and the distribution is somewhat skewed The statistics for analyst characteristics and other control variables are similar to those

of Drake and Myers (2011)

[Insert Table 1]

5.2 Patent Information and Future Firm Performance

Table 2 shows the results from estimating the model, which tests for a relation between patents and future firm performance Panel A of Table 2 reveals that the coefficients on all three patent measures are positively and significantly associated with future (year t+1) earnings at 1% significance level This result suggests that, even after controlling for R&D expenses and R&D capital, information about patent citations is informative about future firm performance In addition, Panel B shows that this association holds when I use t+2 earnings as the dependent variable

[Insert Table 2]

Additional tests using the future return on assets (ROA) as an alternative measure of future firm performance show that the patent measures are positively and significantly associated with year t+1 and t+2 ROAs at the 1% significance level (Panels C and D) The results in Table

2 are consistent with the positive relation between patent citations and future earnings

documented by Gu (2005)

In exploratory analyses, I also use the gross margin percentage (GM%) as a dependent variable If patents give firms a first-mover advantage, I expect firms with more patents and patent citations to have larger gross margin percentages, because sales revenues are larger due to higher prices or because the costs of goods sold are smaller due to cost-saving processes In Table 3, I find that the patent measures are also positively associated with the future gross

margin percentage in year t+1 at 1% significance level

[Insert Table 3]

5.3 Patent Information, Forecast Revisions, and Forecast Errors

Table 4 reveals that the patent measures are positively and significantly associated with analyst forecast revisions This suggests that analysts use patent information to increase their forecast earnings One interpretation is that the analysts understand that patents lead to better future firm performance, and thus revise their earnings forecasts upwards Another interpretation

is that analysts view the patent citations as a signal of economic importance or as a signal of potential royalty income

When I use a dependent variable that compares forecasted earnings to actual earnings in absolute value, I find that the coefficients on the patent measures are positive and significant This suggests that patent information is associated with less accurate forecasts even after

controlling for R&D intensity (Table 5, Panel A) One explanation is that even though patents

and patent citations are positively associated with future earnings, patent information is difficult

to incorporate into earnings forecasts I do not find any significant coefficients on patent

measures when I use signed forecast errors as the dependent variable (Table 5, Panel B)

[Insert Tables 4 and 5]

5.4 Analyst Experience and the Relation between Patent Information and Forecast Errors

Table 6 presents the results from tests of my main hypotheses The coefficient on the interaction between the patent measure and analyst firm-specific experience (Patent*Fexp) is the variable of interest In Panel A, the dependent variable is forecast accuracy, calculated as the absolute value of forecasted EPS minus actual EPS, scaled by stock price A larger (smaller) value of Abs(FE i,t+1) represents a less (more) accurate forecast I expect a negative coefficient

on Patent*Fexp if more experienced analysts are better at incorporating patent information to make more accurate earnings forecasts Panel A of Table 6 reveals that the coefficient of

Patent*Fexp is not statistically significant Thus, I do not find evidence that more experienced analysts are better at making more accurate earnings forecasts with patent information

Furthermore, the coefficient on Fexp is negative and significant, confirming the findings of prior studies, which find that analysts with more experience make more accurate earnings forecasts

[Insert Table 6]

Next, I use signed forecast error as the dependent variable More negative (positive)

FEi,t+1 means more (less) optimistic earnings forecasts (Table 6, Panel B) The coefficient on Patent*Fexp is again my variable of interest Given the previous finding that patent citations lead

to better future earnings, if more experienced analysts make more optimistic earnings forecasts using patent information, I expect the coefficient on Patent*Fexp to be negative and significant Panel B of Table 6 reveals that the coefficient on Patent*Fexp is negative and statistically

significant This suggests that more experienced analysts better understand the implications and benefits of patents so they make more optimistic earnings forecasts Prior literature shows that analysts are generally optimistically biased (Bradshaw et al., 2001; Drake and Myers, 2011), which is confirmed by the negative and significant intercepts Overall, Panel B of Table 6 seems

to show that more experienced analysts make more optimistic earnings forecasts using patent information

The overall results of Table 6 suggest that analysts with more experience are not better at making more accurate earnings forecasts by incorporating patent information However, they do make more optimistic earnings forecasts, possibly from understanding that patents lead to better future firm performance

6 ADDITIONAL TESTS

6.1 Alternative scaling for patent measures

As previously mentioned in Section 3, I scale my patent measures by the industry-year median values There is a possibility that my patent measures are merely proxies for firm size because larger firms tend to have more patents In order to mitigate this concern, I divide the firms of each industry by the median size (total assets) into large and small firms I then calculate the alternative industry medians by industry, year, and size The results of my main tests using patent measures with alternative scaling are presented in Table 7

[Insert Table 7]

As previously predicted for the main test, if more experienced analysts make more

accurate earnings forecasts using patent information, I expect the coefficient on Patent*Fexp to

be negative and significant Panel A of Table 7 presents that the coefficient on Patent*Fexp is

not statistically significant This means that using the alternative scaling for my patent measures the accuracy of analyst earnings forecasts related to patent information is not better for more experienced analysts Panel B of Table 7 presents the results of a test with signed forecast errors

as the dependent variable The coefficient on Patent*Fexp is negative and significant for two out

of three patent measures, which is somewhat consistent with the results of my main tests The value for the second patent measure is just outside of the 0.10 significant level at 0.11 Overall, the results of the test using the alternative scalar for my patent measures (Table 7) are consistent with the results of my main results (Table 6)

P-6.2 Other Types of Experience

In addition to firm-specific experience, prior literature examines other measures of

analyst experience such as general experience (Mikhail et al., 1997, 2003; Clement, 1999; Drake and Myers, 2011) and task-specific experience (Clement et al., 2007) I examine whether these two measures of analyst experience as well as industry-specific experience are correlated omitted variables General experience measures the number of years an analyst has made earnings

forecasts for any firm Next, understanding patents may be industry specific Prior literature generally does not focus on industry-specific experience, and I measure the industry-specific experience as the number of years an analyst has made earnings forecasts for a specific industry (3-digit SIC) For task-specific experience, Clement et al (2007) examine whether analysts, who have experience on forecasting earnings for firms that undergo mergers and acquisitions,

subsequently make more accurate earnings forecasts for firms that undergo mergers and

acquisitions Although patents are common in certain industries, I explore the possibility that using and understanding patents is a specific task for analysts I measure the task-specific

experience as the number of years an analyst has made earnings forecasts for firms with patents

Panel A and Panel B of Table 8 present the results of tests regressing forecast accuracy and signed forecast errors, respectively, on general experience (Gexp), where Gexp is measured

as the number of years to date that an analyst has issued earnings forecasts in I/B/E/S Panel A of Table 8 shows that the coefficient on Patent*Gexp is not statistically significant, which means that analysts with more general experience do not make more accurate earnings forecasts related

to patent information Panel B of Table 8 shows that the coefficient on Patent*Gexp is negative and significant for one out of three patent measures The P-values of the other two measures are insignificant at 0.111 and 0.142 Thus, tests using general experience instead of firm-specific experience find limited results that more experienced analysts make more optimistic earnings forecasts related to patent information

Next, it is possible that experience in a specific industry gives analysts a better

understanding of patent information I measure industry-specific experience (Industryexp) as the number of years an analyst has issued earnings forecasts for a particular 3-digit SIC code

industry Panel C of Table 8 reveals that the coefficient of Patent*Industryexp is not statistically significant, meaning analysts with more industry-specific experience do make more accurate earnings forecasts related to patent information For signed forecast errors, the coefficient of Patent*Industryexp is statistically significant for two out of three patent measures

Similarly, Panel E of Table 8 shows that analysts with more task-specific experience (Texp),11 where Texp is measured as the number of years in which the analyst has issued

earnings forecasts for a firm with patents, do not make more accurate earnings forecasts related

to patent information Panel F of Table 8 shows that analysts with more task-specific experience are more optimistic in their earnings forecasts related to patent information for all three patent measures

Overall, the results of Panels A through F of Table 8 are consistent with my main results that more experienced analysts do not make more accurate earnings forecasts related to patent information However, they do make more optimistic earnings forecasts related to patent

information, although the effect is smaller for general experience than other experience measures

[Insert Table 9]

Table 9 presents the results of additional tests with both the firm-specific experience variable and one of three (general, industry-specific, and task-specific) experience variables in the model Panels A, C, and E of Table 9 show that the coefficients on Patent*Fexp and on the interaction term between Patent and alternative experience measures are not statistically

significant This is consistent with the results of my main test that more experienced analysts do not make more accurate earnings forecasts related to patent information Panel B of Table 9

11

When I use an alternative measure of task-specific experience, measured as the number of

shows that the coefficient of the interaction term Patent*Fexp is negative and significant for two out of three patent measures while the coefficient of Patent*Gexp is not significant, showing that firm-specific experience is the driving measure of experience However, Panels D and F show that the coefficient on Patent*Fexp is not significant and the P-values are just above 10%

significance level One possible explanation for why the coefficient on Patent*Fexp is not

significant is that industry-specific and task-specific experience measures are highly correlated to firm-specific experience measures, and that the multicollinearity introduces the decrease in the significance level

6.3 R&D Capital

Another omitted correlated variable may be information about R&D expenses in the past Lev and Sougiannis (1996) find that past R&D expenses are value relevant, on average, for five years Subsequent studies including Amir, Lev, and Sougiannis (2003) construct an R&D capital measure by summing R&D expenses from the previous five years, assuming a five-year,

straight-line depreciation I include their R&D capital measure to investigate whether the patent measures are simply proxies for R&D capital I calculate R&D capital as:

RD_CAP = (R&D expense in year t * 0.9 + R&D expense in year t-1 * 0.7

+ R&D expense in year t-2 * 0.5 + R&D expense in year t-3 * 0.3 + R&D expense in year t-4 * 0.1) / market value of equity at the end of year t-1

I calculate the Chg_RD_CAP by subtracting RD_CAPt-1 from RD_CAPt

[Insert Table 10]

Table 10 presents the results of my main tests controlling for the change in R&D capital Panel A of Table 10 reveals that the coefficients on Patent*Fexp and Chg_RD_CAP*Fexp are not statistically significant Panel B of Table 12 reveals that the coefficient on Patent*Fexp is

negative and significant, while the coefficient on Chg_RD_CAP*Fexp is not statistically

significant The results suggest that, even after controlling for the Lev and Sougiannis R&D capital measure, analysts with more experience make more optimistic earnings forecasts related

to patent information

6.4 Regulation Fair Disclosure

Prior literature suggests that one mechanism by which more experienced analysts

perform better is building personal relationships with managers and gaining private information (Clement, 1999; Drake and Myers, 2012) In order to prohibit managers from disclosing private information to selective people, the Securities and Exchange Commission (SEC) implemented Regulation Fair Disclosure (Reg FD), which effectively requires that any disclosure managers make to be available to the general public Prior studies find that the private information

disclosed by managers decreased after the implementation of Reg FD in October 2000 (Drake and Myers, 2012; Chen and Matsumoto, 2006; Gintschel and Markov, 2004) As an additional test, I test whether there is a difference in the effect of analyst experience on understanding patents between pre- and post-Reg FD This is to investigate whether my measure of analyst experience is a measure of the ability to gather private information

understanding patent information to make earnings forecasts The coefficient on Patent *

REG_FD * Fexp is not statistically significant, suggesting that Reg FD did not change whether analysts’ understanding of patents improves with experience

6.5 Forecasting Horizons

I also investigate whether there is a difference in earnings forecasts depending on forecast horizons Understanding the impact of patents may vary depending on when analysts make the earnings forecasts Richardson et al (2004) finds that earnings forecasts are generally more optimistic when made earlier Bradshaw et al (2001) and Drake and Myers (2012) find that analyst accruals-related optimism is more pronounced for earnings forecasts made early I

investigate whether earnings forecasts made early are driving the more optimistic earnings

forecasts related to patent information

[Insert Table 12]

I split my sample into three subsamples according to the number of days between the dates of earnings forecasts and the year-end dates of fiscal years for which the earnings forecasts are made: (1) less than 6 months (latest), (2) 6 to 9 months, and (3) more than 9 months (earliest) Table 12 shows the results of tests for different forecast horizon subsamples Panels A, C, and E

of Table 12 show that the coefficient on Patent * Fexp is not statistically significant, suggesting that regardless of forecast horizons, analysts with more experience do not make more accurate earnings forecasts related to patent information Panel B of Table 12 shows that the coefficient

on Patent * Fexp is not statistically significant and the P-values are quite high at 0.651, 0.696, and 0.525 – well outside of 10% significance level This suggests that analysts with more

experience are not more optimistic about patent information when they make the forecasts later

in the year closer to the fiscal year-end dates Panel D of Table 12 shows the results for the next subsample of forecast horizons between 6 and 9 months The coefficient on Patent * Fexp is still

not statistically significant, but the P-values are lower than in Panel B at 0.274, 0.258, and 0.356 Finally, Panel F of Table 12 shows the results for the subsample of earnings forecasts made the earliest between 9 to 12 months before the fiscal year end The coefficient on Patent* Fexp is negative and significant with P-values of 0.035, 0.023, and 0.057 These results suggest that early earnings forecasts are driving the main results, which suggest that analysts with more experience make more optimistic earnings forecasts related to patent information

[Insert Table 13]

Furthermore, I explore forecast horizons longer than 12 months I use two more sets of samples, one for forecasts made between 12 and 24 months and the other for forecasts made more than 24 months before fiscal year-end dates These are not subsamples of my main sample, but are different sets of samples of forecasts made during the same period for years t+2 and t+3 earnings I use the I/B/E/S analyst earnings forecasts for year t+2 earnings For year t+3 earnings

I use the long-term growth rate to calculate the earnings forecasts for year t+3 because analysts

do not make t+3 earnings forecasts often

Panels A and C of Table 13 show that more experienced analysts do not make more accurate earnings forecasts related to patent information for future earnings in years t+2 and t+3 Panel B of Table 13 shows that the coefficient on Patent * Fexp is negative and significant, which is consistent with the results of my main tests Panel D of Table 13, however, shows that the coefficient on Patent * Fexp is not statistically significant Overall, the results of Tables 12 and 13 show that analysts with more experience make more optimistic earnings forecasts related

to patent information when they make the forecasts early (9 to 12 months and 12 to 24 months), but once the forecast horizon stretches beyond 24 months, the effect disappears

6.6 Industry Effects

Although I include all industries with patents, there may be a cross-sectional difference among industries in understanding patent information to make earnings forecasts Following Amir et al (2003) and Gu et al (2004), I take a closer look at specific industries, in which

patents are more important and prevalent: chemicals and pharmaceuticals (SIC 28), machine manufacturing (SIC 35), electronics manufacturing (SIC 36), software (SIC 73), and engineering services (SIC 87)

[Insert Table 14]

Consistent with previous results, the coefficient on Patent*Fexp is not statistically

significant in most of the tests with forecast accuracy as the dependent variable (Panels A, C, E, and G of Table 14) The exception is in engineering services (Panel I) where the coefficient on Patent*Fexp is negative and significant for two of the three patent measures This suggests that

in this particular industry, analysts with more experience make more accurate earnings forecasts related to patent information For signed forecast errors, analysts with more experience make more optimistic earnings forecasts related to patent information in machine manufacturing (Panel D) and software (Panel H)

6.7 Truncating the Sample Period

The patent data on NBER Patent Data Project are available through 2006 Patents granted

in the later years naturally do not have citations received beyond 2006 Therefore, I truncate my main sample to end in 2005 My study adds to prior studies that use patent data because their sample years end much earlier (Hirschey et al (2001) use a sample period of 1986 through 1995;

Gu (2005), 1983 through 1999; Gu and Wang (2005), 1981 through 1998; and Pandit et al (2011), 1972 through 2000) There is a possibility that it is more appropriate to truncate more

than 1 year My additional test uses a sample that truncates the last five years, making the sample period from 1994 to 2001 One drawback of truncating the last five years is that it reduces the number of observations in the sample by a significant amount because the number of patents increased dramatically in the 2000s

Panel A of Table 15 shows that, with a truncated sample, the coefficient on Patent*Fexp

is not statistically significant, suggesting that more experienced analysts do not make more accurate earnings forecasts related to patent information The coefficient on Patent*Fexp is not statistically significant (p-values of 0.154, 0.153, and 0.259) in Panel B of Table 15 as well, meaning analysts with more experience are not more optimistic about patent information in this sample period A possible explanation for why the effect of analyst experience and patent

information on signed forecast errors disappears may be the reduction in sample size Because patents became more prevalent in the 2000s, when I truncate the sample to exclude observations beyond 2001, I lose more than half of my observations from 36,279 analyst-firm-year

observations to 15,935 analyst-firm-year observations

7 CONCLUSION

Extant literature using patent data finds that important patents are associated with firm value (Hirschey et al., 2001; Hall et al., 2005) by documenting the positive relation between patent citations and Tobin’s q Other studies find a positive relation between patent citations and future firm performance (Gu, 2005; Pandit et al., 2011) I confirm that firms with more patent citations have better future firm performance proxied by future earnings and future ROA I also find that patent citations lead to better gross margin percentages Next, I confirm that patent information is associated with less accurate analyst earnings forecasts This is consistent with the

findings in Gu and Wang (2005), that the absolute value of analyst forecast errors is greater for firms with more innovative technologies This is intuitive because understanding the potential impact of new technologies can be difficult Finally, my main test examines whether analysts with more experience have better understanding of patent information to make more accurate and optimistic earnings forecasts given that firms with more patent citations have better future performance I find that analysts with more experience are not better at understanding patent information to make more accurate earnings forecasts However, they seem to make more

optimistic earnings forecasts, possibly from an understanding that patents lead to better future firm performance

This finding can be relevant to regulators and standard setters The main reason behind the full expensing treatment of R&D costs under U.S GAAP is that the future implications of R&D activities are inherently difficult to predict My findings suggest that publicly available information about the output of R&D activities is difficult to accurately incorporate into earnings forecasts, even for sophisticated market participants such as experienced analysts However, these analysts do seem to understand patent information enough to make more optimistic

earnings forecasts In light of this finding, regulators and standard setters may consider requiring firms to disclose R&D output information relevant to future firm performance to a broader range

of market participants Additionally, this finding contributes to the analyst forecast literature by finding a setting where analysts are unable to understand and incorporate available information

to more accurate earnings forecasts It also contribute to the analyst optimism literature by finding that more experienced analysts are more optimistic about patents than less experienced analysts