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 ScholarWorks@UARK 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 Follow this and additional works at: http://scholarworks.uark.edu/etd Part of the Accounting Commons, and the Finance and Financial Management Commons Recommended Citation 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 This Dissertation is brought to you for free and open access by ScholarWorks@UARK It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of ScholarWorks@UARK For more information, please contact scholar@uark.edu, ccmiddle@uark.edu 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 Committee Member Dr Junhee Han Committee Member Dr Vernon Richardson Committee Member 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 INTRODUCTION BACKGROUNDS AND HYPOTHESIS DEVELOPMENT 2.1 Patents 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 2.5 Analyst Experience and the Relation between Patents Information and Forecast Errors SAMPLE SELECTION AND DATA 11 3.1 Sample Selection 11 3.2 Patent Measures .11 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 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 Errors22 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 CONCLUSION 32 REFERENCES 35 APPENDIX 39 10 TABLES 41 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 This is similar to academic studies citing previous studies The methodology section provides more detailed descriptions of my patent measures and tests 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 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 not fully incorporate all of the available information about patents when making In additional analyses, I explore the impact of general experience, industry-specific experience, and task-specific experience on analysts’ understanding of the information in patent citations (0.017) (0.017) (0.016) Size ? -0.002 -0.002 -0.002 (0.564) (0.423) (0.413) R Age ? -0.004* -0.004* -0.004* (0.058) (0.051) (0.054) Industry Fixed Effects YES YES YES Year Fixed Effects YES YES YES N 5,532 5,532 5,532 Adjusted R2 0.246 0.246 0.247 ***, **, and * indicate significance at 1, 5, 10%, respectively The p values are based on onetailed (two-tailed) t tests if there is (is not) a prediction on the sign of the coefficient See appendix for variable definitions R 125 Table 14 (continued) Coefficient estimates from OLS regressions of signed forecast errors on patent measures and analyst experience (SIC 36) Panel F: Dependent Variable = FEt+1 Prediction [1] [2] [3] Intercept -0.021** -0.020*** -0.021** (0.011) (0.009) (0.010) R Patent ? 0.000 -0.001 0.000 (0.934) (0.898) (0.972) R Patent * RFexp -0.001 -0.003 -0.003 (0.402) (0.204) (0.231) R Patent * RBsize ? -0.002 -0.003 -0.004 (0.586) (0.285) (0.293) R Patent * RNoFirms ? -0.004 0.001 -0.002 (0.343) (0.873) (0.592) R Patent * RNoSIC2 ? 0.002 0.001 0.001 (0.646) (0.776) (0.824) R Patent * RFhor ? 0.010** 0.008** 0.008* (0.028) (0.045) (0.056) R Fexp ? -0.001 0.000 0.000 (0.769) (0.852) (0.894) R Bsize ? 0.004* 0.005** 0.005** (0.087) (0.024) (0.028) R NoFirms ? 0.002 -0.001 0.001 (0.567) (0.789) (0.772) R NoSIC2 ? -0.002 -0.001 -0.001 (0.595) (0.665) (0.691) R Fhor ? -0.007** -0.006* -0.006* (0.034) (0.051) (0.057) R WCaccr -0.004** -0.004** -0.004** (0.049) (0.046) (0.045) R WCcfo + -0.000 -0.000 -0.000 (0.527) (0.528) (0.528) R Ferrort + 0.004** 0.004** 0.004** (0.040) (0.040) (0.041) Loss 0.003 0.002 0.002 (0.821) (0.816) (0.819) EquityOff 0.002 0.002 0.002 (0.691) (0.703) (0.713) R BTM -0.004 -0.005 -0.004 (0.138) (0.116) (0.122) R Recom + -0.002 -0.002 -0.002 (0.983) (0.986) (0.986) R Ret + 0.004* 0.004* 0.004* (0.055) (0.065) (0.061) R Numest ? 0.007* 0.007* 0.007** 126 (0.055) (0.050) (0.049) Size ? -0.002 -0.001 -0.001 (0.567) (0.805) (0.837) R Age ? 0.005* 0.005* 0.006** (0.071) (0.059) (0.048) Industry Fixed Effects YES YES YES Year Fixed Effects YES YES YES N 5,532 5,532 5,532 Adjusted R2 0.106 0.104 0.104 ***, **, and * indicate significance at 1, 5, 10%, respectively The p values are based on onetailed (two-tailed) t tests if there is (is not) a prediction on the sign of the coefficient See appendix for variable definitions R 127 Table 14 Coefficient estimates from OLS regressions of forecast accuracy for future earnings on patent measures and analyst experience (SIC 73) Panel G: Dependent Variable = Abs(FEt+1) Prediction [1] [2] [3] Intercept ? 0.017*** 0.018*** 0.016*** (0.002) (0.001) (0.001) R Patent ? 0.002 0.001 0.006* (0.623) (0.825) (0.076) R Patent * RFexp 0.001 0.002 0.001 (0.721) (0.793) (0.673) R Patent * RBsize ? 0.002 0.000 0.003 (0.395) (0.876) (0.238) R Patent * RNoFirms ? 0.002 0.003* 0.003* (0.293) (0.068) (0.085) R Patent * RNoSIC2 ? -0.005 -0.005 -0.010*** (0.270) (0.282) (0.004) R Patent * RFhor ? -0.002 0.001 -0.002 (0.548) (0.651) (0.398) R Fexp -0.001 -0.002 -0.001 (0.199) (0.133) (0.235) R Bsize ? -0.002 -0.001 -0.002 (0.242) (0.580) (0.152) R NoFirms ? -0.002* -0.003** -0.003** (0.052) (0.014) (0.018) R NoSIC2 ? 0.002 0.002 0.004** (0.510) (0.528) (0.024) R Fhor ? 0.003 0.001 0.003 (0.221) (0.639) (0.128) R WCaccr ? 0.000 0.000 0.000 (0.843) (0.818) (0.723) R WCcfo ? -0.004 -0.004 -0.004 (0.162) (0.118) (0.142) R Abs(Ferrort) ? 0.012*** 0.012*** 0.011*** (0.000) (0.000) (0.000) Loss ? 0.003 0.003 0.003 (0.143) (0.199) (0.193) EquityOff ? 0.005* 0.005* 0.005* (0.060) (0.061) (0.057) R BTM ? 0.003 0.003 0.003 (0.476) (0.453) (0.374) R Recom ? -0.003*** -0.003*** -0.003*** (0.003) (0.004) (0.004) R Ret ? -0.007** -0.007** -0.007** (0.028) (0.032) (0.035) R Numest ? -0.005** -0.005** -0.005** 128 (0.031) (0.031) (0.023) Size ? -0.001 -0.001 -0.002 (0.806) (0.616) (0.400) R Age ? -0.002 -0.002 -0.002 (0.363) (0.347) (0.279) Industry Fixed Effects YES YES YES Year Fixed Effects YES YES YES N 4,999 4,999 4,999 Adjusted R2 0.237 0.238 0.243 ***, **, and * indicate significance at 1, 5, 10%, respectively The p values are based on onetailed (two-tailed) t tests if there is (is not) a prediction on the sign of the coefficient See appendix for variable definitions R 129 Table 14 (continued) Coefficient estimates from OLS regressions of signed forecast errors on patent measures and analyst experience (SIC 73) Panel H: Dependent Variable = FEt+1 Prediction [1] [2] [3] Intercept -0.021*** -0.021*** -0.020*** (0.004) (0.004) (0.003) R Patent ? 0.007 0.006 0.002 (0.193) (0.243) (0.605) R Patent * RFexp -0.006** -0.006** -0.005** (0.027) (0.020) (0.031) R R Patent * Bsize ? -0.004 -0.001 -0.004 (0.222) (0.718) (0.183) R Patent * RNoFirms ? -0.005** -0.006*** -0.006** (0.022) (0.009) (0.010) R Patent * RNoSIC2 ? 0.004 0.004 0.008* (0.499) (0.467) (0.064) R Patent * RFhor ? -0.002 -0.005 -0.002 (0.568) (0.210) (0.487) R Fexp ? 0.003 0.003* 0.003 (0.121) (0.065) (0.117) R Bsize ? 0.002 0.001 0.002 (0.196) (0.636) (0.176) R NoFirms ? 0.004*** 0.005*** 0.005*** (0.005) (0.004) (0.003) R NoSIC2 ? -0.003 -0.003 -0.005** (0.396) (0.371) (0.029) R Fhor ? 0.001 0.002 0.001 (0.758) (0.390) (0.646) R WCaccr -0.002 -0.002 -0.002 (0.136) (0.126) (0.105) R WCcfo + 0.002 0.002 0.002 (0.243) (0.230) (0.229) R Ferrort + 0.008*** 0.008*** 0.008*** (0.001) (0.001) (0.001) Loss 0.001 0.001 0.001 (0.582) (0.616) (0.654) EquityOff -0.006* -0.006* -0.006* (0.053) (0.052) (0.050) R BTM -0.002 -0.002 -0.002 (0.349) (0.325) (0.295) R Recom + 0.000 0.000 0.000 (0.368) (0.378) (0.366) R Ret + 0.003 0.003 0.003 (0.210) (0.228) (0.238) R Numest ? 0.004 0.003 0.004 130 (0.148) (0.151) (0.145) Size ? -0.002 -0.001 -0.000 (0.485) (0.767) (0.964) R Age ? 0.000 0.000 0.000 (0.935) (0.898) (0.837) Industry Fixed Effects YES YES YES Year Fixed Effects YES YES YES N 4,999 4,999 4,999 Adjusted R2 0.102 0.102 0.105 ***, **, and * indicate significance at 1, 5, 10%, respectively The p values are based on onetailed (two-tailed) t tests if there is (is not) a prediction on the sign of the coefficient See appendix for variable definitions R 131 Table 14 (continued) Coefficient estimates from OLS regressions of forecast accuracy for future earnings on patent measures and analyst experience (SIC 87) Panel I: Dependent Variable = Abs(FEt+1) Prediction [1] [2] [3] Intercept ? -0.046** -0.044** -0.057*** (0.037) (0.040) (0.009) R Patent ? 0.006 0.013 0.027 (0.817) (0.641) (0.330) R Patent * RFexp -0.040* -0.014 -0.046** (0.069) (0.275) (0.016) R R Patent * Bsize ? 0.059 0.040 0.034 (0.161) (0.274) (0.353) R Patent * RNoFirms ? -0.013 -0.019 -0.016 (0.762) (0.629) (0.683) R Patent * RNoSIC2 ? 0.038 0.040 0.034 (0.223) (0.289) (0.285) R Patent * RFhor ? -0.056* -0.095*** -0.064*** (0.050) (0.005) (0.008) R Fexp 0.025 0.013 0.028** (0.926) (0.831) (0.022) R Bsize ? -0.019 -0.010 -0.007 (0.324) (0.517) (0.635) R NoFirms ? -0.001 0.002 -0.001 (0.942) (0.917) (0.953) R NoSIC2 ? -0.010 -0.014 -0.009 (0.474) (0.403) (0.521) R Fhor ? 0.040** 0.063*** 0.044*** (0.022) (0.003) (0.004) R WCaccr ? 0.014 0.012 0.013 (0.225) (0.226) (0.207) R WCcfo ? -0.014 -0.009 -0.012 (0.201) (0.332) (0.160) R Abs(Ferrort) ? 0.006 0.008 0.007 (0.523) (0.322) (0.361) Loss ? 0.023** 0.021** 0.023** (0.032) (0.011) (0.022) EquityOff ? -0.011** -0.010** -0.012** (0.020) (0.040) (0.015) R BTM ? 0.008 0.011 0.010 (0.386) (0.230) (0.276) R Recom ? 0.001 -0.001 0.001 (0.833) (0.877) (0.788) R Ret ? 0.002 0.001 0.001 (0.744) (0.863) (0.909) R Numest ? 0.045** 0.036** 0.037** 132 (0.026) (0.025) (0.037) Size ? 0.018 0.015 0.027 (0.394) (0.504) (0.215) R Age ? -0.015 -0.016 -0.012 (0.210) (0.173) (0.322) Industry Fixed Effects YES YES YES Year Fixed Effects YES YES YES N 161 161 161 Adjusted R2 0.433 0.444 0.438 ***, **, and * indicate significance at 1, 5, 10%, respectively The p values are based on onetailed (two-tailed) t tests if there is (is not) a prediction on the sign of the coefficient See appendix for variable definitions R 133 Table 14 (continued) Coefficient estimates from OLS regressions of signed forecast errors on patent measures and analyst experience (SIC 87) Panel J: Dependent Variable = FEt+1 Prediction [1] [2] [3] Intercept 0.025 0.017 0.025 (0.823) (0.729) (0.840) R Patent ? -0.057 -0.028 -0.038 (0.251) (0.534) (0.298) R Patent * RFexp 0.056 0.037 0.078 (0.961) (0.834) (0.988) R Patent * RBsize ? -0.017 -0.026 -0.011 (0.797) (0.630) (0.815) R Patent * RNoFirms ? -0.002 -0.016 -0.018 (0.980) (0.759) (0.734) R Patent * RNoSIC2 ? -0.025 -0.032 -0.040 (0.632) (0.536) (0.313) R Patent * RFhor ? 0.080*** 0.124*** 0.069*** (0.007) (0.004) (0.001) R Fexp ? -0.028 -0.018 -0.038* (0.173) (0.427) (0.063) R Bsize ? 0.002 0.004 -0.003 (0.950) (0.892) (0.914) R NoFirms ? 0.005 0.013 0.015 (0.878) (0.635) (0.575) R NoSIC2 ? 0.007 0.015 0.017 (0.749) (0.528) (0.373) R Fhor ? -0.056*** -0.082*** -0.050*** (0.003) (0.003) (0.000) R WCaccr -0.027** -0.020** -0.023* (0.017) (0.020) (0.025) R WCcfo + 0.024** 0.015* 0.018** (0.013) (0.058) (0.031) R Ferrort + 0.021** 0.017** 0.019** (0.034) (0.040) (0.034) Loss -0.013 -0.014 -0.016 (0.174) (0.098) (0.109) EquityOff 0.017 0.016 0.018 (0.958) (0.975) (0.975) R BTM 0.004 -0.004 -0.001 (0.382) (0.377) (0.476) R Recom + -0.008 -0.006 -0.009 (0.815) (0.786) (0.855) R Ret + -0.019 -0.016 -0.016 (0.945) (0.907) (0.914) R Numest ? -0.027 -0.025 -0.018 134 R Size ? R Age ? (0.329) -0.020 (0.395) 0.004 (0.743) (0.302) -0.018 (0.485) 0.002 (0.857) (0.508) -0.037 (0.111) -0.003 (0.808) Industry Fixed YES YES YES Effects Year Fixed Effects YES YES YES N 161 161 161 Adjusted R2 0.271 0.281 0.283 ***, **, and * indicate significance at 1, 5, 10%, respectively The p values are based on onetailed (two-tailed) t tests if there is (is not) a prediction on the sign of the coefficient See appendix for variable definitions 135 Table 15 Coefficient estimates from OLS regressions of forecast accuracy for future earnings on patent measures and analyst experience (Truncated sample) Panel A: Dependent Variable = Abs(FEt+1) Prediction [1] [2] [3] Intercept ? 0.087*** 0.088*** 0.086*** (0.000) (0.000) (0.000) R Patent ? 0.000 -0.001 0.002 (0.938) (0.825) (0.382) R Patent * RFexp 0.003 0.004 0.003 (0.925) (0.960) (0.903) R Patent * RBsize ? -0.001 -0.001 -0.001 (0.642) (0.702) (0.520) R Patent * RNoFirms ? 0.003 0.004 0.005** (0.136) (0.103) (0.027) R Patent * RNoSIC2 ? -0.001 -0.002 -0.004 (0.676) (0.543) (0.169) R Patent * RFhor ? -0.004* -0.003 -0.004* (0.086) (0.159) (0.057) R Fexp -0.003** -0.003** -0.003** (0.025) (0.012) (0.029) R Bsize ? -0.001 -0.001 -0.001 (0.534) (0.509) (0.649) R NoFirms ? -0.001 -0.001 -0.002 (0.546) (0.461) (0.246) R NoSIC2 ? -0.002 -0.002 -0.001 (0.208) (0.249) (0.446) R Fhor ? 0.006*** 0.006*** 0.007*** (0.000) (0.000) (0.000) R WCaccr ? 0.004*** 0.004*** 0.004*** (0.000) (0.000) (0.000) R WCcfo ? -0.002 -0.002 -0.002 (0.243) (0.239) (0.225) R Abs(Ferrort) ? 0.013*** 0.013*** 0.013*** (0.000) (0.000) (0.000) Loss ? 0.003* 0.003* 0.003* (0.055) (0.056) (0.060) EquityOff ? 0.001 0.001 0.001 (0.427) (0.428) (0.426) R BTM ? 0.008*** 0.008*** 0.009*** (0.000) (0.000) (0.000) R Recom ? -0.001** -0.001** -0.001** (0.020) (0.019) (0.021) R Ret ? -0.009*** -0.009*** -0.009*** (0.000) (0.000) (0.000) R Numest ? -0.003* -0.003* -0.003* 136 R Size ? R Age ? (0.091) -0.001 (0.478) -0.003** (0.042) (0.087) -0.001 (0.509) -0.003** (0.040) (0.077) -0.002 (0.351) -0.003** (0.031) Industry Fixed YES YES YES Effects Year Fixed Effects YES YES YES N 15,935 15,935 15,935 Adjusted R2 0.235 0.235 0.236 ***, **, and * indicate significance at 1, 5, 10%, respectively The p values are based on onetailed (two-tailed) t tests if there is (is not) a prediction on the sign of the coefficient See appendix for variable definitions 137 Table 15 (continued) Coefficient estimates from OLS regressions of signed forecast errors on patent measures and analyst experience (Truncated sample) Panel B: Dependent Variable = FEt+1 Prediction [1] [2] [3] Intercept -0.108*** -0.108*** -0.107*** (0.000) (0.000) (0.000) R Patent ? 0.004 0.005 0.002 (0.194) (0.115) (0.466) R Patent * RFexp -0.003 -0.003 -0.002 (0.154) (0.153) (0.259) R Patent * RBsize ? -0.001 -0.003 -0.002 (0.510) (0.180) (0.321) R Patent * RNoFirms ? -0.003 -0.003 -0.004 (0.308) (0.326) (0.157) R Patent * RNoSIC2 ? -0.001 -0.000 0.002 (0.719) (0.973) (0.479) R Patent * RFhor ? 0.002 0.001 0.002 (0.433) (0.675) (0.403) R Fexp ? 0.001 0.001 0.001 (0.415) (0.393) (0.587) R Bsize ? 0.002 0.003** 0.002* (0.125) (0.042) (0.074) R NoFirms ? 0.002 0.002 0.002 (0.221) (0.228) (0.113) R NoSIC2 ? 0.001 0.001 -0.000 (0.526) (0.721) (0.823) R Fhor ? -0.004* -0.003* -0.004** (0.054) (0.098) (0.040) R WCaccr -0.006*** -0.006*** -0.006*** (0.000) (0.000) (0.000) R WCcfo + -0.000 -0.000 0.000 (0.512) (0.508) (0.483) R Ferrort + 0.004*** 0.004*** 0.004*** (0.001) (0.001) (0.001) Loss 0.000 0.000 0.000 (0.584) (0.586) (0.587) EquityOff -0.001 -0.001 -0.001 (0.286) (0.286) (0.289) R BTM -0.005** -0.005** -0.005** (0.010) (0.010) (0.010) R Recom + -0.000 -0.000 -0.000 (0.978) (0.994) (0.972) R Ret + 0.010*** 0.010*** 0.010*** (0.000) (0.000) (0.000) R Numest ? 0.005** 0.005** 0.005** 138 (0.046) (0.040) (0.039) Size ? -0.002 -0.002 -0.002 (0.458) (0.404) (0.539) R Age ? 0.005*** 0.005*** 0.005*** (0.003) (0.003) (0.002) Industry Fixed Effects YES YES YES Year Fixed Effects YES YES YES N 15,935 15,935 15,935 Adjusted R2 0.111 0.111 0.110 ***, **, and * indicate significance at 1, 5, 10%, respectively The p values are based on onetailed (two-tailed) t tests if there is (is not) a prediction on the sign of the coefficient See appendix for variable definitions R 139 .. .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. .. 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. .. 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