بهره وری نوآورانه و بازده سهام
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|2293||2012||23 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 20167 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||27 روز بعد از پرداخت||1,815,030 تومان|
|ترجمه تخصصی - سرعت فوری||هر کلمه 180 تومان||14 روز بعد از پرداخت||3,630,060 تومان|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Financial Economics, Available online 1 October 2012
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.
Recent studies provide evidence suggesting that, owing to limited investor attention, prices do not fully and immediately impound the arrival of relevant public information, especially when such information is less salient or arrives during a period of low investor attention (e.g., Klibanoff et al., 1998, Huberman and Regev, 2001, DellaVigna and Pollet, 2009, Hirshleifer et al., 2009 and Hou et al., 2009). Several papers, therefore, argue that limited attention results in underreaction and return predictability. Theoretical models also predict that limited investor attention affects stock prices and can cause market underreaction (Hirshleifer and Teoh, 2003, Hirshleifer et al., 2011 and Peng and Xiong, 2006). These studies consider the processing of news about current performance such as earnings announcements. However, we would expect investors to have even greater difficulty processing information that is less tangible and that is about firms whose future prospects are highly uncertain. For example, information about the prospects of new technologies or other innovations should be especially hard to process, because the significance of such news depends upon strategic options and major shifts in industrial organizational structure.1 If so, there will on average be price drift after the arrival of non-salient public news about the prospects for firms' innovations. In other words, on average there will be positive (negative) abnormal returns after good (bad) news. In this study, we examine the relation between innovative efficiency and subsequent operating performance as well as stock returns. By innovative efficiency, we mean a firm's ability to generate patents and patent citations per dollar of research and development (R&D) investment. The denominator, R&D, measures resource input to innovation. Patents and citations are measures of innovative output, because innovations are usually officially introduced to the public in the form of approved patents. US firms have increasingly recognized the necessity to patent their innovations and, hence, have been especially active in patenting activities since the early 1980s (Hall and Ziedonis, 2001 and Hall, 2005) owing to the creation of the Court of Appeals for the Federal Circuit in 1982 and several well-documented patent lawsuits (e.g., the Kodak-Polaroid case). Patents are thus the most important measure of contemporary firms' innovative output (Griliches, 1990), and they are actively traded in intellectual property markets (Lev, 2001). A firm's past innovative efficiency can be less salient to investors than explicitly forward-looking information about the prospects for the particular R&D projects that the firm is undertaking. For example, investors devote considerable attention to analyst reports and news articles about the potential outcomes of clinical phase trials conducted by a biotech and pharmaceutical firm, while historical performance of past R&D efforts receives less media attention. According to Kahneman and Lovallo (1993, p. 17), people tend to consider the judgment or decision problem they are facing as unique and, in consequence, “neglect the statistics of the past in evaluating current plans.” Kahneman and Lovallo call a focus on the uniqueness of the problem the “inside view” and a focus on relevant statistical performance data from previous trials the “outside view.” An excessive focus on the inside view implies that people will tend to be overoptimistic about prospects for success when they neglect unfavorable non-salient statistical information and tend to be less optimistic, and perhaps over-pessimistic, about the prospects of success, when they neglect favorable statistical information.2 Furthermore, extensive evidence exists that individuals pay less attention to, and place less weight upon, information that is harder to process (see, e.g., the review of Song and Schwarz, 2010). Information about innovations is hard to process, because it requires developing and applying a theory of how the economic fundamentals of a firm or its industry are changing. It also requires an analysis of the road from patents to final products on the market, the profit of which can be highly uncertain and long deferred. We would expect such hard-to-process information to be underweighted unless there is some offsetting effect (such as high salience). These considerations suggest that investors will underreact to the information content in innovative efficiency because of the difficulty evaluating the economic implications of patents and patent citations. If so, then firms that are more efficient in innovations will be undervalued relative to firms that are less efficient in innovations. Therefore, we expect a positive relation between innovative efficiency and future stock returns and operating performance. An alternative argument for why innovative efficiency would predict higher future returns derives from the q-theory ( Cochrane, 1991, Cochrane, 1996 and Liu et al., 2009). Firms with higher innovative efficiency tend to be more profitable and have higher return on assets. All else equal, the q-theory implies that higher profitability predicts higher returns because a high return on assets indicates that these assets were purchased by the firm at a discount (i.e., that they carry a high risk premium). Specifically, suppose that the market for capital being purchased by a firm is competitive and efficient. When a firm makes an R&D expenditure to purchase innovative capital, the price it pays is appropriately discounted for risk. For concreteness, we can think, for example, of a firm that acquires a high-tech target at a competitive market price.3 In this scenario, a firm on average achieves higher return (large number of patents, resulting in high cash flows) on its innovative expenditures as fair compensation if its purchased innovative capital is highly risky, and it receives low return if capital is relatively low-risk. Past innovative efficiency is, therefore, a proxy for risk, so firms that have high past innovative efficiency (IE) should subsequently be productive in patenting (Dierickx and Cool, 1989) and earn higher profits and stock returns.4 In other words, q-theory also predicts a positive IE-return relation. To test our key hypothesis that innovative efficiency is positively associated with contemporaneous stock market valuation and positively predicts future operating performance, market valuation, and stock returns, we use two measures of innovative efficiency in year t: patents granted in year t scaled by R&D capital in year t−2 (Patents/RDC) and adjusted patent citations received in year t by patents granted in years t−1 to t−5 scaled by the sum of R&D expenses in years t−3 to t−7 (Citations/RD). The lag between the innovative input (R&D) and output (patents) reflects the average two-year application-grant lag (Section 3.1 provides details). These IE measures are in general not highly correlated with other innovation-related return and operating performance predictors, such as R&D-to-market equity (Chan, Lakonishok, and Sougiannis, 2001), significant R&D growth (Eberhart, Maxwell, and Siddique, 2004), patents-to-market equity, and change in adjusted patent citations scaled by average total assets (Gu, 2005). Therefore, the IE measures potentially contain useful incremental information. Fama-MacBeth (1973) regressions show that a higher IE measure (using a logarithmic transform of IE) predicts significantly higher return on assets (ROA) and cash flows (CF) over the next year after extensive controls.5 We also find that both current and lagged IE are significantly positively associated with equity market-to-book (MTB) using Fama-MacBeth regressions that control for abnormal earnings, R&D tax shields, R&D-to-book equity, RDG, PAT/ME, ΔAPC, AD/ME, CapEx/ME, and industry. Furthermore, a significantly positive relation exists between the two IE measures and future stock returns using Fama-MacBeth regressions that control for various possible industry variables and return predictors, including RD/ME, RDG, PAT/ME, ΔAPC, size, book-to-market equity (BTM), momentum, CapEx/ME, AD/ME, ROA, asset growth, net stock issues, and institutional ownership. These findings support our hypotheses that innovative efficiency contains distinct information about future operating performance and market valuation that is incremental to that of other innovation measures and firm characteristics. Portfolio analysis confirms the significantly positive relation between IE and future operating performance as well as stock returns. We sort firms with non-missing IE measures into three IE portfolios (Low, Middle, and High) based on the 33rd and 66th percentiles of the IE measures, and we find that the high IE groups in general have the highest ROA, cash flows, earnings, and profit margin averaged over the subsequent 5 years. The monthly value-weighted size-adjusted returns in excess of the one-month Treasury bill rate for the IE portfolios increase monotonically with IE: 49, 86, and 90 basis points for the low, middle, and high Patents/RDC portfolios, respectively, and 59, 81, and 85 basis points for the low, middle, and high Citations/RD portfolios, respectively. The risk-adjusted returns of the IE portfolios also increase monotonically with IE. Specifically, the monthly value-weighted alphas estimated from the Carhart (1997) four-factor model for the low, middle, and high Patents/RDC portfolios are −19, 22, and 27 basis points, respectively, and the monthly Carhart alphas for the low, middle, and high Citations/RD portfolios are −9, 14, and 26 basis points, respectively. Furthermore, the positive alphas of the high IE portfolios are statistically significant at the 1% level, which indicates that high IE firms are undervalued relative to the Carhart model benchmark. In unreported results, we find the difference in the Carhart alphas between the high and low IE portfolios is also statistically significant at the 1% level for both IE measures. These patterns are similar for alphas estimated from the Fama and French (1993) three-factor model and the investment-based three-factor model (Chen, Novy-Marx, and Zhang, 2011). In addition, the high IE portfolio generally has lower loadings than the low IE portfolio on the market factor, suggesting that high IE firms are less risky than low IE firms. To examine whether the financing-based mispricing factor Undervalued Minus Overvalued (UMO; Hirshleifer and Jiang, 2010) helps explain the IE effect, we add UMO to the Carhart model and the investment-based model. We find the alphas estimated from these augmented models for the high IE portfolios remain significantly positive at the 1% level. This evidence indicates that the IE effect is incremental to the mispricing effect as captured by UMO. In addition, the alphas estimated from these augmented models increase monotonically with IE. In unreported results, we find the difference in the alphas estimated from these two augmented models between the high and low IE portfolios is also statistically significant at the 1% level for both IE measures. If the IE-return relation represents a market inefficiency driven by psychological constraints such as limited attention, we expect to observe greater return predictability among stocks with low investor attention and among hard-to-value stocks. To test this hypothesis, we use size and analyst coverage as proxies for attention to a stock (Hong, Lim, and Stein, 2000) and firm age, turnover, and idiosyncratic volatility as proxies for valuation uncertainty (Kumar, 2009).6 We expect a stronger IE effect among firms with small market capitalization, low analyst coverage (AC), young age, high turnover, and high idiosyncratic volatility (IVOL). The Fama-MacBeth subsample regressions provide supporting evidence. The average IE slopes among low attention and hard-to-value stocks are in general statistically significant and are substantially larger than those among high attention and easy-to-value stocks, which often are insignificant. For example, the slope on Citations/RD is 0.08% (t=2.21) in low AC firms, but only 0.05% (t=1.65) in high AC firms. Similarly, the slope on Citations/RD is 0.11% (t=3.26) in young firms, but only 0.02% (t=0.91) in old firms. In addition, the slope on Citations/RD is 0.08% (t=2.08) in high IVOL firms, but only 0.03% (t=1.34) in low IVOL firms. Although the cross-subsample differences in the IE slopes are not always statistically significant, their magnitudes are economically substantial. To further examine if the IE-based return predictability is driven by risk, mispricing, or both, we construct a factor-mimicking portfolio for innovative efficiency, Efficient Minus Inefficient (EMI), based on Patents/RDC (EMI1) and on Citations/RD (EMI2) following the procedure in Fama and French (1993). Specifically, at the end of June of year t from 1982 to 2007, we sort firms independently into two size groups (small “S” or big “B”) based on the NYSE median size breakpoint at the end of June of year t, and three IE groups (low “L,” middle “M,” or high “H”) based on the 33rd and 66th percentiles of IE in year t−1. The intersection of these portfolios forms six size-IE portfolios (S/L, S/M, S/H, B/L, B/M, and B/H). Value-weighted monthly returns on these six portfolios are computed from July of year t to June of year t+1. The EMI factor is computed as (S/H+B/H)/2−(S/L+B/L)/2. We find that these two EMI factors are not highly correlated with well-known factors such as the market, size, value and momentum factors (Carhart 1997), the investment and return on equity (ROE) factors (Chen, Novy-Marx, and Zhang, 2011), and the UMO factor (Hirshleifer and Jiang, 2010). The correlations between the EMI factors and these well-known factors range from –0.15 to 0.13. In addition, we construct four innovation-related factors based on RD/ME, RDG, PAT/ME, and ΔAPC, following the same method in constructing the EMI factors. We find that the EMI factors are not highly correlated with these four factors, except for the PAT/ME factor. The correlations between the EMI factors and the RD/ME, RDG, and ΔAPC factors range from 0.07 to 0.36. The correlation with the PAT/ME factor is 0.66 for EMI1 and 0.52 for EMI2. The average monthly return of the EMI1 factor is 0.41%, which is higher than that of the size factor (0.07%), the value factor (0.37%), the investment factor (0.36%), the RDG factor (0.21%), the PAT/ME factor (0.31%), and the ΔAPC factor (0.00%). Furthermore, EMI1 offers an ex post Sharpe ratio, 0.23, which is higher than that of all the above factors except the mispricing factor (0.27). The EMI2 factor has similar but slightly lower average monthly return (0.26%) and ex post Sharpe ratio (0.15). The high level of the equity premium is a well-known puzzle for rational asset pricing theory (Mehra and Prescott, 1985). Therefore, on the face of it, the high ex post Sharpe ratios associated with the EMI factors also suggest that the IE-return relation can be too strong to be entirely explained by rational risk premia. Adding EMI1 to the Fama and French three factors increases the ex post Sharpe ratio of the tangency portfolio from 0.29 to 0.39 with a weight of 42% on EMI1. Even when all of the above factors are included, the weight on EMI1 in the tangency portfolio is 22%, which is substantially higher than that of any of the other factors. Adding EMI2 to the Fama and French three factors increases the ex post Sharpe ratio of the tangency portfolio from 0.29 to 0.34 with a weight of 33% on EMI2. Even when all of the above factors are included, the weight on EMI2 in the tangency portfolio is 15.65%, which is higher than that of any of the other factors except the mispricing factor (17.22%) and the market factor (16.00%). These findings imply the IE-return relation captures return predictability effects above and beyond those captured by the other well-known factors and the four innovation-related factors we construct. Our study offers a new innovation-related measure, IE, which is contemporaneously associated with market valuation and predicts future operating performance and stock returns. Existing studies relating to innovation and the stock market focus on the effects of either the input (R&D) or the output (patents) of innovation separately. (The relevant papers in this literature are discussed in Section 2.) Our paper differs in focusing on 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 innovative efficiency is incremental to that of other innovation-related variables such as R&D intensity, significant R&D growth, patent counts, and citations. We also examine whether the IE effect is driven by risk or mispricing, and we explore how the IE effect interacts with proxies for limited attention and valuation uncertainty. The paper continues as follows. Section 2 reviews the literature on innovative input, output, and efficiency. Section 3 discusses the data, IE measures, and summary statistics. Section 4 examines the relation between IE and operating performance as well as market valuation. Section 5 shows the stock return predictability based upon IE using both regression and portfolio analyses. Section 6 examines the IE-return relation within subsamples based on limited attention and valuation uncertainty. Section 7 constructs Efficient Minus Inefficient (EMI) factors and shows their effects on the ex post Sharpe ratio of the tangency portfolio. Section 8 concludes.
نتیجه گیری انگلیسی
We find that firms that are more efficient in innovation on average have higher contemporaneous market valuations and superior future operating performance, market valuation, and stock returns. The relation between innovative efficiency (IE) and operating performance is robust to controlling for innovation-related variables suggested by other studies, such as R&D-to-market equity, significant R&D growth, patent-to-market equity, and change in adjusted patent citations scaled by average total assets. Similarly, the relation between IE and current (and future) market valuation is also robust to a variety of controls. The positive association of IE with subsequent stock returns is robust to controlling for standard return predictions and innovation-related variables. Empirical factor pricing models, such as the Carhart four-factor model and the investment-based three-factor model, do not fully explain the IE-return relation. Adding the financing-based mispricing factor UMO to these models improves the models’ explanatory power, but substantial and significant abnormal return performance remains. These findings show that the IE effect on returns is incremental to existing return predictors and cannot be explained by known factors (risk or mispricing). Further analyses show that proxies for investor inattention and valuation uncertainty are associated with stronger ability of IE measures to predict returns. These findings provide further support for psychological bias or constraints contributing to the IE-return relation. The high Sharpe ratios of the two Efficient Minus Inefficient (EMI) factors also suggest this relation is not entirely explained by rational pricing. Finally, regardless of the source of the effect, the heavy weight of the EMI factors in the tangency portfolio suggests that innovative efficiency captures pricing effects above and beyond those captured by the other well-known factors and other innovation effects, which focus on either innovative input or output separately. The fact that the mispricing factor partly helps explain the innovative efficiency effect suggests that there is commonality in mispricing across firms associated with innovative efficiency. Such commonality could arise, for example, if investors do not fully impound news about the correlated shifts in innovative efficiency that are driven by technological shifts. This is consistent with behavioral models in which there is commonality in mispricing (Daniel et al., 2001 and Barberis and Shleifer, 2003). As a policy matter, if capital markets fail to reward firms that are more efficient at innovation, there will be potential misallocation of resources in which firms that are highly effective at innovation are undercapitalized relative to firms that are less effective at innovating. Our findings suggest that investors should direct greater attention to innovative efficiency and that innovative efficiency can be a useful input for firm valuation.