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The PR PremiumSmadar Sieva

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THE JOURNAL OF BEHAVIORAL FINANCE, 15: 43–55, 2014Copyright C© The Institute of Behavioral FinanceISSN: 1542-7560 / 1542-7579 onlineDOI: 10.1080/15427560.2014.878133

The PR Premium

Smadar SievUniversity of Haifa

Press releases (PR) are the most common and popular way for a firm to publicize its news.The annual number of PR varies substantially among firms, from just a few to hundreds. Thiswork documents a gap in stock’s returns between firms that publish low and high number ofPR per annum in favor of the former. This gap was found for both the year of publication andthe following one and its magnitude is 7–8% and 5–6%, respectively. This gap remains intacteven after controlling for firm characteristics such as beta, market capitalization and more. Icall this gap the “PR Premium.”

Keywords: Disclosure, Press release, Priming, Financial markets, Behavioral finance

INTRODUCTION

Publicly traded companies employ various means to dis-close their performance and activity, such as PR, presenta-tions to analysts, information on websites and conferencescalls. In recent years, the Internet has become the main com-munication channel between firms and market participants.The Internet seems to be the perfect platform to address theRegulation Fair Disclosure (Reg. FD) requirement, whichmandates companies to disclose all material informationsimultaneously to all investors, a regulation adopted by theSecurities and Exchange Commission (SEC) to prevent se-lective disclosure. While firms are required to report all ma-terial information that may affect the value of the securitytraded, they are free to choose if and when to disclose othertypes of information. This discretionary information is poten-tially valuable to investors as it may facilitate understandingof the business environment and forecasting future prospects.

Conventionally, the demand for disclosure stems from in-formation asymmetry between insiders and the public. Theconsensus emerging from the disclosure literature is that cor-porative disclosure is essential for market efficiency. It en-hances transparency and decreases information asymmetry,thereby contributing to improved investment decisions (seeHealy and Palepu [2001], Kothary and Short [2003]). Whileinvestors benefit from the additional disclosure, superfluousdisclosure might harm the firm as it may reveal commercial

Address correspondence to Smadar Siev, Lecturer, University of Haifa,Dr Yona Engel 9, Haifa 34952, Israel. E-mail: Smadar.siev@gmail.com

secrets to its competitors. As a result, firms disclose withcaution and discretion.

PRs usually appear on a firm’s website under the In-vestor Relations section and are distributed via commercialpress release distribution services such as PR Newswire,PR NewsChannel and Business Wire. These services dis-tribute PR to many common financial sites such as fi-nance.yahoo.com and www.google.com/finance. The easeof distribution lessens the firms’ reluctance to report mi-nor issues, and this is one of the reasons for their expandingnumbers.

While reading a PR, one should keep in mind that perhapsit is in the company’s interest to present past performanceas well as future plans in the most positive light. This ob-servation is of special importance because the PR is notdirectly subject to any third-party scrutiny. The nature ofthe PR may be quantitative (e.g., sales segmentation, neworders, future forecasts of financial parameters and backloglevels) or qualitative (e.g., research and development plans,new products, new features, strategic alliances, new distribut-ing channels, technical releases and customer satisfaction re-ports). PR also covers a wide range of firm activities whichare indirectly related to business operations (e.g., communitydonations, participating in sports events, awards). Examplesfor the variability of PR content nature appear in the Ap-pendix. Presumably, investors most value releases containing“hard information” (Petersen [2004]) because they may beeasily assimilated into future forecasts. A PR usually has astructured form. It starts (or finishes) with a brief descrip-tion of the company and may contain praises such as: globalprovider of advanced solutions, the world’s leading provider

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of. . ., Company Y is the global distributor of. . ., and theinnovative technology leader. Readers are exposed to thesemarketing tidbits whenever reading PR issued by a firm, andthus may associate a firm with its self praises.

In the present study, the term PR volume refers to thecounted number of published press releases for each firmper annum. Thus, the number in reference is the result ofcounting each release as a whole, not by number of pagesor words. There is a high variability in PR volume amongfirms, with some that publish just a few per year, while somepublish several each week. (For example, in 2006, IBM pub-lished 728 PR while China Mobile published only 4.) Asexplained later, this study classifies firms into the Low PressRelease (LPR) group and High Press Release (HPR) group.My suggestion is that only rarely do firms have informativenews on a daily basis; therefore, the often prevalent high-frequency PR streaming may also be aimed at enhancing thefirm’s presence among the public. My goal is to documentthe effect-positive or negative of annual PR volume on stockreturns.

The remainder of the paper is organized as follows: Thesecond section reviews some disclosure literature that is rel-evant to this research. The third section discusses researchmotivation and hypothesis. The fourth section describes thesample selection. The fifth section contains the empirical ex-amination. The sixth section suggests some results reasoning,and the final section summarizes.

LITERATURE REVIEW

The relation between disclosure and firm value was examinedin several empirical studies. Fishman and Hagerty [1989] de-veloped a model which shows that more efficient stock pricescan lead to more efficient investment decisions. Achievingsuch efficiency give firms the motivation to increase volun-tary disclosure, as opposed to the regulator’s concern regard-ing not enough disclosure. Since the firms are also competingfor traders’ attention, the more informative the firm’s disclo-sure is the more profitable it is for traders to learn aboutand trade that firm’s stock. Another empirical work regard-ing stock prices was done by Lang and Lundholm [2000].They examined disclosure activity around seasonal equityissuance and its relation with stock price. They found thatfirms that dramatically increased their disclosure activity be-fore the issuance enjoyed increased stock prices prior to theoffering, albeit experienced a large decline when the marketrevealed their intention to issue. The authors suggested thatthe intention behind the increased disclosure was to “hypethe stock” (p. 1). Healy, Hutton and Palepu [1999] found thatexpanding disclosure increased stock prices in the year of theincreased disclosure as well as in the consecutive year. Ex-panding disclosure also increased institutional ownership andanalysts’ coverage. Information asymmetry as measured bythe bid-ask spread is reduced as the level of disclosure rises.The same was concluded by Leuz and Verrecchia [2000] andSchrand and Verrecchia [2004].

The connection between disclosure and liquidity waspointed out by Amihud and Mendelson [1986]. One of theirclaims was that greater disclosure can contribute to greaterliquidity reflected in reduction in the bid-ask spread. Firm’sfinancial policy that enhances liquidity can raise its value.Welker [1995] also documents negative association betweendisclosure levels and relative bid-ask spreads. Welker [1995]and Leuz and Verrecchia [2000] empirically found that trad-ing volume increased as the level of disclosure increased.This finding is consistent with the theory that firms with highlevel of disclosure attract more investors because they can beconfident that stock transactions are done at “fair” prices.

Theoretical support regarding the negative relation be-tween disclosure and cost of capital was given by Diamondand Verrecchia [1991]. They showed that revealing publicinformation, aimed to reduce information asymmetry be-tween firms and market participants, can reduce firm’s costof capital by driving increased demand from large investors.Empirical evidence regarding the relation between disclosureand cost of capital was found in several studies. For exam-ple, Botosan [1997] found that negative relation betweendisclosure in the annual reports and cost of equity capitalexists only for firms with low analyst coverage. Likewise,Botosan and Plumlee [2001] found that the cost of equitycapital decreases with the level of annual reports disclosurebut increases with the level of timely disclosures. Kothari andShort [2003] found mixed results regarding the relationshipbetween disclosure and cost of equity capital. This relationdepends on the disclosure source: the firm, analyst reports orfinancial press and is conditioned by the disclosure nature:favorable or unfavorable. Froidevaux [2004] found negativerelationship between disclosure level in a companies’ web-site (Investor Relations section) and a firms’ cost of capital(evaluated by DCF model) implied in stock price. Positive re-lationship between firm size and disclosure level was mentionfor example, by Chow and Wong-Boren [1987] who hypoth-esized that larger firms have stronger incentive to improvetheir corporate reputation and public image by disclosingmore information.

Recent research regarding media coverage and stock re-turns includes a paper by Fang and Peress [2009] that ex-amined the effect of media coverage on stock returns. Theyfound that a portfolio of stocks not covered by the media out-performed a portfolio of stocks with high media coverage.

RESEARCH MOTIVATION AND HYPOTHESIS

Disclosure level is difficult to quantify. The measurement ofdisclosure is generally subjective and researchers use differ-ent measures. Botosan [1997] used a self-constructed gaugethat was based on data items taken from the financial reports.Miller [2002] constructed a gauge based on the financial pressand categorized disclosures according to their content. Gelband Zarowin [2002], Botosan and Plumlee [2001] and Langand Lundholm [1993] used the disclosure score given by the

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AIMR-FAF, obviously a subjective measurement.1 Anotherdisclosure measurement was introduced by Schrand and Ver-recchia [2004], who classified disclosure as informative anduninformative according to its content. Other limitations,Froidevaux [2004] sampled Internet disclosures volume witha span of only several days. Botosan [1997], Miller [2002],Healy, Hutton and Palepu [1999] and Froidevaux [2004]all examined samples which only contained around 100firms.

Since PR is the major and most popular medium of dis-closure for firms, PR volume is apparently the best proxyfor disclosure level. Focusing specifically on PR volume (ac-tual number of press releases per year) might be consideredsomewhat oversimplification since the number in itself is notqualitative, nor does it deal with informative content. ThePR measure, however, generated coherent results that betterexplain disclosure impact on stocks return.

The study classifies PRs into low level (LPR) and highlevel (HPR). With this classification we could fully estimateand interpret the difference between the groups and explainstock returns. The impact of disclosure increases by its sheervolume, and this measure requires no need to get involvedinto the controversial evaluation of PR content quality. More-over, the results are unconditional and consistent across theboard and can be applied as a usable tool for PR impactanalysis. The findings can help a firm’s PR policy makersregarding the PR volume they choose or investors in assess-ing a stock before buying. In light of the above, the researchhypothesis is:

H0: There is no difference in firms’ stocks returns that stemfrom their difference in PR volumes.

H1: The stocks of firms that publish high annual PR volumeyield significant lower annual returns than the stocks of firmswith low PR volume.

SAMPLE DEFINITION AND BREAKDOWN

The sample covers firms in the technology sector that tradedat NASDAQ (80%) and NYSE (20%) from 2001 to 2007. Thedatabase was constructed by combining information from thefollowing sources: annual volumes of firm PR were collectedfrom firm websites (unreported years were supplemented byutilizing the Internet Archive), stock prices and volume infor-mation from Yahoo Finance; financial reports from Compu-stat Research Insight, media coverage from LexisNexis andMarket Equity (ME) breakpoints (for allocating the firmsinto size groups according to their stock market capitaliza-tion) from Kenneth French’s website.

The Kenneth French 20 Market Equity quantile divisionwas used in order to create five groups in ascending order:5–8, 9–11, 12–14, 15–16 and 17–20. Companies in quan-tiles 1–4 were omitted from the sample due to their low

TABLE 1Annual PR Volume Statistics – Entire Sample

2001 2002 2003 2004 2005 2006 2007

Mean 68 75 69 72 77 74 71Median 44 48 46 50 53 51 50S. D. 78 82 81 75 80 84 80Range 784 615 718 580 569 800 823Min. 3 3 2 4 3 0 1Max. 787 618 720 584 572 800 824Count 375 274 383 335 362 365 373

Note. The table provides descriptive statistics for the annual PR volumefor the entire sample.

market value. The entire sample consists of 498 firms and1,393 firm years. Seventy-five firm years (5% of the ob-servations) were excluded from the analysis using the fol-lowing filters: 36 firm years lacked subsequent year returninformation due to acquisition, mergers, or becoming pri-vate; 29 firm years had a symbol mismatch with Compus-tat data; and 10 firm years as their return was higher than200%.

The chosen proxy for disclosure level was the PR annualnumber. As seen in Table 1, there is high variability of theannual PR volume among the firms, ranging from just afew PR per annum to hundreds. Statistics for Annual PRVolume subpaneled by Market Value (not displayed) showthat average PR volume increase with firm size; from 45 (PRper annum) in the smallest Market Value group to 153 in thehighest.

The sample was arranged as follows: For each year ineach group size the firms are placed according to the an-nual number of their published PR, from low to high. Thefirst third was named LPR and the last third HPR; firms inthe middle third were omitted from the sample to achieve atwo-group test.2 This classification includes certain normal-ization to PR activity. For example, a firm with 40 PR perannum can be classified to either LPR or HPR depending onthe other firms’ annual PR volume in that year and groupsize.

PRESS RELEASES AND STOCK RETURNS

Multiple factors are known to explain stock returns. Risk,firm size and book-to-market ratio are all known and welldocumented examples, although more possible factors arestill unknown. The above factors for HPR and LPR firmswere compared using univariate analysis. OLS regressionswere then employed on select variables as described in themultivariate analysis section.

Univariate Analysis

Several variables between the LPR and HPR firm groupswere compared. Return (t) is the rate of return on a firm’sstock at period t; and Trade (t) is a firm’s daily average

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trading volume at period t (in thousands of $). The riskproxies (Std dev, Beta1, Beta3, bi and Residual deviation)are: Std dev, the standard deviation of daily returns calcu-lated over the period PR Year (henceforth PR Year is theyear for which the PR were counted). Std dev was used asproxy for total risk. Beta1 and Beta3 are systematic risk co-efficients from the market model regression on the S&P500index return; Beta1 was estimated using daily return ob-servations over the PR Year, and Beta3 was estimated us-ing 3 years weekly return observations ending and inclusiveof the PR Year; bi is the coefficient of (Rm-Rf) from theFama and French three-factors model (parameters taken fromtheir website). The factor loadings were estimated for eachcompany using daily observations over the PR Year. Resid-ual deviation is a standard deviation of the residuals from theBeta1 regression estimation. Residual deviation was usedas a proxy for specific risk. Several, possibly overlappingproxies for risk (total, systematic and specific) were used inorder to capture a broad and robust view of the risk differ-ences between the LPR and HPR groups. At a later stage inthe multivariate analysis only the beta33 as control for riskwas used. Financial ratios are: market leverage and book-to-market (B/M). Market leverage was calculated as Total BookDebt divided by [Firm’s Equity Market Value + Total BookDebt]. B/M ratio was calculated by dividing Equity BookValue by Market Value. Both were measured for the end ofJune of the PR Year. The final variable was Media Cover-age; measures in the same method as used by F&P, namely,the annual number of newspaper articles published about astock, in four main U.S. newspapers: New York Times, WallStreet Journal, WashingtonPost and USA Today. Data fromthe LexisNexis database and articles with relevance score ofmore than 90% were extracted.

Table 2 provides descriptive statistics of the entire sampleand presents averages for Risk Proxies, Returns, TradingVolumes, Leverage, B/M Ratio, and Media Coverage. Thistable presents four significant results. First, all risk proxiesare higher for HPR firms. Second, returns are higher forthe LPR groups for the PR Year and for the following year.Third, trading volumes are about three times higher for theHPR firms. Forth, Media Coverage is higher for HPR firms.

One possible explanation to the first finding the higherrisk that characterizes the HPR groups, was suggested byBushee and Noe [2000], who found support to the assumptionthat greater timely disclosure attracts a greater proportion oftransient institutional investors whose trading activities havean increasing volatility effect on stock returns. Accordingto common pricing models, higher risk is usually associatedwith higher return. Analyzing the first and second findings,the present study unexpectedly showed a negative correlationbetween risk and return. In the sample’s 1,393 firm years, theHPR group has higher risk (using several risk proxies) butLPR firms have significantly higher returns in the PR Yearand the following one. This finding is an anomaly to theexpected risk-return trade off. Subsequently, the multivariate

analysis was applied toward the goal of examining whetherthe anomaly of lower return with higher risk for the HPRis a consequence of their relatively intensive PR strategy.Can the difference in returns be explained partially by thedifference in the volumes of PR? Regarding the third finding,the higher trading volume of HPR suggests that high PRactivity dramatically increases the public interest in HPRstocks. As to the fourth finding, media coverage moves inthe same direction as PR volume; that is, wherever PR isrelatively high, the media coverage is also relatively high.

Multivariate Analysis

Due to the unexpected tradeoff between risk and return foundin the univariate analysis, further analysis was performed inorder to try to explain return. Other factors in addition to riskwhere introduced and the hypothesis was tested employingOLS regressions. Three sets of regressions were conducted.In the first set realized return was the target variable. In thesecond set abnormal or abnormal excess return was the ex-plained variable and the third set regressions were performedas robustness tests.

PR Year+1 Realized Return

In the first set, the PR Year+1 annual realized return wasregressed on market beta and PR volume in addition to con-trol variables for: years, market capitalization; book to mar-ket ratio, leverage and media coverage (Henceforth: the mainspecification).

return (PR Year + 1)

= β0 + β1 Beta + β2 PR + β3Y1 + · · · + β8Y6

+β9 MV1 + · · · + β12 MV4 + β13B

M

+β14 Leverage + β15 NMC + β16 HMC (1)

PR is a dummy variable used to classify the volume of pressrelease activity4; defined as 1 for high PR activity and 0 forlow. Y1 to Y6 are dummy variables for the seven years in whichthe press releases were counted (Y1 = 1 for 2001, etc.). MV1

to MV4 are dummy variables used to classify firms accordingto their market value into 5 groups (MV1 = 1 for the smallestgroup etc.). B/M ratio and leverage are described above. Thevariables NMC (No Media Coverage) and HMC (High MediaCoverage) are dummy variables that were constructed basedon the total number of articles published on a stock during ayear. NMC gets 1 for stocks with no media coverage. HMCgets 1 for stocks with high media coverage (namely abovethe median number of articles published on a firm duringa year). Both variables get zero for stocks with low mediacoverage (below the median). Beta is included to control forsystematic risk. The use of control variables MV and B/Mratio is in line with Fama and French [1994] three factormodel. Media coverage was taken as a control variable per

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TABLE 2Data Statistics for the Entire Sample

Risk Proxies and B/M ratio

Beta3 Beta1 Std dev

LPR HPR Diff LPR HPR Diff LPR HPR Diff

Mean 1.44 1.60 −0.16∗ 1.34 1.45 −0.11∗ 2.9% 3.1% −0.2%∗∗S. D. 0.58 0.59 0.60 0.60 1.4% 1.6%Min. −0.02 −0.17 −0.23 0.19 0.9% 0.8%Max. 3.58 3.94 3.53 3.74 12.2% 9.7%

Residual deviation bi B/M Ratio

Mean 2.5% 2.7% −0.2%∗∗ 1.21 1.27 −0.06∗∗ 0.41 0.39 0.02S. D. 1.2% 1.4% 0.50 0.50 0.41 0.35Min. 0.7% 0.7% −0.11 −0.82 −0.82 −1.89Max. 11.8% 9.4% 3.09 2.94 5.31 4.13

Return and Trading Volume in PR Year + 1

Return Trading Volume Leverage

Mean 2.5% −3.4% 5.9%∗∗ 40,893 119,688 −78K∗ 0.22 0.21 0.01S. D. 45.1% 43.4% 85,770 286,868 0.18 0.16Min. −94.2% −97.8% 6 0 0.00 0.01Max. 183.1% 164.6% 1,073,111 2,967,047 0.93 0.85

Return, Trading Volume and Media Coverage for the PR Year

Return Trading Volume Media Coverage

Mean 16.1% 9.2% 7%∗ 39,999 120,353 −80K∗ 2.3 13.9 −11.6∗ 696 ∗ 2Median 13.6% 2.7% 0.0 2.0S. D. 45.4% 48.1% 88,991 287,798 7.8 49.3Min. −86.3% −89.5% 18 7 0.0 0.0Max. 198.8% 197.5% 895,119 3,416,711 101.0 676.0

Note. Trading Volume values are in thousands of $.The notations ∗, ∗∗ and ∗∗∗ indicate significance level at a P-value of less than 0.01, 0.05 and 0.1 respectively, using a two-tailed t test of significance.

Variable description is in the univariate analysis section.

TABLE 3PR Year+1 Annual Return Regressed on Beta, PR, Years, Market Value, B/M Ratio, Leverage and Media Coverage

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat

Intercept −0.29 −10.1 −0.31 −9.3 −0.34 −9.5 −0.36 −8.9 −0.34 −8.8 −0.34 −9.8Beta −0.07 −3.7 −0.06 −3.6 −0.06 −3.2 −0.06 −3.2 −0.06 −3.1 −0.06 −3.1PR −0.04 −2.3 −0.05 −2.5 −0.04 −2.3 −0.05 −2.5 −0.04 −2.2 −0.05 −2.4Y1 0.04 1.2 0.04 1.2 0.05 1.5 0.04 1.4 0.05 1.5 0.04 1.4Y2 0.95 25.6 0.95 25.4 0.96 25.9 0.95 25.8 0.95 25.7 0.95 25.6Y3 0.46 15.0 0.46 15.0 0.47 14.9 0.47 15.0 0.47 15.0 0.46 15.0Y4 0.47 16.4 0.47 16.4 0.48 16.5 0.47 16.4 0.48 16.6 0.48 16.5Y5 0.59 21.4 0.59 21.3 0.59 21.1 0.59 21.1 0.58 21.2 0.58 21.1Y6 0.54 17.1 0.54 17.1 0.54 16.8 0.54 16.9 0.54 16.8 0.54 16.7MV1 0.02 0.9 0.04 1.2MV2 0.01 0.3 0.02 0.7MV3 0.03 1.1 0.04 1.5MV4 −0.02 −0.8 −0.01 −0.4B/M −0.04 −1.4 −0.04 −1.5 −0.04 −1.4 −0.04 −1.4Leverage 0.20 2.9 0.19 2.8 0.20 2.9 0.19 2.8NMC 0.01 0.63 0.01 0.4 0.00 0.2HMC 0.03 1.32 0.04 1.4 0.02 0.8R2 42.7% 42.7% 43.1% 43.1% 43.1% 43.1%Obs. 1393 1393 1393 1393 1393 1393

Note. The table provides average yearly parameters estimates and their t statistics obtained through the OLS regression. Variable description is in theunivariate analysis section.

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the work of Fang and Peress. Table 3 displays the regressionresults.

The coefficient of PR is significant and varies between−5% and −4%, suggesting that high PR activity, namelypublishing many PRs a year, yields lower annual realized re-turn. This gap in returns is defined as The PR Premium. Thecoefficient of the Beta is negative5, significant and around−6%6. The years’ coefficients are significant, probably dueto huge differences in market performance during the sampleyears. The market value and the B/M Ratio coefficients areinsignificant. The leverage coefficient is positive and signif-icant, suggesting that the higher the leverage the higher thereturn in the following year. The media coverage coefficientsare both positive albeit insignificant (Using F test), a resultdissimilar to the findings of Fang and Peress. However, asthe F-test is only two-tailed (and not directional), NMC andHMC were used separately (using a two group’s classifica-tion for media coverage) in order to check their contribution.After employing two more regressions (models 5 and 6), theNMC and HMC coefficients still remain insignificant.

PR Year + 1 Abnormal Return

The second set of regressions employs abnormal return(henceforth AR) as the explained variable. AR is a sim-ple difference between realized (or excess) return and cor-responding benchmark. All three, the market model, marketreturn and Fama and French three-factors model, were usedas the benchmarks. For the market model as the benchmark,the regression equation was:

AR(PR year + 1)

= β0 + β1PR + β2Y1 + ... + β7Y6 + β8MV1

+ ... + β11MV4 + β12B

M+ β13 Leverage + β14NMC

+β15HMC (2)

The explanatory variables are the same as in the main spec-ification. The regression results appear in Table 4. The resultsare similar in nature to the main specification. Specificallythe PR coefficient is negative, significant and varies around−6%. The years and leverage factor loading also proved tobe significant, but market capitalization, B/M ratio and me-dia coverage are not. Using the market return or Fama andFrench’s three-factors model, excess return as benchmarks(not displayed) also yield similar results to the main specifi-cation. Namely the PR coefficient keeps its significance andis between −4% and −6%. The results support the contentionthat the PR role in explaining next year abnormal return isstrong and unrelated to the benchmark used.

PR Year Realized Return

In addition to the proven effect of annual PR on subse-quent year return, estimating the effect of the annual PR on

same year return seemed desirable. The first regressions setwas repeated with the annual PR Year return as the explainedvariable. The regression was:

return (PR Year)

= β0 + β1Beta + β2PR + β3Y1 + ... + β8Y6

+β9MV1 + ... + β12MV4 + β13B

M

+β14 Leverage + β15NMC + β16HMC (3)

Table 5 displays the regression results. The PR coefficientis significant in all models ranging between −7% and −6%.The years’ factor loadings are also significant, but the size,B/M ratio and leverage are not. Both media coverage vari-ables’ coefficients are positive and insignificant (models 2and 4). Regressions using NMC and HMC separately yielda positive and significant NMC factor loading (model 5),which in line with the Fang and Peress results, while theHMC coefficient remains insignificant.

PR Year Abnormal Return

The second set of regressions were employed, first usingPR Year market model AR as the benchmark. The regressionwas:

AR (PR Year)

= β0 + β1PR + β2Y1 + ... + β7Y6

+β8MV1 + ... + β11MV4 + β12B

M+ β13 Leverage

+β14NMC + β15HMC (4)

The results are displayed in Table 6. The PR coefficient issignificant and in all models ranging between (−8%) and(−7%). The year’s factor loadings are significant too. Size,leverage, B/M and media coverage in its various combina-tions are not. Using the PR Year market return as a bench-mark (not displayed) also yields similar result to the mainspecification. The PR coefficient keeps its significance andis between −8% and −7%, and the B/M now is significant.

Robustness

Some of the robustness tests were conducted to alleviatethe concern of causality between PR and returns, and otherswere performed in order to differentiate the role of PR fromother media presence; further checks involved the industry,liquidity and using the PR volume itself as an explainedvariable. All of these tests reinforce the part PRs play inexplaining returns, as shown later.

Media Coverage

PRs and media coverage are similar in the sense that bothare media presence although PRs are media presence that isinitiated and controlled by the firm and not by outside media

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PR PREMIUM 49

TABLE 4Pr Year+1 Annual Market Model Abnormal Return Regressed on Pr, Years, Market Value, B/M Ratio, Leverage and Media

Coverage

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat

Intercept 0.06 3.01 0.05 1.82 0.04 1.39 0.02 0.62 0.03 1.04 0.04 1.48PR −0.06 −3.26 −0.06 −3.20 −0.06 −3.16 −0.06 −3.02 −0.06 −2.94 −0.06 −3.17Y1 −0.04 −1.40 −0.04 −1.39 −0.03 −1.02 −0.03 −1.02 −0.03 −0.94 −0.03 −1.03Y2 0.12 3.26 0.12 3.25 0.13 3.57 0.13 3.56 0.13 3.54 0.13 3.46Y3 −0.11 −3.42 −0.11 −3.41 −0.10 −3.12 −0.10 −3.13 −0.10 −3.10 −0.10 −3.15Y4 −0.06 −2.14 −0.06 −2.10 −0.06 −1.94 −0.06 −1.90 −0.05 −1.82 −0.06 −1.93Y5 −0.02 −0.73 −0.02 −0.73 −0.02 −0.80 −0.02 −0.78 −0.02 −0.76 −0.02 −0.81Y6 0.03 0.83 0.03 0.84 0.03 0.92 0.03 0.94 0.03 0.86 0.03 0.83MV1 0.01 0.32 0.01 0.43MV2 −0.02 −0.82 −0.02 −0.58MV3 0.03 0.96 0.03 1.10MV4 −0.03 −0.98 −0.02 −0.75B/M −0.06 −1.68 −0.06 −1.69 −0.06 −1.70 −0.06 −1.71Leverage 0.19 2.79 0.18 2.71 0.19 2.90 0.19 2.79NMC 0.02 1.02 0.02 1.02 0.01 0.59HMC 0.04 1.38 0.03 1.15 0.02 0.70

R2 3.6% 3.6% 4.2% 4.2% 4.2% 4.2%Obs. 1393 1393 1393 1393 1393 1393

Note. The table provides average yearly parameters estimates and their t statistics obtained through the OLS regressions. Variable description is in theunivariate analysis section.

channels. As was found in the univariate section, HPR firmsare also characterized by higher media coverage (Table 2).There is a large difference in the annual number of PRsversus the annual number of media articles in this sample.The media coverage presence is very low: 81% of the samplefirms had fewer than 5 media articles a year while only 2%of the firms had fewer than 5 PRs a year. The numbers arein favor PR. The average annual number of media articlesis 2.3 in the LPR group and 14 in the HPR group versusan average annual number of 25 PRs in the LPR group and133 in the HPR group. The average annual number of PRs is10–11 times higher than the average annual number of mediaarticles. Not just that the PR is greater in sheer numbers, butits very frequency helps retain its effect on public memory.

In the majority of regressions, media coverage factor load-ings are not significant. In Table 6 (model 5) the NMC factorloading is positive and significant. To make the case clearer,the main regression specification was repeated only this timefocusing on media coverage (PR was excluded). The regres-sion equation was:

return (PR year + 1)

= β0 + β1Beta + β2Y1 + ... + β7Y6

+β8MV1 + ... + β11MV4 + β12B

M+ β13 Leverage

+β14NMC + β15HMC (5)

The regressions results displayed in Table 7. The coef-ficients of the media coverage variables proved to be in-

significant. Thus, in this sample, media coverage does notcontribute to the explanation of the following year’s rate ofreturn. Possibly it stems from the time period in which therealized returns were measured. In this work, the time pe-riod is per year while in Fang and Peress it is per month.Measuring annual return seems too large a time period giventhe low annual number of media articles. When zooming onfirms with “no media coverage,” the relevance of PR can beseen more clearly due to the lack of overlapping between thetwo variables. Using the “no media coverage” indicator tofilter this 1,393 firm’s year sample yielded a subsample of636 firms (46%). The main regression specification was re-peated and not displayed. The PR coefficient still appears asnegatively affecting returns, although it is now −3% to −4%and. insignificant (the lack of significance here is possiblydue to decreased number of observations).

Omitting PR as an explanatory variable and using onlymedia coverage instead, was repeated using return (PR Year)as the dependent variable, the regression equation is:

return (PR year)

= β0 + β1Beta + β2Y1 + ... + β7Y6

+β8MV1 + ... + β11MV4 + β12B

M+ β13 Leverage

+β14NMC + β15HMC (6)

The results appear in Table 8. The media coverage vari-ables’ coefficients are insignificant when both are included;however, their directions are as expected, namely, the NMC

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TABLE 5PR Year Annual Return Regressed on Beta, PR, Years, Market Value, B/M Ratio, Leverage and Media Coverage

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat

Intercept 0.21 6.0 0.18 4.5 0.18 4.2 0.16 3.3 0.18 4.1 0.21 5.3Beta −0.05 −2.4 −0.05 −2.4 −0.05 −2.3 −0.05 −2.2 −0.04 −2.1 −0.04 −2.1PR −0.07 −3.3 −0.06 −2.7 −0.07 −3.2 −0.07 −2.9 −0.06 −2.8 −0.07 −2.9Y1 −0.12 −2.6 −0.12 −2.5 −0.11 −2.4 −0.11 −2.3 −0.11 −2.4 −0.12 −2.4Y2 −0.44 −11 −0.43 −10.9 −0.42 −10 −0.42 −10.4 −0.42 −10.7 −0.43 −10.7Y3 0.51 11.4 0.52 11.5 0.52 11 0.52 11.7 0.52 11.7 0.52 11.7Y4 −0.03 −0.9 −0.03 −0.7 −0.03 −0.8 −0.02 −0.7 −0.03 −0.7 −0.03 −0.8Y5 0.03 0.9 0.03 1.0 0.04 1.0 0.04 1.0 0.04 1.0 0.03 0.9Y6 0.09 2.5 0.09 2.5 0.09 2.6 0.09 2.6 0.09 2.6 0.09 2.6MV1 0.07 2.3 0.06 1.8MV2 0.05 1.4 0.04 1.0MV3 0.02 0.6 0.01 0.4MV4 0.04 1.1 0.03 0.9B/M −0.08 −1.9 −0.08 −1.9 −0.07 −1.7 −0.07 −1.7Leverage 0.10 1.3 0.10 1.4 0.10 1.4 0.10 1.3NMC 0.04 1.7 0.03 1.3 0.04 2.0HMC 0.00 0.0 0.01 0.4 −0.03 −1.0

R2 27.3% 27.4% 27.6% 27.6% 27.6% 27.5%Obs. 1393 1393 1393 1393 1393 1393

Note. The table provides the average yearly parameters estimates and their t statistics, obtained through OLS regression. Variable description is in theunivariate analysis section.

coefficient is positive and the HMC coefficient is negative.Repeating the regression with NMC and HMC separately,yield positive coefficient for NMC and negative coefficientfor HMC and both are significant. In that sense the mediacoverage effect found here is similar to the findings of Fangand Perress. However, their findings relate to the following

month’s return, while here the return is contemporaneousyear return.

Media coverage factor loadings significance in Table 8versus their insignificance in the presence of PRs (Table 6)can stem from the difference in their annual frequency. Assuggested in this work, more publications make a firm more

TABLE 6PR Year Market Model AR Regressed on PR, Years, Market Value, B/M Ratio, Leverage and Media Coverage

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat

Intercept 0.10 3.67 0.08 2.31 0.08 2.04 0.06 1.40 0.07 1.94 0.10 2.94PR −0.08 −3.82 −0.07 −3.30 −0.08 −3.77 −0.07 −3.33 −0.07 −3.28 −0.07 −3.44Y1 0.07 1.62 0.08 1.70 0.08 1.80 0.09 1.86 0.08 1.85 0.08 1.79Y2 −0.01 −0.28 −0.01 −0.19 0.00 0.08 0.00 0.13 0.00 0.06 0.00 0.00Y3 0.19 4.46 0.19 4.52 0.20 4.65 0.20 4.72 0.20 4.75 0.20 4.71Y4 −0.11 −2.94 −0.10 −2.77 −0.10 −2.76 −0.10 −2.61 −0.10 −2.65 −0.10 −2.80Y5 −0.01 −0.43 −0.01 −0.34 −0.01 −0.33 −0.01 −0.28 −0.01 −0.31 −0.01 −0.37Y6 −0.04 −1.21 −0.04 −1.15 −0.04 −1.13 −0.04 −1.09 −0.04 −1.07 −0.04 −1.12MV1 0.04 1.46 0.03 0.93MV2 0.02 0.65 0.01 0.30MV3 0.00 0.07 0.00 −0.15MV4 0.04 1.27 0.04 1.04B/M −0.07 −1.70 −0.07 −1.66 −0.06 −1.61 −0.06 −1.61Leverage 0.12 1.79 0.13 1.83 0.13 1.87 0.13 1.81NMC 0.04 1.53 0.03 1.38 0.04 1.72HMC 0.01 0.21 0.01 0.25 −0.02 −0.76R2 5% 5.2% 5.4% 5.3% 5.5% 5.4%Obs. 1393 1393 1393 1393 1393 1393

Note. The table provides the average yearly parameters estimates and their t statistics obtained using OLS regressions. Variable description is in the univariateanalysis section.

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TABLE 7PR Year+1 Annual Return Regressed on: Beta, Years, Market Value, B/M Ratio, Leverage and Media Coverage

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat

Intercept −0.32 −10.0 −0.34 −9.4 −0.33 −9.2 −0.37 −9.7 −0.36 −9.8 −0.35 −10.5Beta −0.07 −3.9 −0.07 −4.0 −0.07 −4.0 −0.06 −3.3 −0.06 −3.3 −0.06 −3.4Y1 0.04 1.3 0.04 1.2 0.04 1.2 0.05 1.5 0.05 1.6 0.05 1.5Y2 0.95 25.5 0.95 25.7 0.95 25.7 0.96 25.7 0.96 25.8 0.96 25.7Y3 0.46 15.1 0.46 15.1 0.46 15.1 0.47 15.1 0.47 15.1 0.47 15.1Y4 0.47 16.5 0.47 16.5 0.47 16.5 0.48 16.7 0.48 16.7 0.48 16.6Y5 0.59 21.3 0.59 21.3 0.59 21.2 0.59 21.2 0.59 21.2 0.59 21.1Y6 0.54 17.1 0.55 17.2 0.55 17.1 0.54 16.8 0.54 16.8 0.54 16.7MV1 0.03 1.0 0.03 1.0MV2 0.01 0.3 0.01 0.4MV3 0.04 1.4 0.04 1.5MV4 −0.01 −0.5 −0.01 −0.5B/M −0.01 −0.4 −0.04 −1.3 −0.04 −1.3 −0.04 −1.3Leverage 0.20 2.9 0.20 3.0 0.19 2.9NMC 0.02 0.9 0.01 0.7 0.01 0.7 0.02 1.0 0.01 0.8HMC 0.02 0.9 0.03 1.1 0.03 1.1 0.02 0.6 0.00 0.1R2 42.5% 42.5% 42.5% 42.9% 42.9% 42.9%Obs. 1393 1393 1393 1393 1393 1393

Note. The table provides the average yearly parameters estimates and their t statistics obtained through OLS regression. Variable description is in theunivariate analysis section.

visible, and in that sense the higher annual number of PRsovershadows the media coverage. Second, it is well knownthat the capital market professionals read press releases aspart of their daily work, and much of their trading decisionsare a response to these PRs. Media coverage potential read-ership is probably larger, but it is an open question how many

of them are motivated for trading as a response to these me-dia publications or how many are key decision makers. Inaddition, there is a major difference between printed mediaand online media. The Internet is becoming an integral partof life and is regarded as the most updated source of news—amust in the modern capital markets. This might explain why

TABLE 8PR Year Annual Return Regressed on Beta, Years, Market Value, B/M Ratio, Leverage and Media Coverage

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat Coeff. t Stat

Intercept 0.16 3.9 0.14 3.3 0.16 3.5 0.15 3.4 0.15 3.4 0.19 4.7Beta −0.05 −2.8 −0.06 −2.8 −0.06 −2.9 −0.05 −2.5 −0.05 −2.4 −0.05 −2.6Y1 −0.11 −2.4 −0.11 −2.3 −0.11 −2.4 −0.11 −2.3 −0.11 −2.3 −0.11 −2.4Y2 −0.43 −10.6 −0.42 −10.3 −0.41 −10.2 −0.42 −10.4 −0.42 −10.4 −0.42 −10.4Y3 0.52 11.5 0.52 11.5 0.52 11.6 0.52 11.8 0.52 11.8 0.52 11.7Y4 −0.02 −0.6 −0.02 −0.5 −0.02 −0.6 −0.02 −0.5 −0.02 −0.5 −0.03 −0.7Y5 0.04 1.0 0.04 1.1 0.04 1.2 0.04 1.0 0.04 1.0 0.04 1.0Y6 0.09 2.6 0.09 2.6 0.10 2.7 0.09 2.7 0.09 2.7 0.09 2.6MV1 0.04 1.2 0.05 1.5MV2 0.02 0.5 0.02 0.7MV3 0.00 0.0 0.01 0.2MV4 0.02 0.6 0.03 0.8B/M −0.06 −1.5 −0.07 −1.7 −0.07 −1.7 −0.07 −1.7Leverage 0.11 1.5 0.10 1.4 0.10 1.4NMC 0.05 2.0 0.04 1.7 0.04 1.7 0.05 2.0 0.06 2.7HMC −0.02 −0.5 −0.01 −0.3 −0.01 −0.2 −0.02 −0.6 −0.05 −1.9

R2 27.0% 27.0% 27.1% 27.2% 27.3% 27.1%Obs. 1393 1393 1393 1393 1393 1393

Note. The table provides the average yearly parameters’ estimates and their t statistics obtained through OLS regression. Variable description is in theunivariate analysis section.

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online-distributed PRs are found to be more effective thanmedia coverage, which is printed. When using a subsamplefiltered by the “no media coverage” indicator (not displayed),the PR coefficient keeps its size and significance, and rangesbetween −7% and −6%, despite the reduced number of ob-servations.

Due to lack of space the other issues are briefly described.7

Causality

According to Granger’s [1969] causality approach, it wasimportant to check how much of the current return can beexplained by past values of returns, and whether or not addinglagged value of PR can improve this explanation. Next, therest main specification variables were added gradually. Theregression equation was:

return (PR year + 1)

= β0 + β1 ∗ Return(PR Year)

+β2 ∗ Return(PR Year − 1) + β3PR + β4 ∗ Beta

+β5Y1 + ... + β10Y6 + β11B

M+ β12 Leverage

+β13NMC + β14HMC (7)

Though very small, PR Year return alone has some explana-tory power for next year return (R2 = 0.4%). Adding PRmore than doubled the explanation power (R2 = 0.9%). Whenadding the years’ variables the explanatory power improveddramatically (R2 = 42.9%). However, the lagged return islosing its former significance while the PR coefficient re-mains intact and keeps its significance and size of -0.06. Theopposite direction, namely whether return or excess returnin a certain year can shape the next year’s PR volume, wasexamined. The results were that the return or excess returnfactor loadings (not displayed) in all regressions were in-significant, which indicate that return—or excess return—ina certain year does not affect next year firms’ PR strategy.

It was also examined how frequently firms changed theirPR policy in the sample years. It was found that 88.3% offirms kept their PR policy along the years. In other words, thevast majority of firms in both the LPR and HPR groups willremain in their respective group over the years. Only 9.8% ofthe firms shifted from their reference group to the excludedgroup (between LPR and HPR) and 1.8% switched groups(from LPR to HPR and vice versa). This result highly pointsout that PR policy is steady along the years and probably notinfluenced by returns or any other exogenous variables. Thesame issue was examined using not only firms’ level but alsoaggregate level, using the following regression:

monthly Average PR(t)

= β0 + β1 ∗ Sector return(t − 1) (8)

The monthly average PR number was regressed on previ-ous period monthly sector return. As a sector return, the Nas-daq Composite and NYSE Arch 100 Index alternately were

used. Both sector returns factor loadings (not displayed) werenot significant. Hence sector returns in a certain month doesnot explain the PR activity of the next month. In conclusion,causality proved to be unidirectional. The PR volume affectsstock return, but not vice versa.

PR Volume

Despite what is said in note 4, more regressions (not dis-played) were conducted using the PR volume itself as an ex-planatory variable. When the Year in focus was PR Year+1,the sheer number of press releases volume proved to be in-significant. However, when the year in focus was PR Year,the sheer PR number did have explanatory power for thereturns. According to the results each 10 additional PRs de-crease stock performance by 0.28–0.34%.

Industry Effect

The sample firms belong to the technology sector and inthat sense they are comparable. However, firms were furtherclassified by industry to test the effect on returns. The sam-ple firms were classified into industries and regressions (notdisplayed) that include industries as dummy variables wereconducted. The industry variables appear to be significant.Still, the PR variable coefficient keeps its significance andformer magnitude: −5% to −4% when the year in focus isPR Year + 1, and −8% to −7% when the year in focus isPR Year. In summary, the role of the PR is major and keeps itslevel even after adding industry as an additional explanatoryvariable.

Liquidity

A growing number of papers already pointed out the neg-ative relation between liquidity and stock returns, for ex-ample, Amihud and Mendelson [1986], Brennan and Subra-hamayam [1996] and Diamond and Verrecchia [1991]. In thiswork, a prominent result in the univariate analysis showedthat the trading volume of the HPR group as three timeshigher than that of the LPR group. Namely the HPR stocksare much more liquid. In order to monitor the relevance ofliquidity when explaining returns, the main regression speci-fication was employed adding Trading Volume as another ex-planatory variable. When the year in focus is PR Year+1 thetrading volume coefficient in positive (in contrast to the neg-ative relation found in the literature) and significant. Whenthe year in focus is PR Year, the coefficient is negative andinsignificant. In both cases the coefficients are very small(1.60E-07 and -4.09E-08, respectively) and hence have anegligible explanation for the return. What is more impor-tant is that the PR coefficient remains intact. The rest of thecoefficients are similar to the main regression specification.

The main and robust results prove the research’s hypoth-esis H1, that firms that employ high PR strategy yield lowerrates of return in their stock in the same and in the following

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year compared with firms that employ low PR strategy. Themagnitude of the difference is 4–5.6% for the following yearand 7–8% for the PR Year itself. Again, this gap in return isthe “PR Premium.”

REASONING THE RESULTS AND THE “PRPRIMING” EFFECT

The first main finding was that the average annual returnof the HPR firms was substantially lower than that of theLPR firms. The second finding was that the average dailytrading volume of HPR firms was three times higher thanthe LPR firms. The lower returns of HPR firms can perhapsbe reasoned by initial overpriced stocks; however, their stillpopular purchase might be due to the firm’s perceived quality,a possible side effect (intentional or not) of high PR volume.While investors are willing to pay a high price for a stockwherever perceived quality is high, ultimately its low returnis against their interest. It is noted also that from the firm’sperspective, a higher stock price is actually in their favor,for example, for lowering the cost of equity capital. Both thewillingness of investors to be compensated with lower re-turns and the high trading volume of HPR firms’ stocks canbe explained by using the behavioral finance theory, whichsuggests, among others, why market participants make sys-tematic errors and market inefficiencies. PR Premium is anexcellent example for such a systematic error. Investor over-confidence, overoptimism, mimicry (herding instinct) and soon may all be fueled by PR volume. Just to illustrate, considerfew key factors that can be affected by PR volume:

1. Investor Awareness – Inevitably, HPR firms enhancedtheir public awareness through high PR volume. Wher-ever investors rely on reading published informationand compare multiple options for stocks purchase, afirm that stands out might attract purchase of its stock.While PR volume alone will not suffice as a buyingreason, in a side by side stock scenario the compari-son will not be unbiased as the decision maker is moreaware, or primed by, the firm with the most press re-leases.

2. Reliability and Strength – The high PR volume canbe interpreted as signaling by the firm of long termstrength, stability and growth. This adds to the in-vestors’ peace of mind. High PR volume is perceivedas extrovert behavior on part of the company. This sug-gests self confidence and inner strength, whereas an in-trovert company might not increase investors’ comfortlevels.

3. Justification – The individual investor seeks confi-dence, beyond the assurance of the raw data, in makingbuying decisions. Just as well the institutional investor,when faced with colleagues and superiors, needs to jus-tify these decisions plus relay the immeasurable data

that led to his or her confidence. It is easier to jus-tify investment decisions when it comes to firms withhigher awareness in everyone’s mind. Wherever a firmis well known to all, the justification process is easier.

4. Support of Branding Effect – Intense marketing expo-sure leads to branding effect, where customers decisionto buy products is not entirely based on rational pro-cess if they were primed by advertisement. It is similarto the well-known phenomenon that people are willingto pay more for what they perceive as nicer: Firms thatcause lower pollution and hence are more “green” showhigher financial valuation, see King and Lenox [2008];people that are ranked as nicer earn more, see Markusand Rosenblatt [2006]; and people are willing to paymore for “green” products, see Miller [2007]. Pressreleases often include some sort of advertisement, self-praise, slogans and so forth, albeit sometimes hiddenor even unintentional. Therefore, the advertising sideeffect of press releases definitely supports the brandingefforts of companies (intentional or not), and the pressreleases are an affordable and unregulated platform.Thus, a high PR volume is a clear multiplier for sucha side effect as it is positively related.

The underlying mechanism of the above explanations is pos-itive priming. As defined by Reisberg [2007], positive prim-ing is thought to be caused by spreading activation. The firststimulus activates parts of a particular representation or as-sociation in memory just before carrying out an action ortask. The representation is already partially activated whenthe second stimulus is encountered, so less additional acti-vation is needed for one to become consciously aware of it.It is suggested that the PR volume factor, as found in thisresearch, is a true positive primer for investors. The annualPR volume directly impacts the spreading activation. HighPR volume decreases the spreading and increases the impactof the activation in the investors’ mind. Each press releasecontributes to the activation which in our scenario is the cre-ation of a perceived quality of the firm, and as the PR volumeis high less memory fade effect of the idea takes place. Asthis research points out, the more an investor is PR primedfor a particular stock, the more buying that stock is probable.

SUMMARY

Multiple factors are known to explain stock returns. Risk,firm size and B/M ratio are all well-documented examples.The present research highlights the number of counted pressreleases per annum, or PR volume, as a proxy for disclosurelevel and a possible additional factor explaining stock re-turns. Firms were sorted by press release volume and then di-vided into three groups according to their annual PR volume:LPR covered the lower end, HPR the highest (the rest were

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omitted). Analysis was applied in order to examine whetherPR volume is a relevant factor.

The empirical study drew two main findings. The firstshowed a substantial difference in annual stock returns be-tween the LPR and HPR groups in favor of the former. Theaverage annual return of the LPR firms was higher than theHPR firms’ by 4–5% for the PR Year +1 (PR Year is the yearin which the PR were counted) and 6–7% for the PR Year.This result was established after taking into account a hordeof necessary controls such as beta, firm size and others. Thesecond finding showed that the average trading volume ofHPR firms was as much as a three times higher than thatof the LPR firms, both in the PR Year and in the followingyear. These findings illustrate the impact of PR volume onsteering the investors’ interest and decisions; PRs are seen aspart of the investor priming and support the conclusion thathigher perceived company image bias and boost financialtrading activity and stock pricing, which in turn negativelyaffects stock rates of return in the contemporaneous and inthe following year.

The lower rates of return can be considered a positiveoutcome for some and a negative outcome for others: A firmis interested in lowering its cost of equity capital. Investorson the other hand expect to have the proper return for risktaken. PR Premium is in a sense a market inefficiency or atleast a systematic error as explained within the frameworkof behavioral finance. As psychology affects the behavior offinancial market participants not all decision making is ra-tional, and is subjected to emotional motives as exploited byPR volume, whether intentional or not. While the individualinvestor emotional impact cannot be measured or predicted,this study did manage to bring forth the PR volume as a sig-nificant factor towards calculating future return. The researchresults are of special interest to PR strategists and decisionmakers as well as to the investor community.

NOTES

1. The AMIR index covers firms with an average MarketValue of $4.9B. That is by far nonrepresentative to themarket as a whole.

2. Using 2 group tests is a common convention in litera-ture. To mention a few: (1) In the three factors modeldeveloped by Fama and French the HML is definedas the return on a portfolio of high book-to-marketstocks, less the return on a portfolio of low book-to-market stocks. The first portfolio represents the top30% of all firms on COMPUSTAT database and thelatter contains firms in the lowest 30% of it. (2) Inthe Goldfeld–Quandt test for homoscedasticity the ob-servations scope is divided into three groups and themiddle group is omitted.

3. When considering which beta to use as control forrisk, there were pros and cons for using beta1 versus

using beta3. Up to date observations (beta1) are morepreferable however the higher frequency samplingsof beta1 are more noise sensitive. While beta3 esti-mates are indeed based on less frequent observationsthey do include PR Year and therefore the beta3 wasselected.

4. When viewing PR volume as an attention getter, oneshould keep in mind that the PR number is weightedrelative to similar firms’ PR volume. In other words,even a low PR volume can stand out in an environ-ment where adjacent firms have lower or no PR vol-ume at all, creating an impact perhaps similar to oneof a firm with very high PR volume in an environmentof high PR volumes. That is why the sheer numberof PR in itself is often meaningless; it becomes moremeaningful only when compared with others in thesegment.

5. The negative beta factor loading needs some extra clar-ification. As beta is a measure for asset systematic riskin relation to the market, the higher the beta the higherthe stock sensitivity relative to the market. As a result,a stock with a beta > 1 declines more than the mar-ket in bearish years and rises more in bullish ones. Ascan be seen in Table 2, the estimated betas are higherthan 1 for the LPR and HPR groups (1.44 and 1.6,respectively). Hence, as the beta itself is positive, onewould expect that the beta factor loading will be nega-tive in bearish years and positive in bullish ones. Thus,the main regression specifications were conducted foreach year separately and it was shown that this is thecase.

6. Repeating the regressions using beta1 (not displayed)instead of beta3 yielded similar results. It appears thatusing beta1 instead of beta3 is not an issue here.

7. Tables will be provided upon request.

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Botosan, C. “Disclosure Level and the Cost of Equity Capital.” The Ac-counting Review, 72, (1997), pp. 323–350.

Botosan, C. and M. Plumlee. “A Re-examination of Disclosure Level andthe Expected Cost of Equity Capital.” Journal of Accounting Research,40, (2001), pp. 21–40.

Brennan, M. and A. Subrahmanyam. “Market Microstructure and AssetPricing: On the Compensation for Liquidity in Stock Return.” Journal ofFinancial Economics, 41, (1996), pp. 441–464.

Bushee, B. and C. Noe. “Corporate Disclosure Practices, Institutional In-vestors, and Stock Return Volatility.” Journal of Accounting Research,38, (2000), pp. 171–202.

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Diamond, D. and R. Verrecchia. “Disclosure, Liquidity and the Cost ofEquity Capital.” Journal of Finance, 26(1), (1991), pp. 271–283.

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Froidevaux, E. A. “Investor Relation Internet Disclosure and the Cost of Eq-uity Capital.” Doctoral dissertation. University of Fribourg, Switzerland,2004.

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APPENDIX: Examples of PR Disclosure

The goal of the PR examples is to demonstrate the variabilityof the contents’ nature.

Example 1: PR which is “social” in nature

“Magyar Telekom (MTA) wins Family-Friendly Work-place AwardBudapest, February 25, 2009Magyar Telekom has won the Family-Friendly WorkplaceAward. The company received this recognition in the largeenterprise category of the competition invited by the Min-istry of Social Affairs and Labor. Magyar Telekom, as anemployer committed to the principles of sustainability, sup-ports the need to create balance between family and work-place, to ensure diversity at the workplace and equal opportunities. . . .”

Example 2: PR which is qualitative in nature

Eli Ayalon, DSP Group CEO, to Chair Likud ElectionCampaign

SAN JOSE, Calif., Nov. 22 /PRNewswire-FirstCall/ – Eli Ay-alon, Chairman and Chief Executive Officer of DSP Group,Inc. (Nasdaq: DSPG), a worldwide leader in developing andproviding solutions for the residential wireless market, hasannounced that he intends to accept the position of Chair ofthe Likud Campaign Committee for the Israel Knesset elec-tion scheduled to be held on February 10, 2009. Mr. Ayalonwill continue as Chairman of DSP Group, but in light of thedemands of his position as campaign chair he will take atemporary leave of absence in performing the functions ofChief Executive Officer. . ..”

Example 3: PR that contains “hard” information

“Symantec Delivers Solid Fiscal Third Quarter Operat-ing Results

Demand for Mission-Critical Solutions with Near-Term ROIDrove Results

CUPERTINO, Calif. – Jan. 28, 2009 – Symantec Corp.(Nasdaq: SYMC) today reported the results of its third quar-ter of fiscal year 2009, ended Jan. 2, 2009. GAAP revenuefor the quarter was $1.51 billion and non-GAAP revenue was$1.54 billion. Symantec delivered stronger than expected re-sults on all of its key financial metrics in the fiscal thirdquarter. . ..”

Quarterly Results

GAAP Results: GAAP net loss for the third quarter was$6.81 billion compared with net income of $132 million forthe same quarter last year. GAAP diluted loss per share was$8.23 compared with diluted earnings per share of $0.15 forthe same quarter last year. . ..”

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