The mixed theoretical and empirical evidence leads to the following two hypotheses:

We examine the association between the quality of private information and cost of equity capital, after controlling for market beta, earnings growth, firm size, book-to-price and the quality of public information. We employ separate empirical proxies for the precision of individual analysts’ private and public information sets, to capture the quality of private and public information, respectively. Our proxy for the cost of equity capital is the internal rate of return that equates current stock price to analysts’ forecasts of future dividends and target prices

The study most related to our research is Easley, Hvidkjaer, and O’Hara (EHO) (2002). EHO examine the association between average realized returns (their proxy for cost of equity capital) and the probability of information-based trading (PIN) (their proxy for private information), after controlling for market beta, firm size and book-to-price. While EHO document a strong positive association between PIN and averaged realized returns, none of the coefficients on their other risk proxies is consistent with expectations, suggesting that their dependent variable may not be a reliable proxy for cost of equity capital. Moreover, EHO do not control for cross-sectional differences in public information in their analysis.

The remainder of our paper is organized as follows. Section 1 develops our hypotheses with reference to applicable prior research. Our research design and the empirical proxies employed in our analyses are described in Section 2. Section 3 outlines our sample selection procedures and presents descriptive statistics pertaining to our sample. The results of our analyses are found in Section 4, and Section 5 concludes the paper and offers suggestions for future research.

We include market beta, growth, firm size, book-to-price, and the precision of public information in our model to control for sources of risk that could confound our analysis and to validate our proxy for cost of equity capital. The Capital Asset Pricing Model indicates that cost of equity capital is increasing in market beta. Accordingly, we expect the coefficient on BETA to be significantly positive. Beaver, Kettler and Scholes (1970) argue that abnormal earnings streams derived from growth opportunities are more risky because they are subject to erosion as competition enters the market. This suggests a positive association between expected long-term earnings growth and cost of equity capital. Berk (1995) argues that, unless the empirical model for expected returns includes all risk factors, a negative associationbetween firm size and expected returns should be observed as market value is inversely associated with risk in general. Accordingly, we expect the coefficient on firm size (measured by the market value of equity) to be negative. Moreover, Berk (1995) argues that book-to-price inversely proxies for firm size and, consequently, we expect the coefficient on BP to be positive. Finally, a relatively large body of research suggests that greater public disclosure is associated with a lower cost of equity capital through greater liquidity or reduced estimation risk. Thus, we expect the coefficient on RPUBLIC to be negative.

2.2.2 Market Beta (BETA)

Market beta is estimated using the market model with a minimum of 30 out of 60 monthly returns and a market index return equal to the value weighted NYSE/AMEX return. We obtain the data to estimate BETA from CRSP. The estimation period for BETA ends on June 30th of the year rDIVPREM is estimated.

2.2.4 Firm Size (LMKVL)

Our proxy for firm size is the market value of equity. We estimate market value of equity by multiplying the number of common shares outstanding by stock price at the quarter-end immediately prior to June 30th of the year rDIVPREM is estimated. We draw these data from the quarterly Compustat tape. If these data are unavailable, we substitute the market value of the firm reported on CRSP as of June 30th of the Value Line publication year. Market value of equity is stated in millions of dollars. We use a natural log transformation of the data to mitigate skewness in the distribution of market value of equity.

2.2.3 Book-to-Price (BP)

We compute book-to-price by scaling the common equity of the firm by the market value of equity. Both the numerator and the denominator of the ratio are measured at the quarter-end immediately prior to June 30th of the year rDIVPREM is estimated. We collect these data from the quarterly Compustat tape. If these data are unavailable, we substitute data for the fiscal year-end immediately prior to June 30th of the year rDIVPREM is estimated. These data are collected from the annual Compustat tape.

Mean (median) BETA for our sample is approximately 1.07 (1.01). These data indicate that our average sample firm is relatively risky, whereas our median sample firm presents a level of market risk similar to that of the market portfolio. Mean (median) expected long-term growth in earnings (GROW) is 17.1% (14.5%). These growth statistics are similar, albeit higher, than the 15.1% mean and 13.7% median long-term growth in IBES earnings documented by Gode and Mohanram (2002) for an earlier time period. Mean MKVL is $8225.7 million; the median is $2286.0 million, which indicates a sample populated by relatively large firms and a skewed distribution. Mean (median) book-to-price (BP) equals 0.45 (0.39). This indicates that firms trading at a substantial premium above book value characterize our sample.

Consistent with the different theoretical views, the empirical evidence is mixed on the relation between more frequent disclosures and the cost of equity. Botosan (1997) finds a negative relation between her self-constructed disclosure index and the firm’s cost of equity for firms with low analyst following but not for firms with high analyst following. Botosan and Plumlee (2002) and Francis et al. (2008) document that the relation between the cost of equity and voluntary disclosures varies across different disclosure measures. Specifically, Botosan and Plumlee (2002) find that the cost of equity decreases in the annual report disclosure level but increases in the quarterly report disclosure level. Their interpretation is that more detailed quarterly reports attract the attention of transient investors, whose trading activities elevate the cost of equity by increasing the return volatility. Francis et al. (2008) show that the cost of equity is negatively related to the disclosure measure based on annual reports and 10-K filings,

positively related to disclosure measures based on management forecasts and conference calls, and unrelated to press-release based disclosure measures. In sum, the mixed theoretical and

empirical evidence suggests that whether financial reporting frequency affects the cost of

equity remains an intriguing empirical issue.

Botosan and Plumlee (2002)

Froot, Perold and Stein All else being equal, anincrease in volatility leads investors to demand higher returns from their shares as compensation for the added risk. From the perspective of the corporation,this translates into a higher cost of capital that must be used when evaluating prospective investments, thus reducing the aggregate level of investment

Bushee and Noe

Having done so, he is then able to classify institutions into three types: dedicated, transient and quasi-indexer. Dedicated institutions tend to trade infrequently and hold large positions in a few firms. Transient institutions are characterized by having small ownership stakes in several firms and turning over their portfolios frequently. Quasi-indexers hold large well diversified portfolios and do not trade frequently.

However, Hughes, Liu, and Liu (2007) prove that this result does not hold when the economy becomes large, as more information may only affect the (aggregate)market premium but not a firm’s cost of capital directly. They prove that information about the systematic factor is the only information priced by the market. Lambert, Leuz, andVerrecchia (2007) derive whether the presence of additional information in a multi-asset economy would increase or decrease cost of capital. They find that a disclosing firm has a lower cost of capital than this same firm in the economy prior to disclosure; however, after disclosure has occurred, a disclosing firm may

have a higher cost of capital than a firm that did not disclose. Armstrong, Banerjee, and Corona (2008) also consider a multi-firm model where information quality affects cost of capital through systematic risk. They show that observed beta and information quality are negatively related for positive beta stocks but positively related for negative beta stocks. One problem, noted by Christensen, De la Rosa, and Feltham (2008), is that, if there are no real decisions, disclosure should only affect the timing of resolution of uncertainty, and thus a commitment to disclosure does not increase welfare of the manager disclosing or, even, that of investors.

, Banerjee, and Corona (2008) We show that both systematic and firm-specific information quality decreaseexpected returns for positive beta stocks but increase expected returns for negative betastocks - this is what we call the “beta” effect of information quality.

An alternative stream of the literature considers the effects of incomplete, but symmetric, information on asset prices. Building from Merton (1987), a number of authors (see, e.g., Basak and Cuoco (1998) and Shapiro (2002, p. 49)) analyze asset pricing when traders can be unaware of the existence of some assets. In this setting, cross-sectional differences in returns can emerge simply because traders cannot hold assets they do not know about; the lack of demand for these unknown assets results in their commanding a higher return in equilibrium.2 T

On the other hand, it is not clear whether the predictions from these single-firm model should survive in a setup with multiple firms and diversifiable risk. In a multi-asset, noise rational expectations model, Hughes, Liu and Liu (2007) show that information does not have any cross-sectional effects on the cost of capital. This is because firm-specific information idiversified away and systematic information affects factor risk-premia but not factor loadings in large economies. Moreover, other empirical work finds little or no evidence of a negative relation between cost of capital and information quality. For example, Botosan (1997) finds no evidence of a relationship between disclosure and the cost of capital for her full sample of firms and only weak evidence of a negative relationship for the sub-sample of firms with low analyst coverage. Botosan and Plumlee (2002) show that only the sub-component of AIMR scores related to annual report disclosures has a modest negative association with the cost of capital. Core, Guay and Verdi (2008) test Lambert, Leuz and Verrecchia’s (2007)prediction that asymmetric information effects should be diversified away, and empirically document that accruals quality is not a priced risk factor. Similarly, Duarte and Young(2007) decompose the PIN measure into asymmetric information and liquidity components and show that only the liquidity component is priced. It is important to note that since information quality is not a priced risk factor in our model, but affects expected returns through factor loadings, the results in these papers are not inconsistent with our theoretical predictions or empirical results.

On the other hand, Hughes, Liu and Liu (2005) and Lambert, Leuz and Verrecchia (2005) show that in a large economy the effect of asymmetric information on expected returns is diversifiable. They argue that asymmetric information is priced in Easley and O’Hara (2004) because the number of assets in their model is finite and hence asymmetric information risk cannot be diversified away. In spite of the fact that private information should be diversifiable in a large economy, empirically a proxy for information asymmetry, PIN, is positively and significantly related to average stock returns.

Duarte and Young(2007)

This paper examines whether PIN is priced because of information asymmetry or because of other liquidity effects that are unrelated to information asymmetry. Our starting point is a model that decomposes PIN into two components, one related to asymmetric information and one related to illiquidity. In a two-pass Fama-MacBeth regression, we show that the PIN component related to asymmetric information is not priced, while the PIN component related to illiquidity is priced. We conclude, therefore, that liquidity effects unrelated to information asymmetry explain the relation between PIN and the cross-section of expected returns.

Armstrong, Banerjee, and Corona (2008)

We show that an increase in information quality of either type (systematic or firm-specific) has two effects on a firm’s expected re turns: (i) a “beta” effect which decreases expected returns when beta is positive, but increases expected returns when beta is negative, and (ii) a “convexity” expect which generally increases the expected returns.

Unlike single firm models which generally predict a negative relationship between the two, our model predicts a non-monotonic relationship between expected returns and information quality. However somewhat surprisingly, the effect of both types of information quality are similar. We show that both systematic and firm-specific information quality decrease expected returns for positive beta stocks but increase expected returns for negative beta stocks - this is what we call the “beta” effect of information quality. There is also a direct, “convexity” effect of information quality which may increase or decrease expected returns. We show that the convexity effect of systematic information quality unambiguously increases expected returns, by lowering aggregate uncertainty and hence increasing the risk-free rate. However, the convexity effect for firm-specific information quality is positive only when systematic IQ is low, and negative otherwise.