Disclosure quality and Stock returns in the UK
Sulaiman Mouselli
Forthcoming
Journal of Applied Accounting Research
2010
Abstract
Purpose: The purpose of this paper is to update and re-examine the role of corporate narrative reporting in improving investors’ ability to better forecast future earnings change. We also construct a risk factor for disclosure quality (DQ) and test whether such a factor is useful in explaining the time-series variation of UK
Design/methodology/approach: We use the return-future earnings regression model to update and re-examine the value relevance of disclosure quality for investors. We also construct a DQ factor and add it to Fama-French three-factor model. This is undertaken in order to investigate the usefulness of such a factor in explaining the time-series variation of UK
Findings: Our paper contributes to the market based accounting research in three crucial ways. Firstly, it offers updated evidence on the usefulness of corporate narrative reporting to investors. Secondly, it offers evidence that the DQ factor is a significant risk factor in the UK
Practical implications: Our results suggest that narrative reporting contains value relevant information for the stock market. Therefore, regulators should think about asking companies to produce compulsory narrative sections (i.e. operating and financial reviews) in their annual reports.
Originality/value: To the best of our knowledge, our paper is the first to construct and add a DQ factor in the original Fama-French factors.
Keywords: Narrative disclosure; Future oriented information; Value relevance; Fama-French factors; Stock return; United Kingdom
Classifications: Research paper
1. Introduction
There is a fundamental link between accounting information in general, and disclosure quality (DQ) in particular, with regard to the cost of equity capital. In principle, disclosure turns private information into public information. Hence, a higher disclosure level is expected to reduce the cost of equity capital. However, there is still a great level of controversy, not only on the channels where disclosure quality affects stock returns, but also on the scarce empirical evidence to support the association between disclosure quality and stock returns.
Previous research suggests two possible channels where disclosure quality affects stock returns. The first channel is based on stock liquidity and has no direct link to asset pricing models (see, for example, Diamond and Verrecchia, 1991). However, the second channel assumes that disclosure quality, as a proxy for information risk, affects stock’s beta and therefore its expected returns (see for example Barry and Brown (1985), Coles, Loewenstein and Suay (1995)).
Recent studies suggest that information risk is a non-diversifiable risk that cannot be captured by stock’s beta only (see O’Hara, 2003; Easley and O’Hara, 2004; and Leuz and Verrecchia, 2005). We take this one step further and suggest additional risk factors to capture information risk related to disclosure quality similar to Francis et al. (2005), and Core et al. (2008), who advocate a risk factor to capture information risk that is related to accruals quality.
This paper builds on prior research that investigates the importance of disclosure quality for stock market participants. In order to be thorough, we re-examine the value relevance of future oriented earnings statements in the annual report narratives. In particular, we re-examine the degree to which these statements improve investors’ ability to better anticipate future earnings. We expand upon, and update prior research in the UK Walker UK
Our paper adds to the market-based accounting literature in two crucial aspects. Firstly, consistent with theories that demonstrate a role for information risk in asset pricing, this study investigates the relation between disclosure quality and stock returns for a large sample of firms over the period July 1997 to June 2004. We show that firms with poor disclosure quality have higher costs of capital than do firms with good disclosure quality.
Secondly, Fama and French (1993, 1996) show that risk factors constructed on the basis of book-to-market (HML) and market value (SMB) are incrementally important in explaining the time-series variation of US UK
The paper proceeds as follows. Section 2 discusses the theoretical background. Section 3 discusses our disclosure measure. Section 4 describes our research methods. Section 5 describes the data. Section 6 presents our empirical findings. Section 7 concludes.
2. theoretical background
The theoretical research provides two possible channels at which disclosure quality could affect stock returns. First, researchers, i.e. Diamond and Verrecchia (1991) and Espionsa and Trombetta (2007), argue that a greater disclosure should increase stock liquidity and reduce its risk either by reducing transaction costs or increasing the demand on stock, and consequently reducing the expected returns on the stock.
The second channel at which disclosure level could affect stock returns includes Barry and Brown (1985), Coles and Loewenstein (1988), Handa and Linn (1993), Coles et al. (1995) and Clarkson et al. (1996). They argue that better disclosure quality will reduce the potential investors’ estimation risk about the parameters of a stock’s future return or payoff distribution. That is, investors attribute more systematic risk to an asset with low information compared to an asset with high information. Both channels can be aligned under the concept of information asymmetry, however only the latter assumes that impact of disclosure quality on stock returns works through stock’s beta.
Recent studies suggest that information risk is a non-diversifiable risk that cannot be captured by stock’s beta only (see O’Hara, 2003; Easley and O’Hara, 2004; and Leuz and Verrecchia, 2005). For example, Easley and O’Hara (2004) show that more public information reduces the risk to uninformed traders holding the stock. They argue that investors require a higher return to hold stocks with less public information. They further suggest that disclosure is priced because informed investors can adjust their portfolios to incorporate good news while uninformed investors cannot.
Furthermore, Kang (2004) studies the relation between disclosure and stock returns. He derives disclosure risk premium to measure the differences in stock returns by comparing a case in which information asymmetry exists with the other case where there is no information asymmetry. He finds that firms with bad disclosure history will have higher disclosure premium in their stock returns.
Traditional asset pricing theory (i.e. Fama, 1991) considers information risk as a diversifiable risk and consequently discards any impact of it on stocks’ expected returns. However, Easley and O’Hara (2004) argue that information risk is non-diversifiable because uninformed investors cannot modify their portfolio weights in a similar manner to that of informed investors. More recently, Francis et al. (2005) and Core et al. (2008) suggest a risk factor based on accruals quality as a source of information risk.
In this paper, we suggest a different proxy for information risk built on the basis of disclosure quality that uses the number of future oriented earnings statements in annual report narratives as a measure of disclosure quality. We argue that the DQ factor is a systematic risk factor that captures information risk. Hence, we expect the DQ factor to be a significant risk factor in pricing stock returns. We test this prediction empirically, on the UK
3. Disclosure Quality measure
The concept of disclosure quality is very difficult to assess. This is because it refers to the degree to which current and potential investors can read and interpret the information easily (Hopkins
Our DQ scores mainly capture the quantity of future oriented statements. We acknowledge the fact that it is not an easy task to explicitly measure the quality of corporate disclosure. In addition, disclosure quantity alone is not a satisfactory proxy for disclosure quality. However, in a recent article, Beretta and Bozzolan (2004) propose a framework for measuring disclosure quality. They argue that “Quality of disclosure depends both on the quantity of information disclosed and on the richness offered by additional information. While the quantity of disclosure has been discussed in previous literature, little attention has been paid, until now, to the richness of the information in quality. In our view, semantic properties of disclosures about future prospects, that is, the richness—determines whether or not the information helps outside investors appreciate the expected impact of disclosed risks on the firms’ capability to create value” (page 266).
Based on the framework proposed by Beretta and Bozzolan (2004), we use the quantity and richness of future oriented disclosures as a proxy for the quality of future oriented disclosures. We measure disclosure quantity by counting the number statements containing future prospectus. We use good news information as a proxy for the information richness criterion. This is because good news statements are more likely to help investors to better forecast firm’s future prospects.
Prior research shows that good news information in the annual reports dominates bad news information. For example, Bujaki et al. (1999) find that good news disclosures account for 97.5%, while 2.5% of future oriented information is bad news. This result is consistent with the findings in Clarkson et al. (1992 and 1994) and Clatworthy and Jones (2003). Clarkson et al. (1992 and 1994) find that managers tend to publish favourable future oriented information in their annual reports. The findings in Clatworthy and Jones (2003) suggest that UK
We adopt the same measure of disclosure quality developed in Hussainey et al. (2003). They generate their disclosure scores for a large sample of UK
Like Hussainey et al. (2003), we estimate the DQ score for our sample in three steps. In the first step, we search the narrative sections of annual reports for future oriented information. We use the list of future oriented information keywords created by Hussainey et al. (2003, p. 277). This list includes thirty-five keywords as follows: accelerate, anticipate, await, coming (financial) year(s), coming months, confidence (or confident), convince, (current) financial year, envisage, estimate, eventual, expect, forecast, forthcoming, hope, intend (or intention), likely (or unlikely), look forward (or look ahead), next, novel, optimistic, outlook, planned (or planning), predict, prospect, remain, renew, scope for (or scope to), shall, shortly, should, soon, will, well placed (or well positioned), year(s) ahead. Similar to Hussainey et al. (2003) we also take account of future year numbers in the list of future oriented keywords. In the second step, we identify the relevant information to the stock market in assessing the firm’s future earnings. For the purpose of the current paper, we use the same list created by Hussainey et al. (2003, p. 280) that is related to earnings indicators. The list contains the following twelve keywords benefit, breakeven, budget, contribution, earnings, eps, loss, margin, profit, profitability, return and trading. Finally, we use QSR N6 to count the number of sentences that include a minimum of one future oriented keyword and one earnings indicator, and consider this number our measure of DQ score.
4. Research Methods
4.1. The Value Relevance of Disclosure Quality
The article by Collins et al. (1994) is a response to Lev (1989), who notes that the association between returns and current earnings is relatively weak. They investigate two potential factors contributing to the low contemporaneous return-earnings association. One of these factors is earnings’ lack of timeliness in capturing value-relevant events. To capture the intuition that prices lead earnings, they expand the simple return-earnings regression to include future earnings growth variables. Collins et al. (1994: 295) motivate their regression model by assuming the following return-generating process:
Where:
k is limited to three years ahead.
Collins et al. (1994) suggested that returns in period t are generated by three components: (1) the unanticipated component of the current period’s earnings change, , (2) the market’s revision in expectations about future earnings growth rates,
To implement equation (1) empirically, one needs to replace unobservable expectations with observable proxy variables. Prior to Collins et al. (1994), researchers such as Warfield and Wild (1992) used realized earnings growth as an observable proxy for the market’s expectations to explain stock returns. Equation (2) shows the Warfield and Wild’s regression model.
Collins et al. (1994) pointed out that the use of realised earnings growth rates introduces errors-in-variables problems that bias the slope coefficients and R2 downward. The errors-in-variables problems become apparent when one rewrites Equation (2) in terms of variables of interest and measurement errors (Collins et al., 1994: 296):
Where:
Comparing equation (2) with equation (3), it can be seen that equation (2) raises a number of measurement error problems. Firstly, . Secondly,
An important observation in Collins et al. (1994) is that one can mitigate these measurement error problems by the inclusion of errors-in-variables proxies in the augmented regression model. Crucially, Collins et al. (1994) established that the inclusion of such proxies will affect the goodness of fit of the model, only if the reason for the poor performance of the simple return-earnings regression is ‘prices leading earnings’. If value-irrelevant noise is the cause of the poor statistical performance of the standard return-earnings model, then the goodness of fit of Equation (2) will not be improved by adding these proxies.
Collins et al. (1994) suggested three measurement error proxies. These are lagged earnings yield, , current growth in book value of assets,
The first measurement error proxy for expected future earnings growth is the lagged earnings yield variable, ]. It is well known that prices incorporate information about future earnings. Therefore, a high price in relation to last year’s earnings signals high expected earnings growth for the current and future years. As the earnings yield variable and expected earnings growth (the measurement error) are negatively associated, the coefficient on
The second proxy is the asset growth variable, . Higher asset growth indicates that managers increase their production capacity due to an expectation of a higher demand for their product in the future. Such an expansion should lead to higher expected earnings growth. Given that asset growth and expected future earnings changes are positively associated, the coefficient on
Thirdly, and finally, the measurement error proxy for . Unanticipated future events that lead to higher (lower) earnings growth in period t+k should also lead to positive (negative) returns in the period when the news becomes available to the market. Hence, a positive relation between
We employ the multiple regression model introduced by Collins et al. (1994) and further developed by Hussainey and Walker (2009) to study the effect of corporate disclosure quality on the association between current annual stock returns and current and future annual earnings as follows:
where:
As explained in Lev (1989), prior research finds a positive association between current returns and current earnings, so should be positive. Positive coefficient on . The prediction on the coefficients of and . The coefficient on ,
4.2. Disclosure Quality and Stock Returns
Is DQ correctly priced or is it systematically under- or over-valued? This section considers this research question by studying the relationship between DQ and stock returns.
We report answers to a number of questions. The first question we ask is - are stock returns associated with DQ? We respond to the question by investigating whether average returns to portfolios, formed on the basis of sorting firms by DQ, show any pattern as the score in the portfolios move from low to high values of the DQ.
The second question we ask is whether the DQ portfolios exhibit any evidence of significant mis-pricing. Further, we look at whether estimates of mis-pricing increase as the portfolios move from low to high values of DQ. To respond to these questions, we run time-series regressions of monthly portfolio returns on the Fama-French three factor model applied in the UK UK
where:
The portfolios are a zero DQ portfolio ( ) and five quintile DQ portfolios ( ), with firms sorted annually by DQ score and then allocated to the quintile portfolios. The is then used to indicate overpricing if it is less than zero or underpricing if it is more than zero.
The third question we ask is whether a factor reflecting the difference in returns between low DQ firms and high DQ firms is useful in addition to the Fama-French three factor model in the UK
where:
We now report on whether DQ is associated with expected returns. The first test involves sorting firms into portfolios to be held for 12 months from July 1 of year t, based upon the DQ score in year t-1. All firms with zero DQ scores are placed into one portfolio (portfolio zero). The remaining firms are sorted into five equally-sized portfolios. Value-weighted portfolio monthly returns are then calculated. This process is performed for each of the seven years of data. Average monthly returns, and other features of the various portfolios, are reported in Table 4.
5. Data
Electronic versions of UK
The total number of annual reports on Dialog for non-financial firms for this period of time is 8,098 firm-years. Only 7,977 firm-years have Datastream Codes. We have removed firms that change their financial year ends (1312 firm-years). We have also removed firms with missing accounting and return data. This leaves a sample of 3732 firm-year usable observations. Finally, we have deleted outliers defined as observations falling into the top or bottom 1% of the distribution of any of the regression variables. Schleicher et al (2007) provide evidence that the deletion of outliers has no effect on the validity of the conclusions when examining the effect of voluntary disclosure on the returns-earnings association. This reduces the sample to 3528 firm-years usable observations. Accounting and return data for equation 1 are collected from Datastream (see Table 3 for variables definition). To measure the value relevance of disclosure quality, we include a dummy variable, , set equal to 1 for companies in the top 50% of the distribution of disclosure scores and 0 otherwise.
Our sample for the construction of Fama-French factors (HML and SMB) uses monthly return data covering all UK
When portfolios are constructed, if a component stock delists during a portfolio holding period, the proceeds from a delisted stock are assumed distributed among other stocks in the portfolio on the basis of their weights. We set delisting returns to -100 percent whenever the LSPD death type is liquidation (7), quotation cancelled for reason unknown (14), receiver appointed/liquidation (16), in administration (20), or cancelled and assumed valueless (21). We proxy for the return on the market portfolio by the value-weighted return on The Financial Times All Share index.
We follow Dimson et al. (2003) in constructing the Fama-French factors. Their process of describing the factors is as follows. At the end of June for each year t, stocks are allocated to two groups small (S) or big (B), on the basis of being above or below the 70th percentile of the distribution of market value. Stocks are also allocated in an independent sort to three book to market groups, low (L), medium (M) or high (H), according to the breakpoints of the bottom 40%, middle 20% and top 40% of the values of BM recorded at the end of year t-1 . Therefore, six size-BM portfolios (SL, SM, SH, BL, BM, BH) are constructed as the intersections of the two size and three BM groups. Then, we calculate the value-weighted monthly returns for the six intersected portfolios for the subsequent twelve months.
SMB is defined as the monthly difference between the average of the returns on the three small size portfolios (SL, SM, SH) and the average of the returns on the three big size portfolios (BL, BM, BH). HML is calculated as the difference between the average of the returns on the two high BM portfolios (BH, SH) and the average of the returns on the two low BM portfolios (BL, SL).
However, the sample for the DQ factor is restricted to all UK non-financial firms on the Dialog database that have at least one annual report in the period 1996-2002. To construct the DQ factor, we partition firms into five groups on the basis of their DQ score. The DQ factor is defined as the difference between the average of the value-weighted two lowest DQ score portfolio returns, and the average of the value-weighted returns on the two highest DQ score portfolios.
Table 1 provides some initial statistics of the various factors. Fama-French factors (SMB and HML) and DQ factor have positive averages, while the excess market return has a negative average, though they are all insignificant. The positive DQ factor, although insignificant, suggests that firms with the lowest DQ scores generate higher returns than firms with the highest DQ scores. Additionally, the correlations between the factors, although mainly significant, are relatively low.
In order to perform our asset pricing test, we sort stocks into portfolios according to their DQ score to construct DQ portfolios. However, Lo and Mackinlay (1990) warn against using portfolios formed on the basis of some characteristic that are known to be associated with returns in testing asset pricing models. Furthermore, Berk (2000) shows sorting stocks into portfolios, based on a variable known a priori to be correlated with returns, increases the variation in realized excess returns across portfolios and, hence, biases the test in favour of rejecting an economically correct asset pricing model. Therefore, we use industry portfolios as well in our asset pricing tests.
We have used the London Share Price Database Industrial classification (G17) and the FTSE Industrial Classification Benchmark (ICB) in constructing 20 industry portfolios. Then, we calculated the value-weighted returns of these portfolios on the assumption that they are bought and held for a year. Repeating this process, year by year, results in a time series of portfolio monthly returns from July 1996 to June 2002. The excess returns on these 20 portfolios are the dependent variables in the time-series regressions. Table 2 provides descriptive statistics for the 20 industry portfolios used in the time-series tests.
5. results
5.1. THE Value Relevance of Disclosure Quality
Table 3 shows the empirical results of estimating equation (5). As expected, the coefficient associated with
5.2. DISCLOSURE Quality and Stock Returns
Table 4 shows that the average portfolio returns for firms without a DQ score are lower than the average portfolio returns for firms with a DQ score. Moreover, although not entirely monotonic, average portfolio returns decrease as the DQ score increases. This is consistent with the US UK Ireland
Table 4 further illustrates a monotonic increase in average firm size as the portfolios move from low to high DQ scores. The third column of table 4 illustrates that the natural logarithm of market equity increases from 5.16 for the low DQ portfolio to 7.64 for the high DQ portfolio. This result is in agreement with previous literature that suggests a positive relationship between a firm’s size and its disclosure level (see, for example, Chavent et al., 2006).
Moreover, Table 4 demonstrates that portfolios with the highest DQ score have lower average book-to-market ratios than the average book-to-market ratio for zero or low DQ score firms. This is inconsistent with Hussainey and Walker (2009), who find that low book-to-market (growth) stocks disclose more information than high book-to-market (value) stocks.
We then considered whether there is any evidence that markets systematically under- or over-price DQ activity. We ran equation (6) on the zero DQ and the five DQ portfolios. The results are reported below in Table 5.
We explain in section 4.2 that if abnormal returns ( ) is less (more) than zero then the portfolio is overpriced (underpriced). Panels A and B of Table 5 reveal that the zero DQ portfolio is insignificantly overpriced having a negative and insignificant abnormal return (-0.88 and -1.12 respectively). Moreover, the remaining DQ portfolios are insignificantly under-priced having positive and insignificant abnormal returns, apart from the fourth DQ portfolio having negative though insignificant abnormal return. Overall, if taken at face value, the results suggest that DQ portfolios are correctly priced, with the abnormal returns insignificantly different from zero for all DQ portfolios. The results could be taken to imply that the UK
We now turn to the final question asked in this section – is the addition of a DQ ‘factor’ a useful addition to the Fama-French three factor model in explaining both the six DQ portfolios and the twenty industry returns in the UK.
Estimates of equation (6) on the six DQ portfolios suggest that adjustment of the Fama- French model to allow for DQ factor can generate significant improvements in the ability of the Fama-French model to explain portfolio returns. Table 5 provides the results from estimating the Fama-French model, and the modified factor model, for each of the six portfolios formed on the basis of DQ. Panel A of Table 5 provides evidence that the Fama- French model explains between 46.98% and 77.78% of the time-series variation in the returns on these portfolios. The explanatory power is lowest for portfolios comprising firms with low levels of DQ. The results in Panel B of Table 5 indicate that the modified factor model generally outperforms the Fama-French model in explaining portfolio returns. Adjusted-R2 statistics increases for all portfolios, apart from portfolio 3 where the adjusted-R2 slightly declines from 65.13% to 64.71%.
Moreover, the loadings on the DQ factor are positive (as expected), and significant for the zero and the two lowest DQ portfolios, while negative (as expected), and significant for portfolio 4. We interpreted the results for the zero and the two lowest DQ portfolios as well as portfolio 4 by suggesting that the DQ factor cancels out DQ-related information embedded in the other three factors. Comparison between Panels A and B of Table 5 shows that adding the DQ factor generally decreases the significance of the loading on the market factor, SMB and HML.
However, loadings on the DQ factor are insignificant for portfolios 3 and 5. The market factor dominates other risk factors in explaining the excess returns for portfolio 3; while the market factor, together with HML are the only significant risk factors for the highest DQ portfolio (portfolio 5). This suggests that the DQ factor could not explain the excess returns of these two portfolios, and that HML probably captures all the information related to disclosure quality in the highest DQ portfolio.
The SUR model with identical regressors is quite common in asset pricing tests (Greene, 2003). In addition to applying the equation by equation ordinary least square estimates to produce t-statistics for each coefficient in every regression, we use the SUR to produce the F-statistics for the joint significance of each set
of six coefficient estimates from the six regressions estimated as a SUR model. Therefore, we addressKan
of six coefficient estimates from the six regressions estimated as a SUR model. Therefore, we address
The F-statistics values, from the SUR, in both Panels A and B of Table 5 suggest that the DQ factor is a useful factor in explaining stock returns (F=64.37, p-value <0.01). Moreover, the joint significance of the remaining risk factors decreases with the inclusion of the DQ factor in the model. The results are consistent with the correlations reported in Table 1 which show quite complex interactions between the DQ factor and the remaining three factors.
For robustness, we addressed Lo and Mackinlay’s (1990) concerns, by examining the comparative performance of the Fama-French model and the modified factor models in explaining the returns of industry portfolios. The results for the industry portfolios are given in Table 6 (Panels A and B). We ran equation (7) without a DQ factor to examine the usefulness of the possible risk factors in the Fama-French model before the introduction of DQ factor. We reported the results of the Fama-French model in Panel A. Then, we re-ran equation (7) with the full set of factors and reported the results in Panel B.
Panel A, Table 6 shows that the p-values from F-test for the joint significance of the loadings are less than 5%. This result is consistent with Michou et al’s (2007) findings that SMB, HML and excess market returns are useful factors in explaining the time-series variation of industry returns in the UK
Panel B, Table 6 confirms the usefulness of the previous three factors with p-values less than 5%. Moreover, the F-stats from SUR show the DQ factor is a useful risk factor with a p-value of 4%. Moreover, it shows that the significance of the market factor and HML as measured by F-stats slightly declines when a DQ factor is added to the model. This last result suggested that the market factor and HML factor partially capture effects related to disclosure quality.
However, a comparison between Panel A and B of Table 6 illustrated that adjusted-R2 slightly declines for 12 out of 20 portfolios when a DQ factor is added to the Fama-French model. This result could be due to the correlation between the DQ factor and Fama-French factors reported in Panel B of Table 1. Again, this would suggest that Fama-French factors contain some information about disclosure quality.
Overall, the empiricism reported upon in this, and the previous section, suggests that the UK
7. summary and Conclusion
This paper builds on prior research that investigates the importance of the disclosure quality for stock market participants. For the sake of completeness, we re-examined the value relevance of future oriented earnings statements in the annual report narratives for predicting future earnings. We then investigated the relation between disclosure quality and stock returns for a large sample of firms over the period July 1997 to June 2004. Finally, we constructed a disclosure quality DQ factor and added it to Fama-French three-factor model in order to investigate the usefulness of such a factor in explaining the time-series variation of UK
Our results show that future oriented earnings statements in the annual report narratives increase the stock market’s ability to anticipate future earnings change three years ahead. This is consistent with a recent study by Hussainey and Walker (2009). We also find that firms with poor disclosure quality, in general, have higher costs of capital than firms with good disclosure quality. This result is consistent with previous research, for example, Gietzmann and Ireland
Finally, the time-series analysis suggest that allowing for a disclosure quality factor in constructing the asset pricing model can be important. The DQ factor is significant in pricing excess returns of UK
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tables
Table 1 Summary Statistics For, and Correlations Between, the Risk Factors
| ||||
Panel A – Summary Statistics for Monthly Returns
| ||||
Rm-Rf
|
SMB
|
HML
|
DQ
| |
Mean
|
-0.00055
|
0.00283
|
0.007148
|
0.00068
|
Median
|
0.002136
|
0.006029
|
0.004805
|
0.0019
|
Std. Dev.
|
0.045341
|
0.039416
|
0.037698
|
0.038861
|
Panel B – Correlations
| ||||
Rm-Rf
|
SMB
|
HML
|
DQ
| |
Rm-Rf
|
1
|
-0.11
|
-0.25**
|
0.13
|
SMB
|
1
|
-0.29***
|
0.41***
| |
HML
|
1
|
-0.57***
| ||
DQ
|
1
| |||
Note:
Table 2 Industry Portfolios’ Descriptive Statistics for the Period 1997(7)-2004(6)
Industry
|
Average Value-Weighted Monthly Returns %
|
Ave. No. of Stocks
|
Average MV
|
Average BM
|
1.Oil and Gas
|
0.81
|
29
|
730.07
|
0.78
|
2.Chemicals
|
0.50
|
24
|
577.37
|
0.68
|
3.Basic Resources
|
1.25
|
30
|
1048.78
|
1.06
|
4.Construction and Materials
|
0.87
|
61
|
335.19
|
0.89
|
5.Aerospace and Defence
|
1.40
|
12
|
904.83
|
0.32
|
6.General Industrials
|
1.50
|
12
|
103.04
|
0.84
|
7.Electronic and Electrical Equipment
|
0.75
|
48
|
204.48
|
0.63
|
8.Industrial Engineering
|
0.28
|
75
|
165.70
|
0.84
|
9.Industrial Transportation
|
0.60
|
32
|
477.99
|
0.73
|
10.Support Services
|
0.29
|
129
|
201.30
|
0.49
|
11.Automobiles and Parts
|
0.81
|
24
|
298.02
|
1.03
|
12.Food and Beverages
|
0.76
|
48
|
885.61
|
0.96
|
13.Personal and Household Goods
|
1.46
|
105
|
167.26
|
1.10
|
14.Healthcare
|
0.25
|
66
|
1417.23
|
0.42
|
15.Food and Drug Retailers
|
0.78
|
36
|
1525.67
|
0.67
|
16.General Retailers
|
0.82
|
62
|
596.47
|
0.65
|
17.Media
|
0.29
|
66
|
368.46
|
0.47
|
18.Travel and Leisure
|
0.49
|
98
|
331.13
|
0.77
|
19.Technology
|
0.02
|
125
|
249.01
|
0.44
|
20.Utilities
|
0.65
|
33
|
4595.22
|
0.67
|
Note:
In June each year from July 1997 to June 2004, stocks are sorted into 20 value-weighted portfolios using LSPD G17 codes and FTSE Industrial Classification Benchmark (ICB)[4]. Firm size is measured as the number of shares outstanding multiplied by the stock price at the end of June. BM is measured equity capital and reserves minus total intangibles at the end of December of previous year.
Table 3 The Value Relevance of Disclosure Quality
Independent variable
|
Coefficient estimate
|
Intercept
|
–0.02
(0.376)
|
1.53***
(0.001)
| |
0.48***
(0.001)
| |
–0.05
(0.001)
| |
0.09***
(0.001)
| |
1.09***
(0.001)
| |
0.01
(0.604)
| |
0.14
(0.305)
| |
0.27***
(0.004)
| |
0.02
(0.126)
| |
0.01
(0.615)
| |
–0.36***
(0.001)
| |
Observations
|
3528
|
R2
|
0.134
|
Note:
Stock returns, Rt, is calculated as buy-and-hold returns from eight months before the financial year-end to four months after the financial year-end. Rt3 is the aggregated three years future period returns. The earnings variable, Xt, is defined as earnings change per share deflated by the share price four months after the end of the financial year t–1. Xt3 is the aggregated three years future earnings change Earnings measure is the Worldscope item 01250 which is operating income before all exceptional items. AGt is the growth rate of total book value of assets for period t (Datastream item 392). EPt–1 is defined as period t–1’s earnings over price four months after the financial year-end of period t–1. Firms with a disclosure score in the top (bottom) 50% of the distribution of disclosure scores are defined as high (low) disclosure firms. The dummy variable, D, is set equal to 1 (0) for high (low) disclosure firms. The significance levels (two-tail test) are: * = 10 %, ** = 5 %, *** = 1 %.
Table 4 Mean Values for Non-DQ and DQ portfolios
Portfolio
|
Monthly Return (%)
|
ln(ME)
|
BM
|
DQ
|
0
|
-1.06
|
5.54
|
0.77
|
0
|
Low
|
0.99
|
5.16
|
0.77
|
1.16
|
2
|
0.69
|
5.78
|
0.81
|
2.57
|
3
|
0.60
|
6.09
|
0.74
|
4.07
|
4
|
0.13
|
6.69
|
0.74
|
5.85
|
High
|
0.41
|
7.64
|
0.74
|
10.51
|
Note: Monthly returns are value-weighted returns. BM is the ratio of book equity to market equity. ME is the market equity. DQ is the disclosure quality score. All ratios are computed at the end of June of year t. Portfolios are formed annually based on DQ. Portfolio 0 comprises all firms with zero DQ for year t. Portfolio low comprises the lowest quintile of firms sorted on the basis of DQ while portfolio High comprises the highest quintile of firms based on DQ.
Table 5 Regressions of Excess Returns For Six DQ Portfolios on the Market Factor, SMB and HML (Panel A) and the Market factor, SMB, HML, and DQF (Panel B)
a
|
βM
|
βHML
|
βSMB
|
βDQ
|
t(a)
|
t(βM)
|
t(βHML)
|
t(βSMB)
|
t(βDQ)
|
Adj-R2
| |
Panel A: Rp - Rf = a + βM (RM - Rf) + βHMLHML +βSMB SMB + ε
| |||||||||||
DQ=0
|
-0.88
|
1.1206
|
-0.8355
|
0.2362
|
-1.22
|
5.94
|
-3.65
|
1.30
|
52.05
| ||
Low
|
0.80
|
0.8858
|
-0.4011
|
0.4016
|
1.35
|
6.03
|
-2.35
|
2.32
|
46.98
| ||
2
|
0.51
|
1.0210
|
-0.3786
|
0.3311
|
1.49
|
9.11
|
-2.87
|
2.40
|
62.52
| ||
3
|
0.08
|
0.9374
|
0.1357
|
0.1975
|
0.29
|
9.63
|
1.07
|
1.76
|
65.13
| ||
4
|
-0.35
|
1.0841
|
0.1337
|
0.1360
|
-0.76
|
13.33
|
1.05
|
1.41
|
61.79
| ||
High
|
0.20
|
0.7772
|
-0.1760
|
-0.1217
|
1.07
|
14.24
|
-2.74
|
-1.90
|
77.78
| ||
F
|
1.05
|
> 100
|
8.47
|
3.97
| |||||||
P-value
|
0.40
|
< 0.01
|
< 0.01
|
< 0.01
| |||||||
Panel B: Rp - Rf = a + βM (RM - Rf) + βHMLHML +βSMB SMB + βDQ DQ + ε
| |||||||||||
DQ=0
|
-1.12
|
1.0954
|
-0.4908
|
0.0456
|
0.6928
|
-1.55
|
5.93
|
-2.38
|
0.21
|
2.99
|
56.92
|
Low
|
0.61
|
0.8652
|
-0.1204
|
0.2464
|
0.5640
|
1.11
|
6.30
|
-0.87
|
1.45
|
2.65
|
52.54
|
2
|
0.27
|
0.9958
|
-0.0356
|
0.1414
|
0.6893
|
0.88
|
10.40
|
-0.22
|
1.15
|
3.78
|
72.37
|
3
|
0.09
|
0.9382
|
0.1251
|
0.2033
|
-0.0212
|
0.31
|
9.55
|
0.90
|
1.60
|
-0.19
|
64.71
|
4
|
-0.24
|
1.0960
|
-0.0289
|
0.2260
|
-0.3268
|
-0.50
|
12.80
|
-0.17
|
2.20
|
-2.59
|
64.14
|
High
|
0.23
|
0.7805
|
-0.2209
|
-0.0969
|
-0.0901
|
1.26
|
13.67
|
-3.13
|
-1.44
|
-1.16
|
77.92
|
F
|
0.88
|
> 100
|
3.1
|
2.21
|
64.37
| ||||||
P-value
|
0.51
|
< 0.01
|
< 0.01
|
0.04
|
< 0.01
| ||||||
Notes:
(i) Following Petkova (2006), Table 5 reports the loadings from individual time-series regressions for the six portfolios, the t-statistics for the significance of the alpha
(intercept) and beta (slope) coefficients, and the adjusted-R2 from these regressions.
(intercept) and beta (slope) coefficients, and the adjusted-R2 from these regressions.
(ii) The corresponding t-statistics are also reported and they are corrected for heteroscedasticity and serial correlation, using the Newey-West (1987) estimator with five lags.
(iii) The intercepts are in percentages and the sample period is from July 1997 to June 2004.
(iv) The final two rows report F-test statistics for the joint significance of each set of six coefficient estimates from the six regressions estimated as a Seemingly Unrelated Regressions system. For the alpha (intercept) coefficients, the GRS F-test for the joint significance of the six intercepts is reported. For the beta (slope) coefficients, standard F-tests for the joint significance of each set of six coefficient estimates are reported.
Table 6 Loadings from Time-Series Regressions on 20 Industry Portfolios
Panel A: Loadings on the FF Factors from Time-Series Regressions
Industry
|
a
|
ta
|
βM
|
tβ(M)
|
βSMB
|
tβ(SMB)
|
βHML
|
tβ(HML)
|
Adj-R2
|
1
|
-0.07
|
-0.07
|
0.79
|
3.04
|
0.52
|
2.21
|
0.50
|
1.63
|
16.12
|
2
|
-0.22
|
-0.53
|
0.95
|
7.83
|
0.38
|
2.38
|
0.35
|
2.69
|
46.93
|
3
|
0.40
|
0.55
|
1.43
|
7.38
|
0.30
|
1.69
|
0.61
|
2.58
|
49.04
|
4
|
-0.08
|
-0.18
|
1.13
|
9.98
|
0.48
|
3.46
|
0.65
|
5.88
|
58.31
|
5
|
0.59
|
0.89
|
1.39
|
10.11
|
0.09
|
0.38
|
0.64
|
3.17
|
46.37
|
6
|
0.87
|
1.74
|
0.61
|
6.49
|
0.47
|
4.47
|
0.17
|
1.80
|
28.91
|
7
|
0.02
|
0.02
|
1.88
|
6.62
|
1.33
|
4.27
|
0.07
|
0.28
|
54.45
|
8
|
-0.63
|
-1.29
|
1.17
|
7.80
|
0.62
|
5.12
|
0.54
|
3.27
|
57.17
|
9
|
-0.10
|
-0.25
|
1.03
|
7.47
|
0.38
|
3.33
|
0.34
|
2.13
|
58.67
|
10
|
-0.22
|
-0.63
|
1.07
|
12.90
|
0.58
|
7.44
|
0.00
|
-0.02
|
68.63
|
11
|
0.18
|
0.28
|
1.21
|
8.18
|
0.33
|
1.68
|
0.26
|
1.40
|
37.01
|
12
|
0.08
|
0.19
|
0.58
|
3.74
|
-0.04
|
-0.31
|
0.44
|
1.88
|
27.16
|
13
|
0.79
|
1.31
|
0.85
|
4.47
|
-0.05
|
-0.18
|
0.45
|
2.11
|
28.17
|
14
|
-0.17
|
-0.31
|
0.57
|
4.74
|
-0.24
|
-1.53
|
0.14
|
0.95
|
21.36
|
15
|
0.08
|
0.17
|
0.57
|
5.00
|
-0.05
|
-0.39
|
0.46
|
2.36
|
30.27
|
16
|
0.17
|
0.34
|
0.84
|
5.26
|
0.24
|
1.53
|
0.31
|
1.19
|
33.09
|
17
|
0.12
|
0.17
|
1.26
|
9.91
|
0.59
|
3.54
|
-0.48
|
-2.00
|
53.51
|
18
|
-0.39
|
-0.84
|
1.19
|
8.57
|
0.41
|
3.15
|
0.59
|
3.79
|
63.14
|
19
|
0.12
|
0.10
|
1.98
|
8.17
|
1.17
|
4.90
|
-1.02
|
-2.73
|
61.23
|
20
|
0.70
|
1.35
|
0.89
|
5.13
|
-0.35
|
-2.13
|
-0.44
|
-1.70
|
47.18
|
F
|
0.51
|
59.63
|
6.37
|
6.49
| |||||
P-value
|
0.95
|
< 0.01
|
< 0.01
|
< 0.01
|
Panel B: Loadings on the FF + DQF Factors from Time-Series Regressions
Industry
|
a
|
ta
|
βM
|
tβ(M)
|
βSMB
|
tβ(SMB)
|
βHML
|
tβ(HML)
|
ΒDQ
|
tβ(DQ)
|
Adj-R2
|
1
|
-0.03
|
-0.03
|
0.79
|
3.03
|
0.54
|
2.52
|
0.45
|
1.28
|
-0.10
|
-0.38
|
15.20
|
2
|
-0.08
|
-0.22
|
0.97
|
7.76
|
0.48
|
2.76
|
0.16
|
1.12
|
-0.38
|
-2.12
|
50.16
|
3
|
0.33
|
0.47
|
1.42
|
7.17
|
0.25
|
1.28
|
0.71
|
2.69
|
0.19
|
1.01
|
48.86
|
4
|
-0.07
|
-0.15
|
1.13
|
9.70
|
0.49
|
3.15
|
0.63
|
5.63
|
-0.04
|
-0.28
|
57.81
|
5
|
0.73
|
1.13
|
1.40
|
10.20
|
0.20
|
0.88
|
0.43
|
1.58
|
-0.42
|
-1.47
|
47.85
|
6
|
0.85
|
1.75
|
0.61
|
6.42
|
0.46
|
3.69
|
0.20
|
1.66
|
0.05
|
0.29
|
28.11
|
7
|
0.11
|
0.12
|
1.89
|
6.53
|
1.41
|
3.91
|
-0.07
|
-0.22
|
-0.27
|
-0.93
|
54.33
|
8
|
-0.50
|
-1.03
|
1.18
|
7.86
|
0.72
|
6.25
|
0.35
|
2.11
|
-0.38
|
-2.40
|
59.64
|
9
|
-0.07
|
-0.18
|
1.03
|
7.44
|
0.40
|
3.35
|
0.30
|
1.83
|
-0.08
|
-0.66
|
58.34
|
10
|
-0.16
|
-0.42
|
1.08
|
12.40
|
0.63
|
7.90
|
-0.09
|
-0.76
|
-0.18
|
-1.39
|
69.07
|
11
|
0.19
|
0.29
|
1.21
|
8.06
|
0.33
|
1.55
|
0.25
|
1.11
|
-0.03
|
-0.09
|
36.23
|
12
|
0.21
|
0.48
|
0.60
|
3.76
|
0.06
|
0.49
|
0.25
|
1.15
|
-0.37
|
-3.16
|
31.15
|
13
|
0.73
|
1.24
|
0.84
|
4.41
|
-0.09
|
-0.39
|
0.53
|
2.81
|
0.17
|
0.59
|
27.85
|
14
|
-0.10
|
-0.19
|
0.58
|
4.66
|
-0.19
|
-1.17
|
0.05
|
0.38
|
-0.18
|
-0.95
|
21.28
|
15
|
0.10
|
0.21
|
0.58
|
4.97
|
-0.03
|
-0.25
|
0.43
|
2.18
|
-0.05
|
-0.39
|
29.51
|
16
|
0.14
|
0.29
|
0.84
|
5.16
|
0.22
|
1.30
|
0.35
|
1.34
|
0.08
|
0.41
|
32.40
|
17
|
0.01
|
0.01
|
1.25
|
10.03
|
0.50
|
3.12
|
-0.32
|
-1.48
|
0.31
|
1.29
|
54.04
|
18
|
-0.34
|
-0.72
|
1.20
|
8.54
|
0.45
|
3.43
|
0.51
|
3.40
|
-0.16
|
-1.24
|
63.25
|
19
|
0.09
|
0.07
|
1.98
|
7.81
|
1.15
|
4.34
|
-0.98
|
-2.03
|
0.09
|
0.25
|
60.78
|
20
|
0.57
|
1.11
|
0.87
|
5.12
|
-0.45
|
-2.84
|
-0.26
|
-1.07
|
0.38
|
2.34
|
49.30
|
F
|
0.48
|
58.80
|
6.78
|
4.98
|
1.63
| ||||||
P-value
|
0.97
|
<0.01
|
< 0.01
|
< 0.01
|
0.04
|
Notes:
(i) Following Petkova (2006), Table 5 reports the loadings from individual time-series regressions for the 20 industry portfolios, the t-statistics for the significance of the alpha
(intercept) and beta (slope) coefficients, and the adjusted-R2 from these regressions.
(intercept) and beta (slope) coefficients, and the adjusted-R2 from these regressions.
(ii) The corresponding t-statistics are also reported and they are corrected for heteroscedasticity and serial correlation, using the Newey-West (1987) estimator with five lags.
(iii) The sample period is from July 1997 to June 2004.
(iv) The final two rows report F-test statistics for the joint significance of each set of 20 coefficient estimates from the 20 regressions estimated as a Seemingly Unrelated Regressions system. For the alpha (intercept) coefficients, the GRS F-test for the joint significance of the 20 intercepts is reported. For the beta (slope) coefficients, standard F-tests for the joint significance of each set of 20 coefficient estimates are reported.
(v) The intercepts are in percentages.
[1] We are grateful for helpful comments received at the 2009 British Accounting Association conference, University of Dundee Stirling Management School University of Stirling Stirling , FK9 4LA , Scotland UK
[2] Equation (4) is re-produced from Collins et al.’s (1994:297) Equation (6).
[3] The use of the future period returns proxy is widely used in prior research (see, for example, Lundholm and Myers, 2002, Oswald and Zarowin, 2007; Hussainey and Walker Rogers
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