Accounting information quality and systematic risk

Rajgopal and Venkatachalam (2011) choose to use the squared value of abnormal accruals because it is believed to have more desirable distributional properties. However, we find that in our data, the absolute value is more normally distributed than the squared value. Because data transformations can alter the fundamental nature of the data, create curvilinear relationships, and complicate interpretations (Osborne 2002), we prefer to use the absolute value. Another reason that favors the absolute value is that conceptually, the absolute value of abnormal accruals is a more direct measure of earnings quality than the squared value of abnormal accruals. Empirically, the absolute value of abnormal accruals is more widely used than the squared value of abnormal accruals. We note that as in Rajgopal and Venkatachalam (2011), using either the absolute or the squared value produces similar results.

Although Lambert, Leuz, and Verrecchia (2007) and Armstrong, Banerjee, and Corona (2013) suggest that accounting information quality could affect systematic risk, the direction of this impact is ambiguous in their models. This ambiguity, however, is not inconsistent with the notion that accounting information quality is related to systematic risk.

We acknowledge that a negative relation between accounting information quality and systematic risk is not the only view. For example, Johnstone (2016) argues that better information might actually leave investors less certain about future events and thus the relation between accounting information quality and systematic risk might be positive. The discrepancy in theoretical discussions seems to make our empirical analysis even more worthwhile.

While there is a large empirical literature on systematic risk, there are few theoretical models on the determinants of firm betas (for a comprehensive discussion of this issue, see Hong and Sarkar (2007)). In examining systematic risk, previous studies typically control for major firm characteristics. Here we follow Low (2009) and control for this particular set of variables for two reasons. First, these firm-level variables cover the most important firm characteristics that are often used as controls for firm risk. Second, using the same control variables as in Low (2009) makes our results easily comparable. This comparability is desirable given that there is not a fixed set of control variables that is widely agreed upon by researchers.

For the same period (i.e., 1962–2001), our sample appears to be very comparable to that of Rajgopal and Venkatachalam (2011). For example, the DD measure is available for 95,270 firm-year observations in their sample and 95,461 in ours. Our average DD measure of information quality is 4.43, with a standard deviation of 4.46; theirs are 4.47 and 4.15 respectively. Our mean squared value of the ABACC measure is 0.75, with a standard deviation of 1.95; theirs are 0.91 and 2.39 respectively.

This statistic is based on the mean value of the market-model systematic risk measure in Table 1.

Note that the rank risk measures are ranks (from 100 to 1) of firms based on their raw risk measures in a single year. Thus, the rank measures do not vary with market volatility. In essence, the rank measures compare the absolute value of firm betas in a single year.

We believe that a 3-year window is long enough for the effects of an event to materialize but also short enough to mitigate the effects of confounding factors. Using a 1-year or 2-year window yields similar results.

Over 50 years from 1962 to 2012, S&P 500 has negative returns in only 12 years. The returns in the ten worst years range from − 6.98% (in 1977) to − 36.55% (in 2008). The other two negative returns are − 4.7% (in 1981) and − 3.06% (in 1990). We note that using the ten worst years for the CRSP equally-weighted portfolio in the sample period produces virtually the same results.

It is possible that the worst-time performance of stocks (especially individual ones) might reflect both systematic and idiosyncratic risk factors. One way to alleviate this concern is to identify the states of the world that are truly reflective of the states of the market instead of those of the individual firms in a sample. In addition, one can use a large sample so that the idiosyncratic components of firm performance would matter less when firm performance is averaged out. We have taken both steps in the study.