Some Comments About Accountics Science Versus Real Science

This is the lead article in the May 2013 edition of The Accounting Review
"On Estimating Conditional Conservatism
Authors

Ray Ball (The University of Chicago)
S. P. Kothari )Massachusetts Institute of Technology)
Valeri V. Nikolaev (The University of Chicago)

The Accounting Review, Volume 88, No. 3, May 2013, pp. 755-788

The concept of conditional conservatism (asymmetric earnings timeliness) has provided new insight into financial reporting and stimulated considerable research since Basu (1997). Patatoukas and Thomas (2011) report bias in firm-level cross-sectional asymmetry estimates that they attribute to scale effects. We do not agree with their advice that researchers should avoid conditional conservatism estimates and inferences from research based on such estimates. Our theoretical and empirical analyses suggest the explanation is a correlated omitted variables problem that can be addressed in a straightforward fashion, including fixed-effects regression. Correlation between the expected components of earnings and returns biases estimates of how earnings incorporate the information contained in returns. Further, the correlation varies with returns, biasing asymmetric timeliness estimates. When firm-specific effects are taken into account, estimates do not exhibit the bias, are statistically and economically significant, are consistent with priors, and behave as a predictable function of book-to-market, size, and leverage.

. . .

We build on and provide a different interpretation of the anomalous evidence reported by PT. We begin by replicating their [Basu (1997). Patatoukas and Thomas (2011)] results. We then provide evidence that scale-related effects are not the explanation. We control for scale by sorting observations into relatively narrow portfolios based on price, such that within each portfolio approximately 99 percent of the cross-sectional variation in scale is eliminated. If scale effects explain the anomalous evidence, then it would disappear within these portfolios, but the estimated asymmetric timeliness remains considerable. We conclude that the data do not support the scale-related explanation.4 It thus becomes necessary to look for a better explanation.

Continued in article

Jensen Comment
The good news is that the earlier findings were replicated. This is not common in accountics science research. The bad news is that such replications took 16 years and two years respectively. And the probability that TAR will publish a one or more commentaries on these findings is virtually zero.

How does this differ from real science?
In real science most findings are replicated before or very quickly after publication of scientific findings. And interest is in the reproducible results without also requiring an extension of the research for publication of the replication outcomes.

In accountics science there is little incentive to perform exact replications since top accountics science journals neither demand such replications nor will they publish (even in commentaries) replication outcomes. A necessary condition to publish replication outcomes in accountics science is the extend the research into new frontiers.

How long will it take for somebody to replicate these May 2013 findings of Ball, Kothari, and Nikolaev? If the past is any indicator of the future the BKN findings will never be replicated. If they are replicated it will most likely take years before we receive notice of such replication in an extension of the BKN research published in 2013.

Bob Jensen's threads on replication and commentaries in accountics science ---
http://www.trinity.edu/rjensen/TheoryTAR.htm

Question
In statistics what is a "winsorized mean?"

Answer in Wikipedia ---
http://en.wikipedia.org/wiki/Winsorized_mean

An analogy that takes me back to my early years of factor analysis is Procreates Analysis ---
http://en.wikipedia.org/wiki/Procrustes_analysis

"The Role of Financial Reporting Quality in Mitigating the Constraining Effect of Dividend Policy on Investment Decisions"
Authors

Santhosh Ramalingegowda (The University of Georgia
Chuan-San Wang (National Taiwan University)
Yong Yu (The University of Texas at Austin)

The Accounting Review, Vol. 88, No. 3, May 2013, pp. 1007-1040

Miller and Modigliani's (1961) dividend irrelevance theorem predicts that in perfect capital markets dividend policy should not affect investment decisions. Yet in imperfect markets, external funding constraints that stem from information asymmetry can force firms to forgo valuable investment projects in order to pay dividends. We find that high-quality financial reporting significantly mitigates the negative effect of dividends on investments, especially on R&D investments. Further, this mitigating role of financial reporting quality is particularly important among firms with a larger portion of firm value attributable to growth options. In addition, we show that the mitigating role of high-quality financial reporting is more pronounced among firms that have decreased dividends than among firms that have increased dividends. These results highlight the important role of financial reporting quality in mitigating the conflict between firms' investment and dividend decisions and thereby reducing the likelihood that firms forgo valuable investment projects in order to pay dividends.

. . .

Panel A of Table 1 reports the descriptive statistics of our main and control variables in Equation (1). To mitigate the influence of potential outliers, we winsorize all continuous variables at the 1 percent and 99 percent levels. The mean and median values of Total Investment are 0.14 and 0.09 respectively. The mean and median values of R&D Investment (Capital Investment) are 0.05 (0.06) and 0.00 (0.04), respectively. Because we multiply RQ−1 by −1 so that higher RQ−1 indicates higher reporting quality, RQ−1 has negative values with the mean and median of −0.05 and −0.04, respectively. The above distributions are similar to prior research (e.g., Biddle et al. 2009). The mean and median values of Dividend are 0.01 and 0.00, respectively, consistent with many sample firms not paying any dividends. The descriptive statistics of control variables are similar to prior research (e.g., Biddle et al. 2009). Panels B and C of Table 1 report the Pearson and Spearman correlations among our variables. Consistent with dividends having a constraining effect on investments (Brav et al. 2005; Daniel et al. 2010), we find that Total Investment and R&D Investment are significantly negatively correlated with Dividend.

Continued in article

Jensen Comment
With statistical inference testing on such an enormous sample size this may be yet another accountics science illustration of misleading statistical inferences that Deirdre McCloskey warned about (The Cult of Statistical Significance) in a plenary session at the 2011 AAA annual meetings in 2012 ---
http://www.cs.trinity.edu/~rjensen/temp/DeirdreMcCloskey/StatisticalSignificance01.htm
I had the privilege to be one of the discussants of her amazing presentation.

The basic problem of statistical inference testing on enormous samples is that the null hypothesis is almost always rejected even when departures from the null are infinitesimal.

2012 AAA Meeting Plenary Speakers and Response Panel Videos ---
http://commons.aaahq.org/hives/20a292d7e9/summary
I think you have to be a an AAA member and log into the AAA Commons to view these videos.
Bob Jensen is an obscure speaker following the handsome Rob Bloomfield
in the 1.02 Deirdre McCloskey Follow-up Panel—Video ---
http://commons.aaahq.org/posts/a0be33f7fc

My threads on Deidre McCloskey and my own talk are at
http://www.cs.trinity.edu/~rjensen/temp/DeirdreMcCloskey/StatisticalSignificance01.htm