The effect of information complexity on analysts' use of that information.

Publication: Accounting Review

Publication Date: 01-JAN-03

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COPYRIGHT 2003 American Accounting Association

I. INTRODUCTION

Prior research finds that analysts fail to incorporate all available information into their earnings forecasts (e.g., Biddle and Ricks 1988; Bernard and Thomas 1990; Kim and Schroeder 1990; Abarbanell and Bernard 1992; Ali et al. 1992; Abarbanell and Bushee 1997, 1998). McEwen and Hunton (1999) and Chen et al. (2002) conjecture that information complexity reduces analysts' assimilation of the information, but these studies do not directly measure the relative complexity of the information to support this conjecture. In this study I test whether the complexity of information reduces analysts' use of that information. Specifically, I examine the extent to which analysts' effective tax rate (ETR) forecasts incorporate information related to six tax-law changes of varying complexity.

I measure the relative complexity of these six tax-law changes (enacted by the Tax Reform Act of 1986) based on tax professionals' responses to the American Institute of Certified Public Accountants' tax-complexity index questionnaire (AICPA 1993). I find that the analysts' revisions of their forecasts of ETRs appear to impound the effects of less complex tax-law changes but not the effects of more complex tax-law changes. In contrast, the magnitude of analysts' ETR forecast errors increases with the effects of more complex tax-law changes, but is unrelated to the effects of the less complex tax-law changes, as if analysts fully impound the less complex information into their ETR forecasts. Taken together, these results suggest that analysts impound less complex information more fully than they do more complex information, consistent with information complexity reducing their use of the tax-law change information.

Empirical evidence that the complexity of information reduces analysts' use of that information is relevant to standard setters and researchers. For example, the Financial Accounting Standards Board Mission Statement charges the Board to "promulgate standards only when the expected benefits exceed perceived costs" (FASB 2002, 2). The evidence presented in this study suggests that more complex information imposes a cost on even expert users, which in turn suggests that as the complexity of a standard increases, the expected benefit of that standard must also increase to justify promulgation. (1) In addition, the Securities and Exchange Commission (SEC) adopted the "plain English rule," which requires issuers to:

use plain English principles in writing the front and back cover pages, summary and risk factor sections of prospectuses; revise current requirements for highly technical information in the front of prospectuses; and revise the rule on the preparation of prospectuses to provide companies with more specific guidance on the clarity required in the entire document. (SEC 1997, 1)

The goal of the "plain English rule" is to increase investors' understanding and help them make informed investment decisions. My evidence that the complexity of information reduces even expert analysts' use of information supports the SEC's adoption of the plain English rule because less sophisticated users are, by definition, less able than expert analysts to incorporate complex information into their investment decisions. Finally, researchers investigating the relevance of disclosed information should consider the complexity of that information when interpreting their results. For example, evidence that there is no significant relation between specific information and stock prices or other measures of information use does not necessarily mean that the information is irrelevant; it could also mean that the information is too complex to be used cost-effectively.

In the context of tax laws examined in this study, information complexity is a function of both rule complexity (understanding and applying a tax law to a static set of facts) and strategic complexity (understanding firms' tax-planning responses to new tax laws). Because my empirical measure of complexity ranks tax-law changes in terms of their rule complexity only, the validity of my inferences rests on whether this ranking reflects the overall information complexity of these tax laws. Section III provides evidence supporting the validity of my complexity ranking.

I organize the remainder of this paper as follows. Section II provides background information and develops the hypotheses in the context of prior research. In Section III, I discuss research design issues, including the models used, the complexity-ranking measure, and the sample. Section IV discusses the results of the empirical tests; Section V concludes the paper.

II. BACKGROUND AND HYPOTHESES

The Tax Reform Act of 1986 and Effective Tax Rates

The Tax Reform Act of 1986, formally adopted in October 1986, materially changed a number of specific tax-law provisions that affected corporate ETRs. This exogenous change provides a natural setting for examining the effect of complexity on analysts' forecasts. Some of the tax-law changes took effect immediately, while others became effective January 1, 1987. Accordingly, my analysis uses the forecasts of 1987 ETRs that analysts issued in the fourth quarter of 1986.

A firm's ETR equals its total income tax expense divided by its pretax accounting income. Total income tax expense is the product of taxable income and the tax rate applied to that income, reduced by any tax credits available to the firm. Tax-law changes typically affect ETRs through changes in total income tax expense stemming from changes in (1) the tax rates applied to taxable income; (2) the calculation of taxable income; or (3) the calculation or availability of tax credits. This study focuses on six tax-law changes included in the Tax Reform Act of 1986: two changes in tax rates (a decrease in the statutory tax rate and an increase in the capital gains tax rate); one change in tax rate and the calculation of taxable income (implementation of an alternative minimum tax); and three changes to tax credits (elimination of the investment tax credit; computation of the available research and development [R&D] tax credit; extensive changes to the foreign tax credit).

Tax-law changes do not directly affect pretax accounting income, the denominator of ETR. However, prior research documents firms' strategic responses to anticipated tax-law changes that could affect their ETRs through changes in pretax accounting income that do not proportionally affect total income tax expense (e.g., Gramlich 1991; Boynton et al. 1992; Collins and Shackelford 1992; Dhaliwal and Wang 1992; Manzon 1992; Scholes et al. 1992; Guenther 1994; Maydew 1997). I consider the potential implications of firms' strategic responses in my model (in Section III) and on the results (in Section IV).

Using ETR Forecasts

I use Value Line analysts' explicit forecasts of ETRs to examine how the complexity of the Tax Reform Act of 1986's tax-law changes affected analysts' use of that information. Analysts can more directly estimate the effect of tax-law changes on firms' ETRs than on firms' overall EPS, because each firm itemizes material items that directly affect its ETR in the annual report income tax footnote. I use these footnote data to estimate the effect of each tax-law change on the firm's ETR. Thus, testing the effect of the tax-law change on analysts' forecasts of the specific component of earnings most directly affected by the tax-law change (i.e., ETRs) is sharper than testing the effect of the tax-law change on overall earnings forecasts, which may be both directly and indirectly affected by the tax-law change. Provided that analysts exercise due care in forming ETR forecasts, using ETR forecasts increases the power of my tests and provides more reliable evidence on information used by analysts than I could obtain by analyzing their EPS forecasts.

I took several steps to assess whether Value Line analysts take care in forming their ETR forecasts. First, I carefully read the firm-specific text included with Value Line's numerical forecasts for evidence that analysts use tax-related information. During the third and fourth quarters of 1986, Value Line analysts frequently referred to the expected effect of proposed changes in the tax code on firm ETRs and earnings, including: (1) the direct effect of specific tax-law changes on ETRs; (2) the net effect of tax-law changes on earnings; (3) the offsetting effects of the tax-law changes on ETR and earnings; and (4) the effect of the tax-law changes on consumer and firm behavior. (2) These findings suggest that analysts carefully considered how the Tax Reform Act of 1986 tax-law changes would affect firms' ETRs and earnings. Second, I conducted empirical analyses that further support my assumption that Value Line analysts exercise care in developing their ETR forecasts. Specifically, using data described in Section II, I find that when an analyst forecasted an increase in a firm's ETR, she tended to forecast a decrease in the firm's related earnings; there is a negative correlation between the ETR forecasts revisions and earnings forecast revisions (untabulated correlation = -0.15; at p-value of 0.01). Moreover, when an analyst overestimated a firm's ETR, she tended to underestimate the firm's earnings; there is a negative correlation between the ETR forecast errors and earnings forecast errors (untabulated correlation = -0.20; at p-value of 0.001). These data suggest that analysts do take care in forming their ETR forecasts. Third, in personal interviews, two Value Line analysts assured me that they carefully consider the effects of intermediate forecasts (including ETRs) when forming future earnings forecasts.

Prior Literature and Hypothesis Development

Prior research documents that analysts apparently fail to impound all available information into their forecasts, but provides little insight into why (e.g., Biddle and Ricks 1988; Bernard and Thomas 1989; Klm and Schroeder 1990; Abarbanell 1991; Abarbanell and Bernard 1992; Ali et al. 1992; Abarbanell and Bushee 1997, 1998). More recent studies (Mikhail et al. 1997, 1999; Clement 1999) explore how analyst attributes or incentives contribute to variation in analysts' accuracy (which may stem from variation in their use of information, among other factors).(3) In contrast, I focus on information attributes, not analyst attributes.

Two streams of research suggest that an information attribute, complexity, may affect how efficiently market participants use that information. Theoretical and empirical judgment/decision-making research concludes that increased complexity of a task adversely affects judgment quality (e.g., Payne 1976; Einhorn et al. 1977; Iselin 1988; Paquette and Kida 1988; Payne et al. 1988). (4) This literature suggests that task complexity impairs judgment through decision-makers' strategy selection, where a strategy is the method or set of procedures an individual uses to incorporate information into decision making (e.g., expected utility maximization, satisficing, elimination by aspects). For example, Payne (1976) finds that, at a high level of task complexity, individuals use strategies that are analytically simpler to complete the task. Subsequent studies report similar findings (e.g., Payne 1982; Smith et al. 1982; Earley 1985; Bettman et al. 1990). These...

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