The Accounting Treatment of Goodwill,Idiosyncratic Risk,and Market Pricing

Abstract

We study the relationship between goodwill and future returns by considering how the information content of goodwill before and after Statement of Financial Accounting Standards (SFAS) 142 affects idiosyncratic return volatility (IVOL). Contrary to expectations, previous research has documented that stocks with high IVOL have low future returns (IVOL anomaly). We build on research that shows that high IVOL is a function of low information on future earnings and define goodwill as a growth option that could be priced through IVOL. Our results show that, during the goodwill amortization period, IVOL is high and the IVOL anomaly is strong. In contrast, nonamortized and tested for impairment goodwill is informative and corrects the IVOL anomaly. We find evidence that SFAS 142’s recognition of goodwill as an asset with indefinite useful life results in value-relevant information about firm growth options and future earnings, thus reducing IVOL, eliminating the IVOL anomaly, and creating an environment of more efficient market pricing of risk.

Keywords

goodwill idiosyncratic risk impairment testing SFAS 142 stock volatility fair value

Introduction

During the last two decades, accounting standards under U.S. generally accepted accounting principles (GAAP) and International Financial Reporting Standards (IFRS) have profoundly changed accounting for business combinations and, in particular, how goodwill is recognized and measured. Prior to 1996, goodwill was subject to periodic straight-line amortization with a maximum useful life of 40 years. From 1996 until 2001, goodwill was still amortized but was subject to an arguably “ill-defined”¹ recoverability-based impairment test (Statement of Financial Accounting Standards [SFAS] 121: Accounting for the Impairment of Long-Lived Assets). In 2002, SFAS 142 eliminated the amortization requirement and imposed a more stringent two-step quantitative periodic fair-value-based impairment test. Thus, from 2003 until 2013, goodwill amortization was not allowed, and yearly impairment testing was required.

The debate about the accounting treatment of goodwill is centered on whether it should be amortized, amortized and impaired, or impaired only. At the core of the argument are the questions: Is goodwill an asset with indefinite life that captures future economic benefits of a business combination that cannot be measured by other assets? If it does, then amortization might not be appropriate, and it should be tested for impairment yearly. In the alternative, Does goodwill have a useful life that should be amortized? Does goodwill have a useful life that should be amortized and impaired?

In this article, we attempt to shed light on the relevance and informativeness of goodwill, before and after SFAS 142, by examining the relationship between goodwill and firms’ risk as evidenced by their idiosyncratic return volatility (IVOL) and future returns. We evaluate whether nonamortized, but tested for impairment, goodwill, as presented in the balance sheet after SFAS 142, conveys better information than does the amortization regime and, thus, reduces firm risk. We posit that nonamortized, but tested for impairment, goodwill captures the future economic benefits that cannot be measured through other assets and, consequently, should be useful and informative and, thus, should reduce the information risk.

Our results provide evidence that the fair-value model of SFAS 142 that requires yearly impairment testing indeed reduces firm IVOL and eliminates the IVOL anomaly. We find that firms with large goodwill (more than 10% of assets) have statistically lower IVOL post-SFAS 142 as compared with previous regimes of amortization only and amortization and impairment. We explore the potential link between goodwill and IVOL by examining whether firm-specific variables explain the relationship of IVOL to future returns. Based on Fama–MacBeth regressions, after controlling for size, book to market, previous returns, contemporaneous goodwill impairments, industry, and year effects, we confirm a statistically significant negative relationship between IVOL and goodwill, confirming the IVOL anomaly. We also found, however, that, during the period post-SFAS 142, for firms with more than 10% of goodwill, the negative relationship between IVOL and returns disappears. These results suggest that, after SFAS 142, goodwill becomes more informative, thus reducing IVOL and thereby weakening the well-documented “idiosyncratic risk anomaly.” However, firms with no goodwill continue to show a negative relationship between IVOL and returns before and after SFAS 142, and the IVOL anomaly remains. This suggests that firms with more than 10% of goodwill are better priced by the market after SFAS142, whereas firms with no goodwill continue to exhibit the IVOL anomaly.

This article contributes to the accounting and finance literature in three ways. First, we document a reduction in IVOL post-SFAS 142 in firms with more than 10% of goodwill. Second, we document a positive relationship between IVOL and goodwill over total assets (GWL) during 1990-2002 that changes to a negative relationship after SFAS 142 in 2003. We do not claim that the informativeness of the nonamortized goodwill post-SFAS 142 and the decrease on IVOL is necessarily causally related to each other. This temporal association, however, appears to be consistent with the notion that post-SFAS 142 goodwill captures the future economic benefits that cannot be measured through other assets, and, thus, it is useful and informative and reduces information risk. Finally, we integrate the literature in finance related to time trend in IVOL with the research in accounting related to the changes in informativeness of financial statements as a result of different reporting regimes.

The remainder of the article is structured as follows. The “Background” section provides a discussion of accounting for goodwill, idiosyncratic risk, and related research. In the “Hypothesis Development” section, we develop our theory and hypothesis. The “Data and Research Design” section presents the data and research design. The “Results” section provides the results, and the “Conclusion” section concludes.

Background

Accounting for Goodwill

In 2001, Financial Accounting Standards Board (FASB) made a bold move in support of a fair-value model of accounting and issued SFAS 141: Business Combinations (FASB, 2001a) and SFAS 142: Goodwill and Other Intangible Assets (FASB, 2001b). In SFAS 141, FASB eliminated the book-value-based pooling of interests, making the purchase (acquisition) method the only treatment permitted for business combinations. This not only allowed for more consistent and comparable financial statements but was also intended for the balance sheet to better reflect the values exchanged and the assets acquired in a business combination. SFAS 141 also allowed for the fair value of the net assets acquired in a business combination to exceed the value of the consideration exchanged. That is, the situation of “negative goodwill,” which resulted in assets being recorded at an amount less than their fair value, was no longer permitted. Effectively, SFAS 141 made it mandatory that assets and liabilities acquired in a business combination be valued at fair value, regardless of the consideration exchanged. In cases for which the consideration is less than the fair value of the net assets acquired, what was “negative goodwill” became a bargain purchase gain. Similarly, by recognizing the increasing importance of intangibles in business combinations and the need for more relevant information on how they are accounted for, SFAS 142 recognizes that some intangible assets could have indefinite lives, and that a mandated maximum 40 years useful life amortization in the case of goodwill makes little sense and is not value relevant. In 2002, SFAS 142 eliminated amortization for goodwill and other intangibles with indefinite lives and required that a “quantitative” two-step impairment test be done at least once a year.

In light of SFAS 141 and 142, the long-debated accounting treatment for goodwill seemed settled. Nevertheless, in response to concerns about the complexity and cost associated with goodwill impairment testing, in September 2011, FASB loosened the impairment testing process. The Accounting Standards Update (ASU) 2011-08 (Topic 350) allows firms to use a “qualitative” assessment (also called Step 0) to determine the likelihood of impairment before engaging in a “quantitative” test.² In other words, firms now have the option of using a one-step qualitative test to determine whether a second, “quantitative,” step is necessary or, in the alternative, they should go directly to the quantitative test. Nonlisted private firms, however, pressed to return to the old amortization regime. In response, in 2013, FASB endorsed a recommendation by its Private Company Council (PCC) to modify SFAS 142. In January 2014, FASB issued ASU 2014-02, which allows private companies to have the option of returning to the old regime and amortizing goodwill over a period of no more than 10 years.³

ASU 2014-02 retains the requirement of testing goodwill for impairment but only if a “triggering event” occurs that makes the quantitative impairment testing necessary. In March 2014, FASB added to its agenda a project to explore the revision of accounting for goodwill in public firms. In its agenda, FASB intends to evaluate the following alternatives for accounting of goodwill: (a) adapting the private company 10-year amortization plus an event-triggered impairment testing, (b) requiring goodwill amortization with some annual impairment testing, (c) allowing the direct write-off of goodwill at the acquisition date, and (d) requiring a nonamortization approach with a simplified impairment test.

In its deliberations to issue SFAS 142, FASB recognized that, although difficult to value, intangible assets were an increasingly important economic resource for many firms, and that these had been increasing in proportion to the assets acquired in acquisitions.⁴ To a greater extent, FASB acknowledged that, within the context of a global economy that facilitates mergers and acquisitions, the revaluation and creation of intangible assets are only going to increase. In its deliberations for SFAS 142, FASB acknowledged that the arbitrary useful life of 40 years for goodwill did not make sense, and that goodwill amortization expense was irrelevant to the market. FASB recognized the possibility of intangible assets with an indefinite life⁵ and the importance of goodwill as a way to capture, on an ongoing basis, the synergies, economies of scale, and future benefits of the new entity.

In contrast, the previous amortization regime assumed that goodwill was a wasting asset (finite lived) and that, accordingly, it should be amortized and expensed through income with a mandated and arbitrary ceiling of 40 years. FASB did not agree that goodwill and some intangibles were wasting assets. Instead, SFAS 142 determined that goodwill and any intangible assets that had indefinite useful lives should not be amortized; rather, they should be tested annually for impairment. Because the 1995 SFAS 121: Accounting for the Impairment of Long-Lived Assets already required asset impairment but had not resulted in many goodwill impairments (only 15 firms recognized goodwill impairment in the 6 years before SFAS 142), FASB agreed that SFAS 121 did not provide adequate guidance. Thus, SFAS 142 provides detailed guidance for determining and measuring goodwill impairment⁶ and requires a comprehensive disclosure about goodwill in the years subsequent to its acquisition.

FASB explicitly mentions that SFAS 142 enhanced the concept of representational faithfulness,⁷ and that it appropriately balanced “both: relevance and reliability as well as costs and benefits” and “provides users with a better understanding of the expectations about assets, thereby improving their ability to assess future profitability and cash flows.” Nonamortized “goodwill combined with an adequate impairment test will provide financial information that more faithfully reflects the economic impact of acquired goodwill on the value of an entity.”

Previous Research

Goodwill

There is a burgeoning literature on goodwill accounting for which there are two broad areas of inquiry: Balance Sheet studies that provide an examination of the value relevance of goodwill as an asset and Income Statement studies that concern the effect of amortization expense, impairments, and the possibility of earnings management. There is also abundant international literature that focuses on the effect of goodwill impairment regimes because, like in the United States with SFAS 142, firms in other jurisdictions have been affected by IFRS 3/International Accounting Standards (IAS) 3, which imposes a similar goodwill impairment testing requirement.

Research that concerns the value relevance of goodwill as an asset, through an investigation of its relationship with the market value of equity and returns, concludes that goodwill is value relevant (see Barth & Clinch, 1996; Bugeja & Gallery, 2006; Dahmash, Durand, & Watson, 2009; Henning, Lewis, & Shaw, 2000; Jennings, Robinson, Thompson, & Duvall, 1996; Ritter & Wells, 2006). More specific to our study, the issue of goodwill measurement, as it relates to amortization versus impairment, has been examined in other studies that provide an examination of changes in value relevance of goodwill following the adoption of IFRS 3/IAS 3. These studies compare distinct country-specific pre-IAS 3 accounting treatment of goodwill with the changes that occurred after IAS 3 and find some weak and nonconclusive evidence of an increase in value relevance following the impairment-only model (see in the European Union: Aharony, Barniv, & Falk, 2010; in Australia: Chalmers, Clinch, & Godfrey, 2008, and Chalmers, Clinch, Godfrey, & Wei, 2012; in Portugal: Oliveira, Rodriguez, & Craig, 2010; and in the United Kingdom: Amel-Zadeh, Li, & Meeks, 2010, and Horton & Serafeim, 2010). These international studies are useful for understanding the effect of SFAS 142 in the United States. Nevertheless, although the treatment of goodwill and business combinations in IFRS and U.S. GAAP has converged significantly, there are still important differences that do not make the results of IFRS-based studies generalizable in the United States.⁸ In addition, as stated earlier, country-specific regimes pre-IAS 3 allowed for the recognition of internally generated intangibles, capitalization of research and development, and the reversal of impairments, none of which apply to the pre-SFAS U.S. environment.

The second area of goodwill-related research, which provides an examination of the appropriateness and timeliness of goodwill impairments and their impact on income measurement, yields conflicting results. In an event study, Bens, Heltzer, and Segal (2011) used a sample of 388 firms for the years 1996-2006 and found that, although, on average, goodwill impairments produce a significant negative market reaction, this market response is weakened post-SFAS 142 (see also Z. Li, Shroff, Venkataramn, & Zhang, 2011). Lee and Yoon (2012) found that, during the impairment-only regime, the ability of goodwill to predict future cash flows post-SFAS 142 is stronger than in the amortization pre-SFAS 142 period (see also Ahmed & Guler, 2007; Lee, 2011). In contrast, Chen, Krishnan, and Sami (2014) examined impairment charges post-SFAS 142 during 2003-2007, and found that analysts’ forecasts are less accurate and more dispersed for the impairment sample.

Some research has examined the impact of goodwill impairment as it relates to concerns about a larger potential for earnings management. Ramanna and Watts (2012) found that “unverifiable” discretion post-SFAS 142 could be used by managers opportunistically to delay goodwill impairment. K. K. Li and Sloan (2012) found that impairments after SFAS 142 are less timely than during the amortization–impairment period and concluded that SFAS 142, by eliminating the periodic amortization, has given management discretion to manage earnings and, thus, results in inflated goodwill and more aggressive accounting. Darrough, Guler, and Wang (2014), however, found value relevance in goodwill impairment losses as they relate to CEO compensation. Darrough et al. showed that compensation committees do not shield the CEO compensation from goodwill impairment losses, which results in a significant reduction in cash and equity CEO compensation.⁹

Risk and idiosyncratic return volatility

A stream of literature has examined risk within the context of information content and the lack thereof, as measured by volatility. Campbell, Lettau, Malkiel, and Xu (2001) documented that levels of average stock-return volatility in the United States increased significantly from 1962 to 1997 and attribute most of this increase to the growth of IVOL as opposed to the volatility of the stock market index, which remained relatively constant. In a similar vein, Morck, Yeung, and Yu (2000) found that the ratio of IVOL to systematic risk in the United States has surged over time. Rajgopal and Venkatachalam (2011) explored whether a deteriorating financial reporting quality, proxied by accrual-based measures of earnings quality, is associated with the increase in IVOL over the last four decades. Their results indicated that it does. Consistent with the well-established theory that worsening earnings quality causes noisier earnings (e.g., Diamond & Verrecchia, 1991; Easley & O’Hara, 2004; Leuz & Verrecchia, 2000; O’Hara, 2003), results from time-series and cross-sectional regressions show a strong association between rising IVOL and falling earnings quality.

Ang, Hodrick, Xing, and Zhang (2006) applied the Fama and French (FF) three-factor regression model to measure IVOL and found that, contrary to expectations, “Stocks with high idiosyncratic volatility have abysmally low average returns” (p. 296). They concluded that this result is “a substantive puzzle” (Ang et al., 2006, p. 262). This intriguing anomaly, also called the IVOL anomaly, has generated a large stream of research that attempts to disentangle this counterintuitive relationship. B. Li, Rajgopal, and Ventachalam (2014) presented some evidence to suggest that larger IVOL is associated with greater pricing errors and hence less informative or noisier stock prices. The “puzzling” relationship between IVOL and returns could also be explained by the recognition that IVOL is a strong indicator of arbitrage costs related to the unknown asset growth effect, which in turn results in mispricing (Baker & Savasoglu, 2002).

Jiang, Xu, and Yao (2009) showed that the return-predictive power of IVOL is affected by the information provided by future earnings. After controlling for future earnings shocks, Jiang et al. found that the IVOL return anomaly disappears. Building on the theoretical models of corporate disclosure (e.g., Dye, 1986; Verrecchia, 1983) that posit that less information leads to more heterogeneous beliefs and higher volatility, Jiang et al. documented that high IVOL firms tend to have poor disclosure quality, and that this poor disclosure negatively predicts future earnings. Jiang et al. concluded that firms with high IVOL are more likely to be firms with negative earnings shocks in the future.

Hypothesis Development

This article builds on B. Li et al. (2014), who found evidence to suggest that larger IVOL is associated with greater pricing errors and hence less informative or noisier stock prices; Rajgopal and Venkatachalam (2011), who showed that worsening earnings quality is positively associated with rising return volatility over the 40-year period ending in 2001; and Jiang et al. (2009), who linked IVOL to corporate information disclosure and future earnings shocks. Our theoretical argument also develops on the option pricing research of Brennan and Schwartz (1985) and McDonald and Siegel (1986), who posited that option values increase in volatility—both systematic and idiosyncratic. We argue that goodwill could be construed as a growth option that, if properly valued, will be priced through its effect on reducing IVOL. In other words, if individual stock volatility is directly related to the volatility of firm value, and growth options increase in volatility, we would expect that, for firms with high growth options—large and well-valued goodwill—firm returns and firm-level volatility will be positively related. We posit that, if goodwill is informative, it behaves like a growth option, reducing IVOL, which is priced in future returns. Thus, our primary premise is that firms with informative, impairment tested (well-valued) goodwill will have lower idiosyncratic return volatility. This lower IVOL, post-SFAS 142, should eliminate or reduce the IVOL anomaly. In short, more informative goodwill will result in more efficient market pricing.

SFAS 142 creates a perfect setting to investigate whether more informative financial information has an effect on idiosyncratic firm risk and whether this, in turn, is priced through returns. SFAS 142 imposes an unquestionable higher complexity and cost to goodwill valuation as compared with the previous regime of straight-line goodwill amortization; but, at the same time, it should produce better valuations. Thus, our primary motivation is to test (a) whether SFAS 142 improves the information content of goodwill by reducing risk as represented by idiosyncratic firm return volatility and (b) whether this lower IVOL reduces or eliminates the IVOL anomaly.

We rely on the following theoretical constructs: First, pre-SFAS 142 (before 2003) straight-line amortization with an arbitrary useful life was required, and this accounting treatment reduced goodwill regardless of its potential for generating future cash flows. Thus, pre-SFAS 142, amortized goodwill had little information content. Second, from 1996 until 2002, the impairment requirements of SFAS 121 did not result in more value-relevant goodwill. Third, SFAS 142 (after 2003) allowed managers to signal private information and to improve the information environment and value relevance of goodwill (see Dye & Verrecchia, 1995). Fourth, SFAS 142 requires yearly impairment testing as well as considerable disclosure about goodwill in the years subsequent to the acquisition, all of which mitigate information asymmetry and improve the information content of goodwill (Diamond & Verrecchia, 1991; Healy, Hutton, & Palepu, 1999).

Data and Research Design

Data on stock returns, prices, and shares outstanding are obtained from Center for Research in Security Prices (CRSP). Data on total assets, goodwill, book value of equity, leverage, and net income are obtained from Compustat.

In each month, we select stocks with the following criteria. First, to ensure an accurate measure of IVOL, a stock must have at least 15 daily return observations in CRSP during a month. Second, to avoid market micro-structure-related issues, at the end of each portfolio-formation month the stock price must be no less than US$5. The sample period in our study is from 1990 until 2013. We identify all listed firms with more than 10% of assets in goodwill.

Following Ang et al. (2006), we estimate a stock’s IVOL¹⁰ each month from daily CRSP data using the Fama and French (1993) three-factor model. To be specific, IVOL is the standard deviation $σ_{t}^{2}$ of the residuals ( $t$ ) from the following regression:

r_{t} = α + β_{1} \sum_{i = - 3}^{3} r_{m, t - i} + β_{2} HM L_{t} + β_{3} SM B_{t} + ε_{t},

where r_t is the daily stock return, $r_{m, t - i}$ is the daily CRSP value-weighted index return (the market return), and HML_t and SMB_t are the daily Fama–French book-to-market and size factors. We examine stock returns for a holding period of 1 month after the idiosyncratic volatility is measured. At the end of each month in our sample period, we rank stocks based on IVOL to form equal-weighted decile portfolios and then hold the portfolios over the next month.

Table 1 reports summary statistics of IVOL and characteristics of the sample firms during various subperiods from 1990 to 2013. In our analysis, log market capitalization (ln(Size)) is based on stock prices in the portfolio-formation month. The book value of equity in the log book-to-market ratio (ln(B/M)) is obtained from the most recently reported fiscal year. Momentum (PrRet) is the cumulative returns during the past 12 months. Leverage (Lev) is the ratio of the book value of debt to book value of equity from the most recently reported fiscal year. Goodwill (GWL/TA) is the goodwill over total assets. Ret_t₊₁ is the returns for the next month. See all variable definition in the Appendix. All statistics in Table 1 are calculated first cross-sectionally each month and then averaged over time. The results in Panel A suggest that the average idiosyncratic volatility decreases post-SFAS 142—when goodwill is no longer amortized but tested for impairment—as compared with previous regimes pre-SFAS 142 of amortization only and amortization and impairment. IVOL decreases 32% (median) after SFAS 142. Interestingly, during the period after SFAS 121: Accounting for the Impairment of Long-Lived Assets, of “recoverability-based impairment,”IVOL was the highest. During the 6-year period after SFAS 121 (1990-1995) and before SFAS 142, only 15 firms impaired goodwill. The average number of firms, the average market capitalization of stocks (ln(Size)), and the ratio of goodwill to total assets increase steadily in the sample period, while the average book-to-market ratio remains relatively stable. Goodwill increases more than 20% (median). The correlations in Panel B suggest that stocks with higher idiosyncratic volatility tend to be smaller in size, and have higher book-to-market ratios and higher past returns.

Table 1.

Summary Statistics of Idiosyncratic Volatility and Firm Characteristics.

Panel A of this table reports the summary statistics of idiosyncratic volatility and characteristics of sample stocks during subperiods from 1990 to 2013. n is the average number of firms in each month during the subperiod. Idiosyncratic volatility (IVOL) is estimated using daily stock returns for each month from the Fama–French three-factor model. ln(Size) is the log market capitalization in millions of dollars. ln(B/M) is the log of book value of equity for the current fiscal year divided by market capitalization. Momentum (PrRet) is the cumulative returns during the past 12 months. Leverage (Lev) is the ratio of long-term debt to the book value of equity. Goodwill/TA (GWL/TA) is the ratio of goodwill to total assets. Returns (Ret_t₊₁) is future returns. Panel B reports the time-series average of the cross-sectional correlations among IVOL, ln(Size), ln(B/M), PrRet, Lev, GWL/TA, and Ret_t₊₁ during the whole sample period.

Panel A: Descriptive Statistics.
		n	IVOL		ln(Size)		ln(B/M)		PrRet		Lev		GWL/TA		Ret_t ₊₁
			Mean	Med.	Mean	Med.	Mean	Med.	Mean	Med.	Mean	Med.	Mean	Med.	Mean	Med.
Period 1: Pre-SFAS 142
Amortization regimes	1990-2002	553	2.57	2.23	5.84	5.68	−0.83	−0.79	0.22	0.10	1.39	0.51	0.23	0.20	0.74	0.35
Amortization only	1990-1995	378	2.37	2.08	5.54	5.32	−0.82	−0.79	0.23	0.11	1.21	0.51	0.22	0.18	1.04	0.70
SFAS 121	1996-2002	688	2.73	2.36	6.08	5.97	−0.84	−0.78	0.22	0.08	1.22	0.51	0.25	0.21	0.48	0.05
SFAS 121 impairment firms	1996-4/2002	15	2.71	2.48	6.84	6.93	−0.97	−0.90	0.06	−0.02	10.09	0.63	0.21	0.19	1.28	0.66
Period 2: Post-SFAS142
Impairment regime	2003-2013	1,075	1.78	1.51	7.14	7.05	−0.85	−0.82	0.19	0.11	1.47	0.36	0.27	0.23	1.28	0.97
Impairment only	2003-2011	1,046	1.92	1.65	6.99	6.89	−0.82	−0.79	0.19	0.10	1.46	0.36	0.27	0.23	0.80	0.54
ASU 2011-08 Topic 350	2012-2013	1,028	1.42	1.17	7.60	7.55	−0.95	−0.90	0.20	0.16	1.36	0.42	0.26	0.23	2.23	2.01
Δ Period 1 and Period 2			−0.79	−0.72	1.30	1.37	−0.03	−0.03	−0.03	0.02	0.08	−.15	0.03	0.04	0.54	0.62
%change			−0.31	−0.32	0.22	0.24	0.03	0.04	−0.15	0.17	0.06	−0.29	0.13	0.20	0.73	1.77

p value of change			0.00	0.00	0.00	0.00	0.62	0.58	0.58	0.75	0.77	0.00	0.00	0.00	0.47	0.33

Panel B: Correlations.
	IVOL	ln(Size)	ln(B/M)	PrRet	Lev	GWL/TA	Ret_t ₊₁
IVOL	1.00
ln(Size)	−.39	1.00
ln(B/M)	.03	−.31	1.00
PreRet	.03	.02	−.33	1.00
Lev	.01	−.01	−.28	.02	1.00
GWL/TA	.003	.02	.02	−.01	.01	1.00
Ret_t ₊₁	.01	−.003	.01	.02	.00013	−.01	1

We examine the relationship between IVOL and future stock returns 1 month after the idiosyncratic volatility is measured using portfolio analysis. At the end of each month in our sample period, we rank stocks based on IVOL to form equal-weighted decile portfolios and then hold the portfolios for 1 month. As in Ang et al. (2006), we refer to the IVOL estimation month as the portfolio-formation period and the subsequent month as the portfolio holding period. In calculating holding-period returns, we follow Shumway (1997) to treat delisting returns and replace missing delisting returns with −30% if delisting is performance-related, and 0 otherwise. For each of the 10 decile portfolios, we calculate the time-series means of the quarterly holding-period returns and their t statistics. The time-series t statistics are computed using the Newey–West heteroscedasticity and autocorrelation consistent covariance estimator (see Newey & West, 1987).

We are interested in exploring the relationship between IVOL and future returns. In particular, we investigate the effect of SFAS 142 on this relationship. We study various subperiods for comparison: (a) for the entire sample from 1990 until 2013, (b) for the amortization period pre-SFAS 142 from 1990 until 2001, and (c) for the post-SFAS 142 nonamortization period between 2003 and 2013.¹¹ We further investigate the effect of SFAS 121 from 1996 until 2001 when asset impairment plus amortization was required (although we find that only 15 firms impaired goodwill during this period). We also examine the effect of ASU 2011-08 (Topic 350) or Step 0 by dividing the post-SFAS142 period in two: 2003-2011 and 2012-2013. We identified and eliminated any early adopters during each period. To examine the effect of goodwill on the relationship between IVOL and future returns, we compare if the nature of this relationship is different in firms with goodwill and firms with no goodwill by examining the periods before and after SFAS 121 in both groups.

I V O L = β_{0} + β_{1} \ln (S i z e) + β_{2} \ln (B / M) + β_{3} L e v + β_{4} R O E + β_{5} S t d R O A + β_{6} I m p D u m + β_{7} G W L / T A + β_{8} I n d + {\overset{'}{ε}}_{t} .

We investigate the effect of goodwill during different regulatory periods and its effect on idiosyncratic volatility (IVOL). We estimate Model 2 cross-sectionally and regress IVOL on firm characteristics: size, leverage, growth options (ln(B/M)). We also investigate the effect of accounting accruals (Accr), firm performance (ROE), earnings (ROE), and earnings volatility (StdROA) in IVOL. We include a dummy variable to capture future goodwill impairments (ImpDum) and goodwill (GWL/TA). ImpDum is the binary variable taking the value of 1 if a firm has goodwill impairment after 2002 and if the decrease of intangible assets from the previous year is higher than 0.5% of the firm’s lagged assets before 2002, and it takes the value of 0 otherwise. Industry dummies were created using the 12 Industry Classifications from Kenneth French website. The industry sectors are (1) Consumer Nondurables, (2) Consumer Durables, (3) Manufacturing, (4) Energy, (5) Chemicals, (6) Technology: Business equipment, (7) Telecom, (11) Retail, and (12) Other: Entertainment, Hotels, and so on. As in previous literature, regulated industries: Utilities (8), Healthcare (10) and Financial (11) have not been included. The rationale for the variable selection is below.

Size, Growth Opportunities, and Financial Leverage

We follow Ang et al. (2006) and Jiang et al. (2009), and control for size and expect a negative relationship to IVOL. We expect a negative relation between book to market and IVOL as firms with greater growth opportunities will likely have greater stock-return volatility. Also, firms with greater book to market would likely be in their growth stage and have larger stock-return volatility. High levered firms will be riskier, and the agency problems between managers and bondholders will be exacerbated suggesting a positive association with IVOL (see also Boldin, Chaudhry, & Palacios, 2011; Rajgopal & Venkatachalam, 2011).

Performance and Earnings Volatility

We control for firm performance as measured by return on equity (ROE). We expect that higher ROE will have a negative relation to IVOL. We expect earnings volatility as measured by the standard deviation of return on assets (StdROA) to be positively correlated with IVOL. ROA measures firm productivity and how efficiently assets are used to generate income. ROA also shows managements’ ability, so higher volatility in ROA should be positively correlated with IVOL.

Accounting Accruals

Following Rajgopal and Venkatachalam (2011) who found that the increase in IVOL over time is associated with deteriorating earnings’ quality, we include a measure for accruals (ACCR). We expect that higher accruals will signal higher uncertainty about future cash flows and higher IVOL (see Jiang et al., 2009). We measure accruals following Sloan (1996) for each firm using the annual balance sheet and income statement data from Compustat.

Accruals = \frac{[(Δ CA - Δ Cash) - (Δ CL - Δ STD - Δ TP - Dep)]}{ATA} .

ΔCA is the change in current assets from the previous fiscal year; ΔCASH is the change in cash and cash equivalents; ΔCL is the change in current liabilities; ΔSTD is the change in debt included in current liabilities; ΔTP is the change in income taxes payable; DEP is the depreciation and amortization expense; and ATA is the average of the beginning and ending total assets of the fiscal year. The ACCR variable is measured for the fiscal year that ends in year t− 1.

Goodwill

We expect a positive relationship between GWL and IVOL during the amortization period (before SFAS 142) and a negative relationship during the impairment period. After SFAS 142 in 2003, we expect that the relationship between GWL and IVOL will change and become negative. During the SFAS 142 period, GWL will be more informative and reduce IVOL.

IVOL and Returns

Jiang et al. (2009) found that IVOL contains information about future earnings. This suggests the possibility that the return-predictive power of idiosyncratic volatility may be induced by its information content about future earnings. To test this hypothesis, we run pooled regression of next month stock returns onto current month IVOL, an interaction dummy variable (142Dum) to capture the effect of SFAS 142 regime, idiosyncratic volatility (IVOL), and goodwill (GW).

r_{t + 1} = β_{0} + β_{1} I V O L + β_{2} 142 D u m \times I V O L + β_{3} l n (B / M) + β_{4} P r R e t + β_{5} L e v + β_{6} l n (S i z e) + β_{6} G W / T A + β_{7} I m p D u m + {\overset{'}{ε}}_{t} .

We focus on whether the association between IVOL and future returns changes subsequent to the SFAS 142 regime. Specifically, we expect that the coefficient of IVOL (β₁) will be negative as per the IVOL anomaly which reflects the association between IVOL and future returns prior to SFAS 142. However, if the changes in recognition and measurement requirements relating to SFAS 142 result in more information about future returns through goodwill, after the SFAS 142 the coefficient β₂ of the interaction variable between the dummy variable for SFAS 142 and IVOL (142Dum×IVOL) will be positive.

Results

We look at the entire sample in Panel A of Table 2 and confirm the well-documented “IVOL anomaly” of an inverse cross-sectional relation between IVOL and future stock returns reported in Ang et al. (2006). In Panel A, stocks in the lowest decile of IVOL (D0-L) significantly outperform those in the highest decile of IVOL (D9-H) by 0.58% per month. Returns exhibit a sharp downward trend from the first decile (D0-L) to decile (D8) but drop off sharply for the 10th (D9) decile portfolio. The difference in alphas between the D0 and D9 portfolios is .9% per month and statistically significant. In other words, factor exposure cannot explain the inverse relation between IVOL and stock returns. Moreover, the portfolio alphas exhibit a similar pattern to the raw portfolio returns. Namely, the main difference in alphas between the low and high IVOL portfolios is due to the very low alphas of the two highest IVOL deciles (D8 and D9). The alpha estimates are insignificant for deciles D0 to D7 but significantly negative for deciles D8 and D9. A similar pattern is noted in Ang et al. (2006) and Jiang et al. (2009) based on quintile portfolios.

Table 2.

Returns and Portfolio Characteristics: Sorted in Deciles by IVOL and Goodwill.

This table reports the time-series means of the idiosyncratic volatility (IVOL) and various characteristics, including ln(SIZE), ln(B/M), and momentum (PrRet), leverage (Lev) of equal-weighted decile portfolios formed at the end of each quarter based on IVOL. It also reports the average quarterly portfolio returns (r) together with t statistics of the Carhart (1997) four-factor model. Return and IVOL are in percentage points. The Newey–West t statistics are computed with a four-quarter lag for portfolio characteristics and a one-quarter lag for returns. Robust and significant t-stats are in bold.

Panel A: All Firms.
Decile IVOL	IVOL	Ret_t ₊₁	ln(Size)	ln(B/M)	PrRet	Lev	n	α
D1_LOW	0.792	0.933	7.529	−0.934	0.170	0.834	78.778	0.119
D2	1.109	1.204	7.388	−0.895	0.153	0.852	79.292	0.283
D3	1.333	1.077	7.090	−0.864	0.159	0.906	79.375	0.091
D4	1.551	1.132	6.808	−0.829	0.172	0.917	79.267	0.103
D5	1.779	1.050	6.547	−0.811	0.183	0.914	79.153	−.010
D6	2.034	1.123	6.293	−0.812	0.199	0.943	79.517	0.034
D7	2.334	1.136	6.046	−0.810	0.216	0.954	79.351	.014
D8	2.724	1.054	5.819	−0.807	0.224	0.978	79.292	−.061
D9	3.319	0.705	5.566	−0.814	0.254	0.943	79.375	−.368
D10_HIGH	5.066	0.352	5.219	−0.802	0.243	0.949	78.878	−.784

D1–D10	−4.274	0.580						0.903
t statistics	−67.811	1.672						4.578
Panel B: Amortization Period Pre-SFAS 142.
Decile IVOL	IVOL	Ret_t ₊₁	ln(Size)	ln(B/M)	PrRet	Lev	n	α
D1_LOW	0.902	0.848	6.814	−0.841	0.179	0.870	54.878	0.005
D2	1.278	1.158	6.821	−0.861	0.166	0.926	55.404	0.218
D3	1.547	0.900	6.539	−0.851	0.171	1.039	55.449	−.093
D4	1.808	0.995	6.236	−0.814	0.187	1.036	55.391	0.002
D5	2.082	0.862	5.973	−0.809	0.199	1.035	55.192	−.103
D6	2.389	1.057	5.714	−0.819	0.219	1.068	55.628	0.064
D7	2.743	0.943	5.452	−0.817	0.240	1.029	55.468	−.124
D8	3.197	0.868	5.225	−0.820	0.243	1.030	55.372	−0.066
D9	3.888	0.114	4.988	−0.815	0.267	0.953	55.481	−.681
D10_HIGH	5.826	−0.382	4.623	−0.809	0.210	0.979	54.974	−1.221

D1–D10	−4.924	1.230						1.226
t statistics	−66.400	2.238						3.770
Panel C: Impairment Period Post-SFAS 142.
Decile IVOL	IVOL	Ret_t ₊₁	ln(Size)	ln(B/M)	PrRet	Lev	n	α
D1_LOW	0.662	1.034	8.373	−1.043	0.160	0.790	107.023	0.213
D2	0.909	1.259	8.059	−0.936	0.138	0.765	107.523	0.287
D3	1.082	1.287	7.741	−0.879	0.145	0.750	107.652	0.221
D4	1.248	1.294	7.485	−0.847	0.154	0.777	107.485	0.150
D5	1.422	1.274	7.225	−0.813	0.163	0.771	107.470	0.063
D6	1.614	1.202	6.978	−0.804	0.174	0.795	107.750	−0.047
D7	1.851	1.366	6.747	−0.800	0.187	0.865	107.576	0.087
D8	2.164	1.277	6.521	−0.791	0.202	0.916	107.561	−.059
D9	2.647	1.409	6.249	−0.814	0.238	0.931	107.614	0.005
D10_HIGH	4.168	1.227	5.924	−0.794	0.283	0.912	107.129	−.279

D1–D10	−3.506	−0.193						−.492
t statistics	−64.081	−0.510						−2.623
Panel D: Amortization Period Pre-SFAS 142 (1996-2002).
Decile IVOL	IVOL	Ret_t ₊₁	ln(Size)	ln(B/M)	PrRet	Lev	n	α
D1_LOW	0.957	0.809	6.743	−0.814	0.179	0.990	69.869	0.072
D2	1.362	0.784	6.922	−0.874	0.154	0.981	70.405	−0.034
D3	1.644	0.619	6.732	−0.859	0.155	1.068	70.405	−0.218
D4	1.918	0.724	6.486	−0.831	0.162	1.068	70.381	−0.111
D5	2.204	0.760	6.277	−0.824	0.174	1.047	70.131	−0.052
D6	2.530	0.782	6.003	−0.804	0.189	1.055	70.679	−0.078
D7	2.907	0.645	5.798	−0.809	0.215	1.021	70.440	−0.241
D8	3.392	0.604	5.575	−0.832	0.235	0.998	70.345	−0.102
D9	4.135	−0.516	5.370	−0.842	0.270	0.952	70.464	−1.161
D10_HIGH	6.254	−0.386	5.060	−0.869	0.253	0.951	69.940	−0.990

D1–D10	−5.298	1.195						1.062
t statistics	−46.963	1.271						2.212
Panel E: Impairment Only—Quantitative Two-Step Post-SFAS 142 (2003-2011).
Decile IVOL	IVOL	Ret_t ₊₁	ln(Size)	ln(B/M)	PrRet	Lev	n	α
D1_LOW	0.694	0.882	8.262	−1.030	0.148	0.775	108.065	0.204
D2	0.955	1.089	7.954	−0.914	0.125	0.717	108.565	0.274
D3	1.136	1.111	7.640	−0.859	0.134	0.720	108.704	0.209
D4	1.311	1.097	7.383	−0.823	0.143	0.737	108.528	0.117
D5	1.493	1.077	7.132	−0.797	0.156	0.739	108.519	0.042
D6	1.695	0.986	6.883	−0.783	0.171	0.751	108.806	−0.086
D7	1.941	1.219	6.653	−0.786	0.188	0.817	108.620	0.116
D8	2.265	1.001	6.421	−0.764	0.204	0.864	108.611	−0.148
D9	2.762	1.166	6.157	−0.790	0.246	0.842	108.657	−0.093
D10_HIGH	4.306	1.003	5.811	−0.778	0.297	0.844	108.185	−0.360

D1–D10	−3.612	−0.121						−0.564
t statistics	−59.457	−0.272						−2.848
Panel F: Impairment Only—One-Step Qualitative/Quantitative Post-SFAS 142 (2012-2013).
Decile IVOL	IVOL	Ret_t ₊₁	ln(Size)	ln(B/M)	PrRet	Lev	n	α
D1_LOW	0.516	1.750	8.874	−1.101	0.215	0.861	102.333	−.288
D2	0.702	2.056	8.530	−1.032	0.194	0.979	102.833	0.346
D3	0.835	2.115	8.194	−0.969	0.194	0.885	102.917	0.100
D4	0.963	2.221	7.941	−0.959	0.201	0.956	102.792	0.745
D5	1.100	2.199	7.641	−0.882	0.195	0.915	102.750	0.220
D6	1.251	2.218	7.403	−0.899	0.191	0.991	103.000	0.473
D7	1.446	2.058	7.171	−0.864	0.185	1.080	102.875	−0.108
D8	1.711	2.573	6.968	−0.911	0.191	1.148	102.833	0.529
D9	2.128	2.549	6.661	−0.922	0.203	1.331	102.917	1.031
D10_HIGH	3.549	2.280	6.434	−0.866	0.219	1.221	102.375	1.006

D1–D10	−3.033	−0.531						−1.295
t statistics	−44.453	−0.965						−1.987

The results in Panels B and C, which represent the amortization regime pre-SFAS 142, and the impairment regime post-SFAS 142, show very different patterns. In Panel B, during the amortization regime, we see a very significant IVOL anomaly behavior through an inverse cross-sectional relation between idiosyncratic volatility and future stock returns. Panel B is statistically significant, and stocks in the lowest decile of IVOL (D0) significantly outperform those in the highest decile of IVOL (D9) by 1.23% per month. However, during the impairment period, post-SFAS 142 in Panel C displays no IVOL anomaly. On the contrary, although not statistically significant, stocks with the highest volatility have higher returns than stocks with low volatility. Stocks in the lowest decile of IVOL (D0) significantly underperform those in the highest decile of IVOL (D9) by 0.193% per month.

Interestingly, in Panel D, after SFAS 121, we see that the IVOL anomaly is larger than in Panel B, suggesting that the SFAS 121 regime did not result in more informative goodwill. We divide the impairment regime post-SFAS 142 into two: Panels E and F, to explore the effect of ASU 2011-08. In both cases, we do not see any IVOL anomaly. After ASU 2011-08, we see a very strong positive relationship between IVOL and returns. It is notable that the alphas in Panel F are the highest in D9-H and the lowest in D0-L.

Table 2 also reports various portfolio characteristics. For each decile portfolio, we calculate the time-series means of log market capitalization, log book-to-market ratio, and momentum at the end of each portfolio-formation period. The results in all panels suggest that firms with high IVOL overall tend to be smaller in size and have higher past returns. All panels with the exception of Panel D have lower book-to-market ratios. Based on the Newey–West t statistics, the differences in stock characteristics between the low IVOL portfolio (D1) and high IVOL portfolio (D10) are all statistically significant.

For robustness, to control for other factors, like the effect of Sarbanes–Oxley (SOX) that might have reduced IVOL and eliminated the IVOL anomaly in firms with goodwill, we examine IVOL in firms with no goodwill and thus unaffected by SFAS 142. For each quintile¹² portfolio, we calculate the time-series means of log market capitalization, log book-to-market ratio, and momentum at the end of each portfolio-formation period. Specifically, we test if the IVOL anomaly disappears in firms with no goodwill post-SFAS 142, as observed in Table 2. Panel A of Table 3 compares firms with GWL/TA > 10% with firms with no goodwill during the period before SFAS 142 (1995-2002). Results show a statistically significant IVOL anomaly in both groups before SFAS 142. Panel B compares firms with GWL/TA > 10% with firms with no goodwill after SFAS 142 (2003-2013). Interestingly, Panel B shows that the IVOL anomaly disappears in firms with GWL/TA > 10% after SFAS 142, whereas the IVOL anomaly continues in firms with no goodwill. The results are statistically significant in firms with no goodwill before and after SFAS 142, thus suggesting a change in the pricing of risk in firms with goodwill after SFAS 142, while no effect in firms that do not have goodwill.

Table 3.

Returns and Portfolio Characteristics of Firms With GWL/TA > 10% and Firms Without Goodwill Sorted in Quintiles by Idiosyncratic Volatility and Goodwill Regimes.

Panel A: Pre-SFAS 142 (1995-2002).
Firms With GWL/TA≥ 0.10				Pre-SFAS 142				Firms with no goodwill			Pre-SFAS 142
Quintile of IVOL	IVOL	Return (r)	ln(Size)	ln(B/M)	PrRet	Lev	n	Quintile of IVOL	IVOL	Return (r)	ln(Size)	ln(B/M)	PrRet	Lev	n
Q1_LOW	1.072	0.976	6.752	−0.838	0.179	0.896	102	Q1_LOW	0.932	1.255	5.948	−0.568	0.191	0.827	576
Q2	1.649	0.926	6.292	−0.818	0.185	1.019	103	Q2	1.577	1.285	5.683	−0.646	0.207	0.778	577
Q3	2.197	0.936	5.742	−0.799	0.211	1.053	103	Q3	2.195	1.317	5.175	−0.748	0.248	0.730	577
Q4	2.910	0.787	5.248	−0.801	0.239	1.043	103	Q4	3.002	0.990	4.769	−0.876	0.291	0.656	577
Q5_HIGH	4.754	−0.364	4.730	−0.797	0.226	1.020	103	Q5_HIGH	4.945	0.164	4.311	−0.951	0.262	0.586	577
Q1–Q5	3.682	−1.340						Q1–Q5	4.013	−1.091
t statistics	71.033	−3.237						t statistics	70.659	−2.284
Mean	2.516	0.652						Mean	2.530	1.002
Panel B: Post-SFAS 142 (2002-2013).
Firms With GWL/TA≥ 0.10				Post-SFAS 142				Firms with no goodwill			Post-SFAS 142
Quintile of IVOL	IVOL	Return (r)	ln(Size)	ln(B/M)	PrRet	Lev	n	Quintile of IVOL	IVOL	Return (r)	ln(Size)	ln(B/M)	PrRet	Lev	n
Q1_LOW	0.786	1.147	8.216	−0.989	0.149	0.777	215	Q1_LOW	0.802	1.124	6.678	−0.601	0.165	0.962	226
Q2	1.165	1.291	7.613	−0.863	0.149	0.763	215	Q2	1.355	1.051	6.124	−0.702	0.179	0.837	227
Q3	1.518	1.238	7.101	−0.808	0.169	0.783	215	Q3	1.848	1.094	5.822	−0.772	0.217	0.789	227
Q4	2.008	1.321	6.634	−0.796	0.195	0.890	215	Q4	2.493	0.808	5.570	−0.839	0.268	0.764	227
Q5_HIGH	3.406	1.319	6.087	−0.804	0.260	0.922	215	Q5_HIGH	4.309	0.222	5.172	−0.931	0.351	0.790	226
Q1–Q5	2.620	0.172						Q1–Q5	−3.507	−.901
t statistics	61.896	0.557						t statistics	62.563	−2.306
Mean	1.776	1.263						Mean	2.161	0.860.

We directly investigate if the change in idiosyncratic volatility documented in the previous results is due to the information content of goodwill during the different periods. Table 4 confirms our predictions. The goodwill coefficient is positive and statistically very significant during all the amortization period, suggesting that amortized goodwill increases IVOL: higher goodwill, higher risk. However, the coefficient changes to negative for the early adopters of SFAS 142 who impaired goodwill. During the post-SFAS 142 period, the relationship between goodwill and idiosyncratic risk dramatically shifts, becomes negative and very significant. Interestingly the r-squares are also very high implying a large explanatory power of the variables to explain idiosyncratic risk. As expected, firm size, ln(B/M), and ROE all are negatively correlated to IVOL. Leverage¹³ and earnings volatility are positively correlated to IVOL, and thus increase risk. Accruals¹⁴ are negatively correlated to IVOL, suggesting that higher accruals reduce IVOL. These results are consistent with the findings of Sloan (1996) who found that stocks with high ACCR have low future returns (accrual anomaly). Our results could support the argument that if higher accruals lead to lower IVOL, this lower IVOL contributes to lower returns. Our results show that post-SFAS 142, the relationship between accruals and IVOL changes and the accrual anomaly disappears. Post-SFAS 142 accruals are positively correlated to IVOL: Higher accruals lead higher uncertainty and risk. In untabulated results, we find that during the period of 2012-2013¹⁵ the results are consistent and stronger than during the SFAS 2003-2011 period. That is, during 2012-2013, the coefficient of goodwill is also negative and very significant, suggesting that goodwill reduces IVOL. However, we cannot interpret these results as endorsing the one-step impairment test as we are unable to corroborate which firms used the one step and which used the two steps. It is worth noting that in a study on goodwill impairment in the United States, in 2013 by Duff and Phelps, a survey finds that 75% of respondents indicated not using the one-step qualitative approach and 60% indicated not using the qualitative test because they preferred the two-step quantitative approach (see Roland & Nunez, 2013). This of course would be an area for future research.

Table 4.

Pooled Regressions: Idiosyncratic Risks.

This table reports the pooled regression results of idiosyncratic risks for various goodwill accounting regimes. We include controls for size, book to market (ln(B/M)), leverage (Lev), return on equity (ROE), standard deviation of ROA (StdROA), accounting accruals (Accr), goodwill (GWL), and Fama–French Industry Classifications (Ind). Robust and significant t statistics are provided in bold.

	Post-SFAS 142	All periods	Early adopt. SFAS 142	Pre-SFAS 142	Post-SFAS 142
	Predicted	1990-2013	2002-2003	1996-2002	2003-2013
ln(Size)	Neg	−0.30	−0.24	−0.32	−0.26
		−161.69	−40.94	−69.49	−131.79
ln(B/M)	Neg	−0.13	−0.02	−0.23	−0.05
		−31.52	−1.61	−23.64	−10.27
Lev	Pos	0.02	0.05	0.00	0.03
		9.88	8.48	−0.74	13.33
ROE	Neg	−0.14	−0.29	−0.15	−0.11
		−25.71	−11.60	−15.25	−13.52
StdROA	Pos	0.01	0.45	0.42	0.00
		2.88	11.97	13.92	1.39
Accr	Pos	−0.25	0.11	−0.54	0.10
		−7.76	0.91	−9.44	2.17
ImpDum	Pos	0.19	0.27	0.27	0.15
		18.16	10.15	8.33	14.88
GDWL/TA	Neg	−0.02	−0.01	0.54	−0.28
		−1.10	−0.15	9.88	−11.95
1 NonDur	?	−0.28	−0.40	−0.36	−0.24
		−13.02	−6.48	−7.22	−9.98
2 ConsDur	Neg	−0.23	−0.44	−0.34	−0.07
		−9.56	−6.35	−6.42	−2.64
3 Mfg	Neg	−0.26	−0.41	−0.37	−0.17
		−12.78	−7.07	−8.06	−7.21
4 Energy	Pos	0.35	0.00	0.46	0.32
		11.73	−0.02	6.03	9.81
5 Chem	Pos	−0.26	−0.50	−0.37	−0.19
		−10.98	−7.23	−6.87	−7.16
6 Tech	Pos	0.16	0.36	0.46	0.10
		7.95	6.27	9.85	4.32
7 Telecom	?	−0.08	−0.15	−0.04	−0.11
		−3.27	−2.16	−0.63	−4.18
9 Retail	Neg	0.00	−0.09	0.16	−0.05
		0.07	−1.57	3.29	−2.30
10 Health	?	0.02	0.10	0.10	0.01
		1.19	1.71	2.08	0.22
12 Entert.	?	−0.01	0.01	0.10	−0.03
		−0.40	0.11	2.10	−1.24
Year controls		Yes	Yes	Yes	Yes
Adj_R²		0.78	0.81	0.79	0.78
n		182,597	17,746	46,781	114,746

The industry dummies show that firms in the energy and technology industries have higher IVOL. Consumer durables, nondurables, manufacturing, chemical, and telecom all have lower IVOL. These results could be interpreted within the context of how informative financial statements of these industries are.

Panel A in Table 5 reports the results of the Fama–MacBeth regressions for Equation 4. As expected, the association between IVOL and returns is negative and very significant. Our coefficient of interest, the interaction variable 142Dum and IVOL is positive and statistically significant, suggesting that the change in recognition of goodwill after SFAS 142 provides incremental information to explain returns, and that the relationship between idiosyncratic volatility and returns has fundamentally changed. In Panel B, we confirm that after SFAS 142 the relationship between GWL and IVOL changes. Similarly, the effect of previous return momentum also changes. Pre-SFAS 142, IVOL, and amortized goodwill have a negative relationship with future returns: High IVOL and high GWL firms have lower returns. Pre-SFAS 142 the IVOL anomaly is strong. Post-SFAS 142, however, IVOL and GWL have a positive relationship with returns: High IVOL and high GWL firms have higher future returns. Post-SFAS 142 the IVOL anomaly disappears and GWL informs future returns.

Table 5.

Examining the Effect of SFAS 142 on Returns and IVOL.

Panel A in of this table reports the results of the Fama–Macbeth regression (FM-Reg) of future return on idiosyncratic volatilities and the pooled regressions of future stock returns on idiosyncratic volatility (IVOL); a dummy variable SFAS142 (142D) taking the value of 1 if the firm-year observation has a fiscal year subsequent to SFAS 142 adoption, or 0 otherwise; and goodwill (GWL) is the ratio of goodwill to total assets and various control variables. Controls are ln(SIZE), ln(B/M), PrRet (momentum) and Lev (leverage). Panel B are pooled regressions dividing the sample in two periods: before and after SFAS142. Robust and significant t statistics are in bold.

Panel A: Regression Model: $r_{t + 1} = β_{0} + β_{1} IVOL + β_{2} 142 Dum \times IVOL + β_{3} \ln (B / M) + β_{4} PrRet + β_{5} Lev + β_{6} \ln (SIZE) + β_{7} GW / TA + β_{8} ImpDum + {\overset{'}{ε}}_{t}$ .
	Predicted	FM-Reg	Pooled regressions
Intercept		0.018	1.367	1.965	2.215
		4.438	16.136	12.228	13.056
IVOL	Neg	−0.209	−0.252	−0.299	−0.299
		−3.182	−11.968	−12.642	−12.654
142Dum×IVOL	Neg		−0.064	−0.063	−0.069
			−2.782	−2.657	−2.908
ln(Size) × 100	Neg	−0.059		−0.046	−0.040
		−1.696		−2.380	−2.041
ln(B/M) × 100	Pos	0.266		0.380	0.389
		2.435		8.835	8.960
PrRet× 100	Pos	0.597		0.153	0.151
		1.941		2.908	2.866
Lev× 100	Pos	0.038		0.109	0.109
		1.012		6.661	6.662
GDWL/TA× 10^4	Neg				−1.106
					−5.023
Impairdummy	Pos				0.444
					4.166
Industry control		Yes	Yes	Yes	Yes
Adj_R²		0.35	0.01	0.02	0.02
N		213,600	215,131	213,600	213,600

Panel B: Regression Model: $r_{t + 1} = β_{0} + β_{1} IVOL + β_{2} \ln (Size) + β_{3} \ln (B / M) + β_{4} PrRet + β_{5} Lev + β_{6} GW / TA + β_{7} ImpDum + {\overset{'}{ε}}_{t}$ .
	1990-2002	2003-2013
IVOL	−0.135	0.060
	−3.793	2.161
ln(Size)	0.061	0.163
	1.641	10.080
ln(B/M)	0.616	0.229
	7.978	4.675
PrRet	0.388	−0.483
	4.382	−7.420
Lev	0.102	0.110
	3.441	5.892
GDWL/TA * Eliminate *	−1.988	0.542
	−4.893	2.344
ImpDum	0.111	−0.047
	0.433	−0.439
Industry control	Yes	Yes
Year controls	Yes	Yes
Adj_R²	0.007	0.039

Conclusion

SFAS 142: Goodwill and Other Intangible Assets, which eliminates goodwill amortization, has generated a significant amount of criticism. Arguments against SFAS 142 have been numerous, including that it is too costly, too difficult to implement, that accountants have not been trained to perform impairment testing, and that it increases the probability of opportunistic earnings management. The alternative proposed, similar to what is seen in the newly adopted ASU 2014-02 for private firms, is to return to the straight-line amortization regime. SFAS 142 has been called a “failed experiment,” and some predict that the future of goodwill accounting may bear greater resemblance to the “good old days” of straight-line amortization.¹⁶ In this study, we directly examine whether SFAS 142 has changed the relevance of goodwill or not. In light of this recent debate about what (if anything) should be done to address the accounting treatment of goodwill, our results are very timely.

Our results strongly suggest that SFAS 142 has dramatically improved the informativeness and value relevance of goodwill, by reducing firm risk and creating an environment of more effective market pricing of risk. We provide strong evidence that, during the periods in which goodwill amortization was required, IVOL is high and the IVOL anomaly is strong. However, under SFAS 142, which eliminates amortization and requires a yearly impairment testing, goodwill is informative, IVOL is lower, and the IVOL anomaly disappears.

The strength and efficiency of capital markets ultimately depend on the quality of information. We find evidence in support of yearly impairment testing of goodwill and against goodwill amortization. Managers may care about changes in idiosyncratic risk to the extent it has implications for asset pricing and, within this context, it could ultimately provide incentives to improve reporting quality. Our study shows that the amortization of goodwill through annual impairment tests as mandated in SFAS 142 may have provided useful information to the markets and helped to reduce the level of idiosyncratic volatility (IVOL) in publicly traded firms.

Footnotes

Appendix

Acknowledgements

We are very grateful to Bharat Sarath (editor), Kashi R. Balachandran and two anonymous reviewers for their helpful comments and suggestions. We also thank Bin Li, Neerav Nagar, Robert Couch, Luis Felipe Palacios (WRDS), Peter Brous, Ruben Trevino, Tina Zamora, Gabriel Saucedo, Ajay Abraham, Niranjan Chipalkatti, and participants of the 2015 JAAF MISB Bocconi Mumbai, 2015 American Accounting Association, and 2015 International Finance and Banking Society annual meetings. We gratefully acknowledge the financial support of Seattle University Albers School of Business & Economics.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

References

Accounting Standard Update (ASU) No. 2014-02. Intangibles—Goodwill and Other (Topic 350), An Amendment of the FASB Accounting Standards Codification, January 2014. FASB.

Aharony

Barniv

Falk

(2010). The impact of mandatory IFRS adoption on equity valuation of accounting numbers for security investors in the EU. The European Accounting Review, 19, 535-578.

Ahmed

A. S.

Guler

(2007). Evidence on the effects of SFAS 142 on the reliability of goodwill write-offs (Working paper). College Station: Texas A&M University.

Amel-Zadeh

Faasse

Meeks

(2013). Has accounting regulation secured more valuable goodwill disclosures? (Working paper). Cambridge, UK: Judge Business School.

Ang

Hodrick

Xing

Zhang

(2006). The cross-section of volatility and expected returns. Journal of Finance, 61, 259-299.

Baker

Savasoglu

(2002). Limited arbitrage in mergers and acquisitions. Journal of Financial Economics, 64, 91-116.

Barth

M. E.

Clinch

(1996). International accounting differences and their relation to share prices: Evidence from U.K., Australian, and Canadian firms. Contemporary Accounting Research, 13, 135-170.

Bens

D. A.

Heltzer

Segal

(2011). The information content of goodwill impairments and SFAS 142. Journal of Accounting, Auditing & Finance, 26, 527-555.

Boennen

Glaum

(2014). Goodwill accounting: A review of the literature (Working paper). Hesse, Germany: Justus-Liebig Universitaat Giessen.

10.

Boldin

Chaudhry

Palacios

L. F.

(2011). Are ADRs different from US stocks? An analysis of idiosyncratic risks. Journal of Money, Investment and Banking, 2008(3), 5-24.

11.

Brennan

M. J.

Schwartz

E. S.

(1985). Evaluating natural resource investment. Journal of Business, 58, 135-157.

12.

Bugeja

Gallery

(2006). Is older goodwill value relevant?Accounting & Finance, 46, 519-535.

13.

Campbell

J. Y.

Lettau

Malkiel

B. G.

(2001). Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. Journal of Finance, 56, 1-43.

14.

Carhart

(1997). On persistence in mutual fund performance. Journal of Finance, 52, 57-82.

15.

Chalmers

Clinch

Godfrey

J. M.

(2008). Adoption of International Financial Reporting Standards: Impact on the value relevance of intangible assets. Australian Accounting Review, 18, 237-247.

16.

Chalmers

Clinch

Godfrey

J. M.

Wei

(2012). Intangible assets, IFRS and analysts’ earnings forecasts. Accounting & Finance, 52, 691-721.

17.

Chen

L. H.

Krishnan

Sami

(2014). Goodwill impairment charges and analyst forecast properties. Accounting Horizons, 29, 141-169.

18.

Dahmash

F. N.

Durand

R. B.

Watson

(2009). The value relevance and reliability of reported goodwill and identifiable intangible assets. The British Accounting Review, 41, 120-137.

19.

Darrough

Guler

Wang

(2014). Goodwill impairment losses and CEO compensation. Journal of Accounting, Auditing & Finance, 29, 435-463.

20.

Diamond

Verrecchia

R. E.

(1991). Disclosure, liquidity, and the cost of capital. Journal of Finance, 46, 1325-1360.

21.

Dye

R. A.

(1986). Proprietary and nonproprietary disclosures. Journal of Business, 59, 331-366.

22.

Dye

R. A.

Verrecchia

R. E.

(1995). Discretion vs. uniformity: Choices among GAAP. The Accounting Review, 70, 389-415.

23.

Easley

O’Hara

(2004). Information and the cost of capital. Journal of Finance, 69, 1553-1583.

24.

Fama

French

(1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3-56.

25.

Financial Accounting Standards Board. (2001a). Statement of Financial Accounting Standards No. 141, business combinations. Norwalk, CT.

26.

Financial Accounting Standards Board. (2001b). Statement of Financial Accounting Standards No. 142, goodwill and other intangible assets. Norwalk, CT.

27.

Financial Accounting Standards Board. (2010a, December). Statement of Financial Accounting Standards No. 142, goodwill and other intangible assets (Topic 350), Accounting Standard Update No. 2010-08. Testing goodwill for impairment. Norwalk, CT.

28.

Financial Accounting Standards Board. (2010b, December). Statement of Financial Accounting Standards No. 142, goodwill and other intangible assets (Topic 350), Accounting Standard Update No. 2010-29. Testing goodwill for impairment. Norwalk, CT.

29.

Financial Accounting Standards Board. (2011). Statement of Financial Accounting Standards No. 142, goodwill and other intangible assets (Topic 350), Accounting Standard Update No. 2011-08. Testing goodwill for impairment. Norwalk, CT.

30.

Financial Accounting Standards Board. (2014, January). Statement of Financial Accounting Standards No. 142, goodwill and other intangible assets (Topic 350), Accounting Standard Update No. 2014-02. Testing goodwill for impairment. Norwalk, CT.

31.

Healy

Hutton

Palepu

(1999). Stock performance and intermediation changes surrounding sustained increases in disclosure. Contemporary Accounting Research, 16, 485-520.

32.

Henning

S. L.

Lewis

B. L.

Shaw

W. H.

(2000). Valuation of the components of purchased goodwill. Journal of Accounting Research, 38, 375-386.

33.

Horton

Serafeim

(2010). Market reaction to and valuation of IFRS reconciliation adjustments: First evidence from the UK. Review of Accounting Studies, 15, 725-751.

34.

Hribar

Collins

(2002). Errors in estimating accruals: Implications for empirical research. Journal of Accounting Research, 40, 105-134.

35.

Hutton

Marcus

Tehranian

(2009). Opaque financial reports, R², and crash risk. Journal of Financial Economics, 94, 67-86.

36.

Jennings

Robinson

Thompson

R. B., II

Duvall

(1996). The relation between accounting goodwill numbers and equity values. Journal of Business Finance & Accounting, 23, 513-533.

37.

Jiang

Yao

(2009). The information content of idiosyncratic volatility. Journal of Financial and Quantitative Analysis, 44, 1-28.

38.

Lange

Fornaro

Buttermilch

(2014). Qualitative assessment of impairment for goodwill and other indefinite-lived intangibles. The CPA Journal, 2014, 22-29.

39.

Lee

(2011). The effect of SFAS 142 on the ability of goodwill to predict future cash Flows. Journal of Accounting and Public Policy, 30, 236-255.

40.

Lee

Yoon

S. W.

(2012). The effects of goodwill accounting on informativeness of earnings: Evidence from earnings persistence and earnings’ ability to predict future cash flows. Journal of Accounting & Finance, 12(3), 124.147.

41.

Leuz

Verrecchia

R. E.

(2000). The economic consequences of increased disclosure. Journal of Accounting Research, 38(Suppl.), 91-124.

42.

Rajgopal

Ventachalam

(2014). R² and idiosyncratic risk are not interchangeable. The Accounting Review, 89, 2261-2295.

43.

K. K.

Sloan

(2012). Has goodwill accounting gone bad? (Working paper). Berkeley: Haas School of Business, University of California Berkeley.

44.

Shroff

Venkataramn

Zhang

I. X.

(2011). Causes and consequences of goodwill impairment losses. Review of Accounting Studies, 16, 745-778.

45.

McDonald

Siegel

(1986). The value of waiting to invest. Quarterly Journal of Economics, 101, 707-728.

46.

Morck

Yeung

(2000). The information content of stock markets: Why do emerging markets have synchronous stock price movements?Journal of Financial Economics, 58, 215-260.

47.

Newey

West

(1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55, 703-708.

48.

O’Hara

(2003). Presidential address: Liquidity and price discovery. Journal of Finance, 58, 1335-1354.

49.

Oliveira

Rodriguez

L. L.

Craig

(2010). Intangible assets and value relevance: Evidence from the Portuguese stock exchange. The British Accounting Review, 42, 241-252.

50.

Pounder

(2013, July) Accounting for Goodwill: Back to the good old days?Strategic Finance, pp. 15-16.

51.

Rajgopal

Venkatachalam

(2011). Financial reporting quality and idiosyncratic return volatility. Journal of Accounting & Economics, 51, 1-20.

52.

Ramanna

Watts

(2012). Evidence on the use of unverifiable estimates in required goodwill impairment. Review of Accounting Studies, 17, 749-780.

53.

Ritter

Wells

(2006). Identifiable intangible asset disclosures, stock prices and future earnings. Accounting & Finance, 46, 843-863.

54.

Roland

Nunez

(2013, December). Notable results from annual goodwill impairment study. Financial Executive, pp. 52-55.

55.

Shalev

Zhang

(2011). CEO Compensation and fair value accounting: Evidence from purchase price allocation (Working paper). St. Louis, MO: Washington University in St. Louis.

56.

Shumway

(1997). The delisting bias in CRSP data. Journal of Finance, 52, 327-340.

57.

Sloan

(1996). Do stock prices fully reflect information in accruals and cash flows about future earnings?The Accounting Review, 71, 289-315.

58.

Verrecchia

(1983). Discretionary disclosure. Journal of Accounting & Economics, 5, 179-194.