Idiosyncratic Volatility and Firm‐Specific News: Beyond Limited Arbitrage

• Published date: 01 December 2016
• DOI: http://doi.org/10.1111/fima.12135
• Date: 01 December 2016
• Author: R. Jared DeLisle,Adam R. Smedema,Nathan Mauck

Idiosyncratic Volatility and Firm-Specific

News: Beyond Limited Arbitrage

R. Jared DeLisle, Nathan Mauck, and Adam R. Smedema∗

Weexamine the relation between idiosyncratic volatility and returns aroundnews announcements.

Mispricing is most likely to occur during news announcements. If idiosyncraticvolatility generates

a limit to arbitrage, then the negative relation between returns and news volatility should be

stronger than the relation to nonnews volatility. Instead, we find nonnews volatility has a robust

negative relation to returns and lackskey features expected if volatility were a reflection of limits

to arbitrage.Pricing of nonnews volatility is related to lottery-like features of a stock’sreturn. Our

results suggest that volatility has a price effect beyond a limit to arbitrage.

Since classical asset pricing theory predicts either no relation or an intuitive positive relation

(Levy, 1978; Merton, 1987) between returns and idiosyncratic risk, Ang et al.’s (2006, 2009)

finding of a negative relation between average monthly returns and short-term idiosyncratic

volatility presents a substantial puzzle.1While idiosyncratic volatility is typically assumed to

capture firm-specif ic news announcements, little effort has been spent relating the pricing of

idiosyncratic volatility to actual firm-specific news announcements.2This omission is particularly

stark since one of the most promising explanations for the negative price relation, the limited

arbitrage explanation from Stambaugh, Yu, and Yuan (2015), has an important role for firm-

specific news.3

Since volatility limits demand from arbitrageurs (Barberis and Thaler, 2003; Pontiff, 2006),

stocks with high idiosyncratic volatility are potentially mispriced and the relation between

We thank the editor (Marc Lipson), an anonymous referee, Ajay Bhootra, Arthur Cox, Richard Followill, and seminar

participants at the University of Northern Iowa, the 2016 Southwest FinanceAssociation meeting, and the 2016 Eastern

Finance Association meeting.

∗R. Jared DeLisle is an Assistant Professor in the Department of Economics and Finance at Utah State University

in Logan, UT. Nathan Mauck is an Assistant Professor in the Department of Finance at the Henry W. Bloch School of

Management at the Universityof Missouri – Kansas City in Kansas City,MO. Adam R. Smedema is an Assistant Professor

and the Tim Williams Junior Faculty Fellow in the Department of Finance at the University of Northern Iowa in Cedar

Falls, IA.

1We use the term “short-term idiosyncratic volatility” to denote the measure we use throughout this study. Following

Ang et al. (2006), we use returns at a monthly granularity, but estimate volatility over the previous month using daily

returns. We call this “short-term” to stress that we only consider this one particular methodology and to distinguish this

method from the other common measure of idiosyncratic volatility that uses a long time series of monthly returns to

forecast future monthly volatilityfrom generalized autoregressive conditional heteroskedasticity models (Guo, Kassa, and

Ferguson, 2014; Chicherna, Ferguson,and Kassa, 2015). Results from previous studies, such as Fu (2009) and Peterson

and Smedema (2011), suggest that these are two different phenomena despite their similar construction.

2The notable exception to this is Bali, Scherbina, and Tang (2011), who look at a two-year period of corporate press

releases and find the presence of these releases are positively related to increases in idiosyncratic volatility.Their research,

like Boehme, Danielsen, and Sorescu (2006) and Boehme, Danielsen, Kumar, and Sorescu (2009), primarily focuses on

using Miller’s(1977) difference of opinion to explain the negative price of idiosyncratic volatility, which is outside the

scope of our research.

3While this explanation is most closely attributable to Stambaugh et al. (2015), one could drawsimilar conclusions from

the results of many other studies, such as Brav, Heaton, and Li (2010), Duan, Hu, and McLean (2010), and Bhootra and

Hur (2015), who link the pricing of other overvaluation anomalies to idiosyncratic volatility.

Financial Management •Winter 2016 •pages 923 – 951

924 Financial Management rWinter 2016

idiosyncratic volatility and subsequent returns is due to the correction of the mispricing. This

explanation leads to four expected features of the pricing of idiosyncratic volatility.4First, since

short selling is more constrained than stock purchases, high-volatility stocks are, on average,

overvalued and will have negative alphas as the stocks’ prices fall to correct the overvaluation.

In addition, stocks with low idiosyncratic volatility are fairly priced and should have alphas of

approximately zero since their low volatility does not limit arbitrageurs from correcting mis-

pricing. Moreover, among potentially overvalued stocks (e.g., stocks with high short interest),

the negative relation between volatility and subsequent returns is particularly strong. Finally,

among potentially undervalued stocks (e.g., stocks with high book-to-market ratios), the relation

between volatility and subsequent returns is potentially positive as the stock’s price must rise to

correct the undervaluation. Since these four features are well established in the literature, the

limited arbitrage explanation seems to provide a simple and plausible explanationfor the negative

volatility and returns relation.

In this study, we evaluate the limited arbitrage explanation of the pricing of idiosyncratic

volatility by examining actual firm-specific news announcements. The pricing of idiosyncratic

volatility,by this explanation, should be related to fir m-specific news. While limited arbitrage is a

necessary condition for mispricing, it is not sufficient. Some impetus must create a divergence of

prices from fundamental values that arbitrageurs fail to expeditiously correct. Since firm-specific

news moves prices (Ryan and Taffler, 2004; Bali, Scherbina, and Tang, 2011), material firm

announcements should increase the likelihood of short-term mispricing (Ball and Brown, 1968;

Pritamani and Singal, 2001). Thus, the four features associate with the pricing of idiosyncratic

volatility should be strongest when volatility is contemporaneous with news announcements.

Our examination of firm-specif ic news announcements suggests that the limited arbitrage ex-

planation cannot fully explain the pricing of idiosyncratic volatility. In this study,our f irm-specific

news data are the announcement or declaration dates for: 1) share repurchases, 2) seasoned equity

offerings (SEOs), 3) debt issuances, 4) as an merger and acquisition (M&A) target, 5) an M&A

acquirer, 6) an insider trade, 7) an analyst recommendation, 8) earnings, 9) dividends, or 10)

a stock split. The extant literature indicates that all of these events impact a firm’s stock price.

We decompose short-term idiosyncratic volatility into “news volatility” and “nonnews volatil-

ity.” News volatility is the idiosyncratic volatility related to firm-specif ic news announcements.

Nonnews volatility is the idiosyncratic volatility unrelated to news announcements. The pricing

of news volatility supports the limited arbitrage explanation as it demonstrates the four features

identified above, but its pricing is weak and not robust.

Contrary to the limited arbitrage explanation, we find that idiosyncratic volatility on nonnews

days is more strongly related to future returns than the volatility on newsdays. Further, the pricing

of nonnews volatility does not demonstrate all four features expected by the limited arbitrage

explanation. The negative relation between returns and nonnews volatility when estimated with

zero-cost portfolios that are short low-volatility stocks and long high-volatility stocks is sig-

nificantly negative, both statistically and economically. The monthly average alpha of our base

model is –66 bp (t-statistic of 4.08). However, low nonnewsvolatility por tfolios havesignif icantly

positive alphas (12 bp per month, t-statistic of 2.25). Furthermore, while the price of nonnews

volatility increases somewhat across stocks with different levels of overvaluation, the increase is

certainly not monotonic and the negativerelation is not pooled in only the most overvalued stocks.

Finally, when forming a portfolio with the least undervalued stocks, nonnews volatility-sorted

4Features one, three, and four are explicitlyenumerated by Stambaugh et al. (2015). We believe the second feature is also

consistent with the limited arbitrage explanation.