The Curious Incident of the Absence of News…and How it Matters for Prices

[This is another of a series of editorials by Executive Editor Andrew Karolyi at the Review of Financial Studies featuring forthcoming or recent papers at the journal. This editorial features “No News is News: Do Markets Underreact to Nothing?” by the University of Chicago Booth’s Stefano Giglio and Kelly Shue, lead article in Issue 27 (12) for December 2014. It was selected as an Editor’s Choice article on the Oxford University Press web site for RFS.]

finance-462986_640-sherlock-pixabayThe 1892 book, The Memoirs of Sherlock Holmes by Sir Arthur Conan Doyle, is a collection of short stories, one of which entitled “Silver Blaze” is a mystery about the disappearance of a famous racehorse the night before a race and the murder of the horse’s trainer. Sherlock Holmes solves the mystery by recognizing that no one that he interviewed in his investigation remarked that they had heard barking from the watchdog during the night in question. The fact that the dog did not bark when it was expected to while the horse was stolen leads Holmes to conclude the perpetrator was not a stranger to the dog and thus would cause it not to bark. This “negative fact” cracked the case.

The opening of Giglio and Shue’s paper makes clear that the tale of “the dog that did not bark” indeed inspired them in part to investigate a novel question: does the absence of news reports and the passage of time contain important information for markets? Their paper offers up many possible settings in which one could explore the instance of no news and how economic agents might react to its non-existence, but they offer up the context of mergers. As Shue writes to me, “the merger context is great for this analysis because we can easily quantify the information content of the passage of time: the time elapsed after a merger announcement without completion or withdrawal offers information about the probability a merger will be completed.” The authors build a simple hazard rate function of the likelihood of completion in the next week conditional on its not having been completed or withdrawn to date. The implied hazard rate function forms a hump shape in a consistent matter across a large sample of U.S. merger deals over a 35-year horizon. The most intriguing part of the paper comes when the authors show that the implied hazard rates can predict weekly returns: the higher the probability of completion, the higher is also the average return.

The study takes yet one more turn in the mystery, making Sir Arthur very proud. A kind of mispricing is revealed. With the passage of time after merger announcement with hazard rates rising, the market seems to underestimate the probability of merger completion and positive surprises arise on average. As even more time passes and hazard rates start to fall, the market overestimates the probability of merger completion and negative surprises ensue. The intensity of this under- and over-reaction is greatest for illiquid stocks which may imply some kind of limits to arbitrage.

When I asked the authors to rationalize the big takeaway from their study, they offered that this evidence of mispricing to the passage of time is “suggestive of a more general phenomenon…it is less exciting than the new stories typically covered by the media.” They added that “underreaction to new news can potentially exacerbate asymmetric information problems in other contexts such as between voters and politicians, between managers and employees, or investors and insiders, where arbitrage is even more difficult.”

RFS Authors Featured on OUP Blog

Authors Chen Xue, Kewei Hou, and Lu Zhang are featured on the Oxford University Press blog in a post titled, “A new benchmark model for estimating expected stock returns,” based on their paper, “Digesting Anomalies: An Investment Approach,” which is forthcoming in RFS. To accompany the post, we’ve made the paper free to read online! Check out the blog post here and read the paper here.

Tail Risk is Not Crash Risk…or Is It?

[This is the second of a new series of editorials by Executive Editor Andrew Karolyi at the Review of Financial Studies featuring forthcoming or recent papers at the journal. This editorial features “Tail Risk and Asset Prices,” by Chicago-Booth’s Bryan Kelly and Michigan State University’s Hao Jiang, lead article in Issue 27(10). It was selected as an Editor’s Choice article on the Oxford University Press web site for RFS.]

stock-exchange-1376107_640-pixabayThe global financial crisis called into question the ability of markets to deal with extreme events. As The Economist magazine put it a few years ago (“Fat-tail attraction,” March 24, 2011), “…tail-risk hedging was the talk of Wall Street in 2008 after global markets nosedived.” The key word here is after. The article points out how anxious investors tried to figure out how they could protect themselves from extreme or “black swan” events that arise outside the mid-range of the distribution of outcomes. The current (2014) unstable global market environment has surely revved up investor interest in uncovering such protections anew.

Excess tail risk is technically defined as a higher-than-expected likelihood of an investment position moving more than two or three standard deviations away from the mean. For many, tail risk simply means any large decline in a portfolio’s value. But a critical first step to hedging tail risk is to measure it. What Kelly and Jiang’s (2014) study proposes is an innovative measure of time-varying tail risk that is implied by the cross-section of stock returns. What they do is assess the magnitude of firm-level price declines every month to come up with a market-wide measure of common fluctuations in tail risk among individual stocks. They demonstrate how their measure is linked to traditional ones of tail risk extracted from equity index options, and how it moves inversely with real economic conditions. But what Kelly and Jiang ultimately seek out–and affirm–is an ability to forecast aggregate stock market returns while also capturing the cross-section of returns. Their back-tested portfolio that spreads U.S. equities on past tail-risk exposures delivers a juicy annual alpha of more than 5%.

Those statistics may capture active investor attention, but allow me to offer a few sobering thoughts the authors shared about their study’s most important findings. “I think it’s important to emphasize that our findings suggest that investors hedge against risk of an aggregate tail event…(and they are) not about stocks with higher crash risk having higher average returns,” states Bryan Kelly. In other words, they interpret their measure as an aggregate crash risk “state variable” contributing to fluctuations in investors’ marginal utility. This is why market participants will sacrifice a big chunk of their return on wealth to insulate themselves against a surge in crash risk. They also caution that their finding is “…not solely about return tails,” and show in their paper a significant correlation between return tails and cash-flow tails, which I think is very interesting. What is the link between tails on asset price returns and tails on the real side of firm behavior? One wonders how these concepts are connected to the surge of recent research on uncertainty shocks and disaster-risk shocks. The authors propose another intriguing connection may be to derivative pricing models that incorporate not only some measure of volatility as an observable state variable, but also build in a dynamic jump intensity. Which could be related to those volatility dynamics…or something else. Indeed, could Kelly-Jiang’s new measure of tail risk represent a useful observational process for jump-risk modeling in derivatives?