Are Hedge Funds Good at Reading The Market>

The tentative answer seems to be "Yes".

According to a new study "Unbundling Hedge Fund Betas" by by Ulloa, Giamouridis, Mesomeris, and Noorizadesh there's evidence that hedge funds increase betas prior to market upswings. Here's the abstract:

This article is concerned with the systematic exposures of equity hedge fund managers. In particular we seek common equity hedge fund systematic exposures through rigorous model selection techniques. We study their time variance to examine if equity hedge fund style characteristics are stable through time. Most importantly, we explore the informational role of manager decisions to shift their exposures to certain styles. Our results suggest that equity fund managers are exposed to three dominant style strategies, namely the 'market', 'value' and 'momentum'. We also discover that there is a considerable degree of variability in the factor exposures over time for the various dominant sources of systematic risk/return. Finally, we show evidence that managers vary their exposures to the 'market' in time to exploit favourable market moves. A similar pattern is however not observed for their 'value' or 'momentum' exposures.

Read the whole thing (downloadable copy at SSRN) here.

HT: All About Alpha

Another Paper Done

I spent most of the last week working on a paper for a conference deadline on Monday. We managed to get the thing done Sunday night (I was in my office until 1:00 a.m. Sunday morning), and planned on submitting it Monday after one last quick read-through. Of course, on Monday morning we got an email announcing that the deadline had been extended. We should have expected it since this happens almost every year for this conference (the Eastern Finance Association annual meeting).

So, we gave the paper one last thorough going over. It was sent out today. It' s early, and the paper will need a lot more work (and polishing) before it's ready to submit to a journal. But the initial results look good, and it's always satisfying to have a finished version (even if it's preliminary) of a paper.

I like working with these coauthors. It's the first time I've worked simultaneously with two fellow alumni of the Unknown Alma Mater, and the initial experience has been very, very good.

Damn The Data - Full Speed Ahead.

I finally got into heavy data grinding mode for a conference submission that's due on Monday. I should be done with my part of the work and will have tables to show today by 5. My coauthor is working on another paper with a deadline of tomorrow, so he won't even look at this stuff until Friday afternoon at the earliest. The deadline is not until until Monday, so we have time.

To do my part, I had to update the data set to reflect the latest year or so of data. I thought "this should be easy." That thought was four days and about 30 hours of programming ago. It turns out that the final data set resulted from three intermediate steps, which also required data sets to be updated.

The end result was that I ended up running programs last night at 12:00 in between watching Mixed Martial arts on TV. All in all it's not bad -- I'm a big fan since I did a bit of competitive judo college (pretty low level stuff, and I wasn't that good) and took taekwondo in high school.

But I'd have been happy to Tivo it for a reasonable hour if I wasn't already up dealing with data issues.

I once had a fellow grad school student who said that "empirical research is easy." If we ever cross paths again (he went into industry) I'm gonna go upside his head with a full hard copy of the SAS manuals.

Another Hit

Just got an email from a coauthor forwarding an acceptance letter for a journal on a small piece we've been working on for a while. It's in am p.k., but definitely second-tier journal, But since I'm not yet tenured, my attitude is that ANY publication is a good one (what's the old saying - "a Dean is someone who can't read but can count").

At Unknown University, we do our annual faculty productivity reports on a June-June basis. So this means I start the new year with a slap single already on the scpreboard.

Rollin, Rollin, Rollin, Keep Them Doggies Rollin

I and a couple of fellow alums of the Unknown Alma Mater have been talking about a project for about the last 6 months. We finally started working on it in earnest about a month and a half ago. So far, we've got initial result (and kick-hiney ones, if I may say so), a literature review, some pretty good looking but simple charts, and (by the end of this week) a couple of tables. This might be the fastest initial progress on a project I've ever seen.

So far, it's been a good summer. I've almost finished reworking a paper that got rejected (it'll be sent out to a nearly-top-tier journal in the next week or two), started two new projects that we've already got interesting initial result on (they'll be done in time to submit to a conference in September), and moved a fourth project from the "vaporware" stage to the point where it could also possibly be conference ready by the end of September.

But I have to be careful what I say, because coauthors on two of the projects are regular readers of the blog. So, I can't gripe about them here. Not that I have to - they've all been (if for different reasons) pleasures to work with. Keep up the good work, y'all.

Is There Predictive Power In The Option-Implied Volatility Smirk?

Apparently, the answer is yes. Xiaoyan Zhang (of Cornell), Rui Zhao (of Blackrock Inc.), and Yuhang Xing (of Rice University) recently conducted a study titled "What Does Individual Option Volatility Smirk Tell Us about Future Equity Returns?" Here's their abstract (emphasis mine):

The shape of the volatility smirks has significant cross-sectional predictive power for future equity returns. Stocks exhibiting the steepest smirks in their traded options underperform stocks with the least pronounced volatility smirks in their options by around 15% per year on a risk-adjusted basis. This predictability persists for at least six months, and firms with steepest volatility smirks are those experiencing the worst earnings shocks in the following quarter. The results are consistent with the notion that informed traders with negative news prefer to buy out-of-the-money put options, and that the equity market is slow in incorporating the information embedded in volatility smirks.

Basically, they calculate the "volatility smirk" (the difference between the implied volatility for At-The-Money (ATM) calls and Out-of-The-Money (OTM) puts) for individual stocks. They then sort firms into portfolios based on deciles of the smirk, and compare returns for the various portfolios (or for "hedge portfolios" constructed by shorting the "high smirk" decile and going long the "low smirk" decile) . The logic for this approach is the hypothesis that informed traders with negative news will choose to buy OTM puts, thereby causing a divergence in the IV of the puts vs for the call.

All in all, a pretty cool paper showing how information flows across markets. Given some work I'm doing with options data, I found it to be particularly timely.

Read the whole thing here.

HT: CXO Advisory Group

Finance: 0% Politically Correct

Inside Higher Education just highlighted some research done by Neil Gross, a sociology professor at Harvard, and Solon Simmons, a sociology professor at George Mason University. Here's the "punch line" from the summary:

Humanities and social science fields tend to have higher politically correct rankings, while professional and science disciplines do not. The table that follows is in order of political correctness. Psychology is the only field where a majority of professors are politically correct. Four fields — finance, management information, mechanical engineering and electrical engineering — had no one who was politically correct (emphasis mine).

In addition, the five disciplines next least likely to be politically correct were Biology(2%), Computer Science (3%), Accounting (4%), Marketing (4.5%), and Economics (4.7%). All in all, these numbers aren't surprising: I can't imagine what a politically correct approach tom teaching finance would entail (maybe an NPV of a project that differs based on the race, gender, or class of the project manager?).

Read IHE's summary here, and get the original article here.

HT: Marginal Revolution, who I'm less politically correct than.

Cash Flows, Earnings Quality, and Stock Returns

One of my original purposes in starting this blog was to create a place to keep track of things I came across on the Web that might be useful in my classes. I just found another one: "Cash Flow Is King: Cognitive Errors by Investors" by Todd Houge (of U of Iowa) and Tim Loughran of Notre Dame. Here's the abstract:

When investors fixate on current earnings, they commit a cognitive error and fail to fully value the information contained in accruals and cash flows. Extending the accrual anomaly documented by Sloan [1996], we identify significant excess returns from a cash flow-based trading strategy. The market consistently underestimates the transitory nature of accruals and the long-term persistence of cash flows. We find that the accrual anomaly derives from the poor performance of high accrual firms, which are more likely to manage earnings. Combining the accrual and cash flow information also reveals that investors misvalue the quality of earnings. Contrary to Fama [1998], these anomalies are robust to the three-factor model with equally or value-weighted portfolio returns.

Houge and Loughran find that markets undervalue firms with high operating cash flows to asset ratios and overvalue those with low cash flow/asset ratios. Somewhat surprisingly, Cash Flow/Assets is negatively correlated with Book/Market ratios (i.e. a firm with low CF/Assets is likely to also be a high Book/Market firm), so this is not just another way of capturing the value anomaly. They also find that the negative returns for high accrual firms are mostly evident among firms with the highest accruals.

But the really interesting finding in the paper has to do with earnings quality. The high cash flows/low earnings combination (basically, low accruals) indicates high earnings quality, while low cash flows and high earnings (high accruals) proxies for low earnings quality. When they compare returns to high cash flow/low earnings firms to those with low cash flows and high earnings, the high CF/low earnings firms outperform their opposite numbers by almost 16% per year on a risk adjusted basis. Not too shabby.

The paper was published in the Journal of Psychology and Financial Markets in 2000, but you can get an ungated version here.

Investor Sentiment and Stock Returns

Here's a paper that will definitely make it into the class readings the next time I teach investments. In their paper "How Does Investor Sentiment Affect the Cross-Section of Stock Returns?", Malcolm Baker, Johnathan Wang and Jeffrey Wurgler investigate whether factors that make firms' securities harder to arbitrage (like firm size) result in differences in returns between high and low-sentiment market periods.

They construct an index of sentiment based on factors like share turnover on the NYSE, the dividend premium, the volume and first-day returns from IPOs, discounts on closed-end funds, and the equity share of news issues. Here's the conclusions section from their paper (normally I post the abstract, but this section gives a better feel for their results):

Investor sentiment affects the cross-section of stock returns. For practitioners, the main takeaway is that the cross-section of future stock returns varies with beginning-of-period investor sentiment. The patterns are intuitive and consistent with economic theory. When sentiment is high, stocks that are prone to speculation and difficult to arbitrage, namely stocks of young, small, unprofitable, non-dividend-paying, highly volatile, distressed, and extreme growth firms, tend to earn relatively lower subsequent returns. When sentiment is low, the reverse mostly holds. Most strikingly, several characteristics that exhibit no unconditional predictive power actually exhibit predictive power once we condition on beginning-of-period investor sentiment. These results suggest there is much to be done in terms of understanding more about investor sentiment and its effects.

The paper is downloadable from SSRN here.

HT: CXO Advisory Group

Back In The Research Saddle Again

What a relief to have the CFA exam behind me. So far today, I've worked on two projects and talked with two coauthors. They should make for interesting projects - both are using existing data in entirely new settings, and both seem to have the potential for ending up in good quality journals.

One project is with a former grad school classmate that I've wanted to do a paper with since we graduated. On this one, I get to play "data monkey" (it involves a LOT of data manipulation, and he'll be responsible for the initial writeup. On the other project, I get to use some stuff I worked on several years ago that never went anywhere, but with a new data set. The paper is with a new coauthor that I met at a recent conference. In this one, I'll end up doing some data work on the front end, and will probably end up doing more of the writing.

I have high hopes for both projects. They're both "core" finance (dividend policy and bankruptcy), but both papers approach the issues in what I think are novel ways. And to make it more fun, I get to work with two new data sets, and with two new coauthors. I'll have to be careful not to say too much more, since both are regular readers of the blog.

So, as far as I'm concerned, the summer research season is now officially open. So, as Elmer Fudd would say, "Be vewwy vewwy qwiet. I'm hunting data."

Time to close up shop for the day and go on a bike ride. Either that, or rent advertising space on my overly large posterior.

Identifying Overvalued Equity

here's an interesting paper by Daniel Beneish of Indiana U. and Craig Nichols of Cornell titled "Identifying Overvalued Equity."

Here's the abstract:

Jensen (2005) argues that overvaluation changes the behavior of managers in ways that increase agency costs, but suggests that overvaluation is difficult to identify. We show that observable characteristics of changes in managers' accounting, operating, investing and financing decisions can be used to predict two likely consequences of overvalued equity: future stock price declines and overstatement of accounting earnings. In particular, we show that an overvaluation score (O-Score) that combines proxies for earnings overstatement, prior merger activity, excessive stock issuance, and the manipulation of real operating activities identifies firms with one-year-ahead abnormal price declines averaging -27%. We also estimate a model that integrates these various attributes to predict accounting restatements associated with fraud. In light of the costs associated with overvalued equity, the findings that firm characteristics can be used to identify overvalued equity should interest researchers who study overvaluation and professionals who oversee management on behalf of investors.

RTWT here.

It's an interesting paper, because it uses publicly available information to identify firms with high probabilities of negative returns. While it's probably not that applicable to individual investors, I can see their approach being of use to short-sellers (or those running long-short funds).

Fantastic Review of Analyst Forecast Research

If you do research in analyst forecasts, this one;s a must read: The Financial Analyst Forecasting Literature: A Taxonomy with Suggestions for Further Research by Sundaresh Ramnath, Steve Rock, and Philip Shane in the International journal of Forecasting (2008)

Here's the abstract:

This paper develops a taxonomy of research examining the role of financial analysts in capital markets. The paper builds on the perspectives provided by Schipper [Schipper, K. (1991). Analysts' forecasts. Accounting Horizons, 5, 105-131] and Brown [Brown, L. (1993). Earnings forecasting research: Its implications for capital markets research. International Journal of Forecasting, 9, 295-320]. We categorize papers published since 1992, describe the research questions addressed, and suggest avenues for further research in seven broad areas: (1) analysts' decision processes; (2) the nature of analyst expertise and the distributions of earnings forecasts; (3) the information content of analyst research; (4) analyst and market efficiency; (5) analysts' incentives and behavioral biases; (6) the effects of the institutional and regulatory environment (including cross-country comparisons); and (7) research design issues.

Note: although it's a forthcoming paper, there's a downloadable version available from SSRN here.

Bernanke's Bubble Laboratory (from the WSJ)

There's a great article on the front page today's Wall Street Journal titled "Bernanke's Bubble Laboratory" about three economists at Princeton (Harrison Hong, Wei Xiong, and Markus Brunnermeier) who were hired by Bernanke prior to his becoming Fed Chief. They do research on market bubbles.

This one's a keeper, and will probably end up being discussed in a lot of investment and markets classes. It does a pretty good job of laying out some of their research. Here are a smattering of things these three have found that are discussed in the article:

• Major innovations (or big changes) like the rise of the Internet in the mid/late 1990s and the recent credit innovations cause large disagreements between investors about fundamental valuations. Difficulties and costs associated with shorting overvalued stocks allows the most bullish investors to drive prices.
  
• In markets with fewer shares available (like China's A/B shares markets), optimists can really push the prices up
• Skeptics that might drive prices back down won't move in a booming market until they're pretty sure other skeptics will also be on board. So, when the "pessimists" finally start moving, prices can drop much more quickly than they rose.

All in all, a worthy piece, and one that's digestible by people without a graduate degree in finance. Read it here (pay subscription may be required).

Gotta go - classes are done, grades have been handed in, and I have CFA to study for and my own research to work on. It may not be focused on bubbles, but I still like it...

Options and The Volatility Risk Premium

Classical mean-variance portfolio theory assumes that investors are risk-averse. Here's a paper that examines the "volatility risk" premium using options data, titled "The Price of Market Voilatility Risk", by Jefferson Duarte and Christopher Jones:

We analyze the volatility risk premium by applying a modified two-pass Fama-MacBeth procedure to the returns of a large cross section of the returns of options on individual equities. Our results provide strong evidence of a volatility risk premium that is increasing in the level of overall market volatility. This risk premium provides compensation for risk stemming both from the characteristics of the option contract and the riskiness of the underlying equity. We also show with a large scale Monte Carlo simulation that measurement error in option prices and violations of arbitrage bounds induce highly economically significant biases in the mean returns of options. In fact, our simulation results demonstrate that biases can be up to several percentage points per day. These large biases can lead researchers to faulty conclusions with respect to both the magnitude of the volatility risk premium and the sign of expected option returns.

Read the whole thing here.

While their paper does a good job of showing how option returns in academic studies can be biased by bid-ask spread, they also give some nice results on just how big the "volatility premium" may be (they're not the first to find this, but I like their results nonetheless).

The following table from the paper, shows mean returns on S&P 500 index options at various maturities (Short, Medium, Long) and degrees of of moneyness (In The Money, At The Money, Out of The Money). The figures are in basis points/day and are adjusted for bid-ask spread biases. What I found most striking were the results for short positions on short-term deep out-of-the-money puts (4% return per day) and deep OTM calls (3-9% per day).

Now THAT's definitely a table suitable for use in class.

HT: CXO Advisory Group

Informed traders and Optons Markets

If you were an informed trader, would you trade in the options market or in the market for the underlying asset? Finance theory says you'd trade in the options market because of increased leverage.

Now here's another paper that supports this idea. In their March 2008 paper Xiaoyan Zhang, Rui Zhao and Yuhang Xing look at whether relatively expensive put options can be used as "bad news" indicators. Here's the abstract of their paper:

The shape of the volatility smirks has significant cross-sectional predictive power for future equity returns. Stocks exhibiting the steepest smirks in their traded options underperform stocks with the least pronounced volatility smirks in their options by around 15% per year on a risk-adjusted basis. This predictability persists for at least six months, and firms with steepest volatility smirks are those experiencing the worst earnings shocks in the following quarter. The results are consistent with the notion that informed traders with negative news prefer to buy out-of-the-money put options, and that the equity market is slow in incorporating the information embedded in volatility smirks.

Read the whole thing here.

In case you're not familiar with the term, the volatility "smile" refers to the phenomenon that implied volatility increases for options that are further out of the money. If the increase in implied volatility is greater on one side than on the other, the pattern is known as a volatility "smirk". In the case of this paper the smirk is used as an indicator of the degree to which puts or calls are relatively expensive. For example, if calls are relatively more expensive, that is taken as an indicator that informed traders have been buying calls because they have positive information about a stock, with expensive puts being an indicator that traders possess bad news.

In addition to predicting subsequent returns, the authors also find that firms with the most expensive put options are more likely to have the worst negative earnings shocks in the following quarter.

All in all, a pretty cool paper that indicates how information from one market can predict movements in another.

HT: CXO Advisory Group

Is Valuation Driven More By Cash Flows or Discount Rates?

Here's one for my next semester's Security Analysis class: In "What Drives Stock Price Movement?" Long Chen and Xinlei Zhao use analyst forecast and stock market data to examine whether stock price changes are associated more with changes in cash flows or discount rates. Here's the abstract (note: the emphasis is mine):

A central issue in asset pricing is whether stock prices move due to the revisions of expected future cash flows or/and of expected discount rates, and by how much of each. Using consensus cash flow forecasts, we show that there is a significant component of cash flow news in stock returns, whose importance increases with investment horizons. For horizons over three years, the importance of cash flow news far exceeds that of discount rate news. These conclusions hold at both firm and aggregate levels, and diversification only plays a secondary role in affecting the relative importance of cash flow/discount rate news. The conventional wisdom that cash flow news dominates at the firm level but discount rate news dominates at the aggregate level is largely a myth driven by the estimation methods. Finally, stock returns and cash flow news are positively correlated at both firm and aggregate levels.

HT: CXO Advisory Group

Empirical Finance Research

When Financial Rounds first started out, a number of the bigger names in the finance/econ blogoshpere were nice enough to mention this site. So, whenever possible I try to pay the favor "forward" by doing the same for newer blogs. The latest new finance blog of note is titled Empirical Finance Research, which is intended to (in the authors' own words):

1. Highlight research from the academic finance archives that may be useful to investors.
2. Serve as a venue for the contributors to share our thoughts and insights with others who enjoy empirical finance research.
3. Act as an outlet for authors or readers who would like to showcase their latest research.

It's authored by three guys (two of which are currently pursuing Ph.D.s in finance), and focuses on investment applications of current academic finance research. Good job, gentlemen, and keep up the posting. After all, the world needs more blogs run by finance PhDs.

Revise and Resubmit

For those of you who aren't academics, the publishing process (for "refereed" journals) goes like this:

1. You send a piece out to a journal
2. The editor reviews it to find one or more suitable referees (people who supposedly have some background in that area)
3. The referee goes over the piece with a fine-tooth comb. They make comments/suggestions about everything from the statistical methodology you've used to the writing style to the basic premises of the piece. Pretty much anything is fair game, stopping just short of "you dumb, you ugly, and yo momma dress you funny")
   
4. They write a referee report that can be either clear rejection (i.e. go away or we will taunt you again), revise and resubmit (make the following suggested changes and we'll look at it again) or accept (almost never done on the first round).
5. If it's a revise and resubmit, you attempt to make the changes (or at least address the issues raised in some way) and then resubmit it to the journal. The editor sends it back to the referee(s) for another look. Sometimes they even bring in a new referee.
   
6. Steps 4 and 5 can be repeated several times (the record I've heard at a finance journal is 4 rounds).

Most times, the referees' comments truly improve the paper (they should, since they're chosen ostensibly because they know the topic). But once in a while, you get a referee who seems like an idiot. Or alternately, you get two referees who ask you to do two things that are mutually exclusive. In that case, it can feel like this:

HT: Mike Munger

A Great Review of Analyst Forecast Research

As most academics know, a good survey article is worth its weight in gold. So, here's a good one on analyst forecast research (you can send money later)

Sundaresh Ramnath, Steve Rock and Philip Shane have a piece in the 2008 International Journal Of Forecasting entitled "The Financial Analyst Forecasting Literature: A Taxonomy with Suggestions for Further Research." In it, they catalog and organize about 250 research articles on various facets of the equity analysis process done since 1992 (it builds on earlier pieces by Schipper (991) and Brown (1993)). They arrange their review into the following topics:

• How do analysts make decisions (i.e. what information do they use, how does their environment affect them, etc...)
• What is the nature of analysts expertise (i.e. how do you measure it, is there herding, etc...)
• Information content (how informative are analysts forecasts, is there information in forecasts over an above other available information)
• Market efficiency (how much is extant information reflected in forecasts, do stock prices reflect the info in forecasts, etc...)
• What incentives or behavioral biases affect or are present in analyst forecasts
• How does the regulatory environment affect the forecasting process
• How statistically valid are analyst forecast studies?
  

All in all, it's a very thorough piece, and I suspect it'll end up being read and cited by quite academics. In particular, I'd recommend it to grad students who are trying to get up to speed on this very broad literature.

The IJF piece is for subscribers only, but there's an ungated version on SSRN here.

HT: CXO Advisory Group.

More on The Accrual Anomaly

Here's another paper on "tradable" patterns in stock returns. The CXO Advisory Group recently put up a summary of the study titled "Repairing the Accruals Anomaly" by Hafzalla, Lundholm and Van Winkle. The paper examines the pattern that stock market performance of firms with low accruals (i.e. the difference between the firm's earnings and cash flows) is significantly greater than the performance of their higher accrual counterparts. It does a pretty good job of examining Sloan's "Accrual Anomaly" with a few tweaks:

• It corrects for the extent to which the firm is financially healthy, using Piotrowski's "financial health" indicator.
• It measures accruals in relation to earnings rather than to assets
  

Their findings are that the accrual anomaly does a better job of sorting out investment performance for financially healthy firms. Their results are pretty strong (note- the following is CXO's summary):

• A hedge strategy that is long (short) firms of high (low) financial health (ignoring accruals) generates an average size-adjusted annual return of 9.36% across the entire sample.
• After excluding firms with the lowest financial health scores, a hedge strategy that is long (short) the 10% of firms with the lowest (highest) traditional accruals generates an average size-adjusted annual return of 13.64%, with 7.98% coming from the long side
• Using the total sample, a hedge strategy that is long (short) low-accrual, high financial health (high-accrual, low financial health) firms produces an average size-adjusted annual return of 22.93%, with a 14.92% from the long side. (See the first chart below.)

Here's a pretty good grapic of size adjusted abnormal returns on the various portfolios. Note that financially healthy firms with low accruals earn a size-adjusted abnormal return of about 15% annually, while those in the "financially unhealthy/high accruals" group have negative size adjusted returns of about almost 10% a year.





Read the paper here.

It looks like my students in Unknown University's Student Managed Fund will have another indicator to look at next semester.