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.

Spam, Spam, Spam, Spam

A couple of years back, I was on the train (coming back from a consulting gig), and, being an extrovert, I started talking with a guy sitting next to me. He was a "stock tout". In other words, he was one of those guys who sent out emails pushing one stock or another. He claimed it was a pretty profitable business.

Now I have some evidence backing him up.
Here's a pretty interesting piece on the market effects of internet stock spam spam. A couple of years ago, Well, Frieder and Zittrain did a study titled Spam Works: Evidence from Stock Touts and Corresponding Market Activity. They found that on spammers "touting" (i.e. pushing) a stock has some pretty significant effects on the touted stock's price and trading volume. Here's the abstract (emphasis mine):

We assess the impact of spam that touts stocks upon the trading activity of those stocks and sketch how profitable such spamming might be for spammers and how harmful it is to those who heed advice in stock-touting e-mails. We find convincing evidence that stock prices are being manipulated through spam. We suggest that the effectiveness of spammed stock touting calls into question prevailing models of securities regulation that rely principally on the proper labeling of information and disclosure of conflicts of interest as means of protecting consumers, and we propose several regulatory and industry interventions.

Based on a large sample of touted stocks listed on the Pink Sheets quotation system and a large sample of spam emails touting stocks, we find that stocks experience a significantly positive return on days prior to heavy touting via spam. Volume of trading responds positively and significantly to heavy touting. For a stock that is touted at some point during our sample period, the probability of it being the most actively traded stock in our sample jumps from 4% on a day when there is no touting activity to 70% on a day when there is touting activity. Returns in the days following touting are significantly negative. The evidence accords with a hypothesis that spammers "buy low and spam high," purchasing penny stocks with comparatively low liquidity, then touting them - perhaps immediately after an independently occurring upward tick in price, or after having caused the uptick themselves by engaging in preparatory purchasing - in order to increase or maintain trading activity and price enough to unload their positions at a profit. We find that prolific spamming greatly affects the trading volume of a targeted stock, drumming up buyers to prevent the spammer's initial selling from depressing the stock's price. Subsequent selling by the spammer (or others) while this buying pressure subsides results in negative returns following touting. Before brokerage fees, the average investor who buys a stock on the day it is most heavily touted and sells it 2 days after the touting ends will lose close to 5.5%. For those touted stocks with above-average levels of touting, a spammer who buys on the day before unleashing touts and sells on the day his or her touting is the heaviest, on average, will earn 4.29% before transaction costs. The underlying data and interactive charts showing price and volume changes are also made available.

If you're not convinced (or even if you are), I have a couple of names of people who are related to the former finance minister of Nigeria who need your help getting money out of the country (and are willing to share the profits with you). I'll give them to you for a small finder's fee. Just send me the routing number on your bank account and I'll take care of it electronically.

HT: The Psi-Fi Blog

Of course, with a title like that, it was inevitable

The Summer Winds Down

It's been a busy week here in Unknownville. Unknown University starts up next week (we start later than most), so we've had a rash (or is that a plague?) of meetings. I'm still juggling several papers (writing a lit review for one, doing data work for another, and some polishing/editing for a third) and sequentially disappointing my coauthors.

Ah well - them's the breaks. But I have to be nice, since coauthors on each paper read the blog. So fear not, coauthors - my parts will be done in good time.

Along those lines, I just received a bunch of results from one coauthor, some of which are pretty interesting. It's an area that I had an unsuccessful paper in several years ago, and she usues s new and difficult data set that allows us to revisit the topic in a very new way. WE've got a good story and good results, and it'll gp to the head of the pile, since we're sending the paper to an upcoming conference (the Eastern Finance Association annual meeting) for which the deadline is next week. I hope it gets accepted since the Unknown Wife and I plan on making it a little vacation (she's neve been to Miami). We're pretty confident - its a good idea,goood data, and believable results (And we know the program chair).

After all, that's potentially one of the perks of academia - you can sometimes have the university partially fund your vacations by choosing your conferences wisely.

Finally, I just got an email telling me I've won 12,841,340 Euros in an inernational lottery that I don't recall entering. I have to share it with 14,000 winners, but it'll give my students something to calculate when I cover foreign exchange rates.

UnknwonDaughter is now back in scnool and once agaion well ahead of her classmates.. And Unknown Baby boy continues t alternately make us laugh and make us gag as he exceeds manufacturers capacit on his diapers (or as we call them "Code Brown!). Ah well - the wages fo hchild rearing.

Peace

-UP

The Difficulty of Measuring the Gains To Fundamental Research

Here's a paper by Bradford Cornell that I've had in my in box for a while. It's titled "Investment Research: How Much Is Enough?" Here's the abstract

Aside from the decision to enter the equity market, the most fundamental question an investor faces is whether to passively hold the market portfolio or to do investment research. This thesis of this paper is that there is no scientifically reliable procedure available which can be applied to estimate the marginal product of investment research. In light of this imprecision, investors become forced to rely on some combination of judgment, gut instinct, and marketing imperatives to determine both the research approaches they employ and the capital they allocate to each approach. However, decisions based on such nebulous criteria are fragile and subject to dramatic revision in the face of market movements. These revisions, in turn, can exacerbate movements in asset prices.

I raises some interesting issues about the difficulties in measuring gains to fundamental research. To name a few:

• The difficulty in measuring "abnormal" performance", given the stochastic (i.e. random) nature of stock returns
• The time-varying nature of any possible gains to analysis (funds and strategies change over time).
• Given the needs for sample size and duration necessary to get high levels of statistical significance, most findings are of pretty low confidence
• The ad hoc nature of many analysis strategies and the role that judgement plays
  

It's worth reading, and give some good points for discussion in a class module on efficient markets (and the related topic of "anomalies" like the size and value effects). You can read the working paper on SSRN here

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.

Research Love/Hate

I love doing research. Actually, I like finding out new stuff. But sometimes the research process makes me rue the fact that I work on a dry campus.

Like this week.

I've been working on a paper where I needed to update the data on. Since the latest version was a rush job put together for a conference (yes - this happens a lot), I decided to go back and check every line of my program (always a good thing to do). I also wanted to do the anal-retentive (I know, that's redundant. - except in research, where it's expected) thing where I can relate what happens to my sample at each filtering step. While doing this, I found out that I'd used the wrong data code for one of my variables - one of my MAIN variables. So, the whole data set was, in a word, crap.

After taking a deep breath, I made the corrections and redid most of the analysis. Luckily, the results still held, with minor modifications.

Then I discovered a minor discrepancy in the number of observations at one step. It's likely not very important at all. But I need to track it down before I go further. So, since my coauthor reads the blog, he'll just have to wait another day or so. But I'm getting close, so I should be able to finish my part of the work and ship it off to my coauthors in another day or two.

Then a coauthor on another paper told me that she'd found an error she made in her coding. In this case, when she found and corrected her error, it quadrupled our sample size. If you're an empiricist, you know how much an increase from about 90 observations to about 400 means. If not, let's just say it's a big deal to the alpha nerds among us (and that description applies to most of my friends).

So, like most days in the research salt mines, there's some good and some bad.

Now about that "dry campus" thing...

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.

Advice From A Journal Editor

Here's a very interesting and informative piece titled "Edifying Editing" by R. Preston McAfee (former co-editor of AER and editor of Economic Inquiry). It's not entirely applicable to finance because he's an econ guy. But there is a great deal of similarity between the fields. Here are a few things that stuck with me:

1. He cites a paper by Dan Hamermesh (1994), who discovered that, conditional on not receiving a report in 3 months, the expected waiting time was a year. So, if you want to endear yourself to editors and you're a reviewer, get stuff done quickly. I know that the longer I wait on a referee report, the less I feel like punching it out.
2. Around 25% of the to AER during his tenure were rejected due to poor execution. That is, the paper represented a good start on an article worthy topic, but provided too little for the audience. I recently was discussing a former student (and current coauthor) with a friend of mine who edits a pretty good journal. His comment was that my friend does good work, but "needs to finish his papers". Unfortunately, my friend often sends papers out to journals to get feedback from referees. That's what colleagues are for.
   
3. He feels like a a surprising number of papers provide no meaningful conclusion. Don;t merely reiterate your introduction in the conclusion. The introduction is to motivate a problem and summarize your results, and the conclusion is your opportunity to tie things together and make some parting shots.
4. He feels that submitting a paper where the editor has deep expertise usually produces a higher bar but less variance in the evaluation.
   

All in all a very worthwhile read. So read it here.

HT: Marginal Revolution

I'm Still Alive (but things might make me laugh myself to death)

Because of the end of the semester, some heath issues (since resolved), working on research, and being a bit burned, I haven't posted anything for several months.

But like one of the best business tacticians of our times says, "Just when I thought I was out... they pull me back in". So I guess this is my "welcome back" post.

I just received a referee's report that made me laugh at its awesomeness. First a bit of background: I sent a paper to a lower-tier journal back in June of 2008. There was no response for over a year, so I sent several emails (and voice mails) to the editor with no response. Finally, getting fed up, back in November, I sent him an email (and follow-up voicemail) asking the editor to withdraw the paper. We subsequently got a revise and resubmit another journal.

Then today I get this from the original journal (i.e. the where I'd withdrawn the paper long ago):

RE: XXXXX and the use of XXX

I have now received a report on your paper in which the referee makes a number of recommendations for improvement. Unfortunately I am unable to accept the paper for publication in its current form. However I would be happy to reconsider the paper if you were to revise it along the lines suggested by the referee. I look forward to your resubmission.

The reviewer's comments are given below.

Referee: 1
Comments to the Author

This paper examines the relationship between XXX and XXX. However, Pearson correlation coefficient that this paper uses is very ordinary. And this often does not measure the non-linear relationship for variables. In addition, the paper does not make the necessary statistical test and analysis to the studying results.

Note: emphasis is mine, and I only changed the relatively few words necessary to protect the guilty.

Yes, that is the sum total of the referee's report. I'd always heard that the main difference between "good" journals and "weak" ones wasn't so much the mean quality of reviewer but the variance. Now I have my own data point.

Next time I will make sure to use "extraordinary" Pearson correlation coefficients and "make the necessary statistical test and analysis to the studying result".

update: I told a friend and former classmate of mine about this, and he suggested that "Outstandingly Bad Referee Reports" would make for a fun session topic at a conference- particularly if we had a journal editor select the panel members. However, he suggested that the entertainment value would be much better if you could somehow ensure that (unbeknownst to each other) both the recipients of the reports and the originators were both on the panel).

But that would be wrong. Funny, but wrong.

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.

High Impact Research

Most of what I do is not "high impact." Ah well- I yam what I yam.

"Garbage Research" and The Equity Risk Premium

Instead of the CCAPM (Consumption CAPM), we now have the GCAPM (Garbage CAPM). Alexi Savov (graduate student at U of Chicago) finds that he can explain much more of the Equity Risk Premium using aggregate garbage production than he can using National Income and Product Account (NIPA) data. Here's the logic behind his research (from Friday's Wall Street Journal article titled "Using Garbage to Measure Consumption"):

In theory, one way to explain the premium would be to look at consumption, a broad measure of wealth. People should demand a premium from an investment that goes down when consumption goes down. That’s because the alternative — bonds — hold on to their value when consumption declines. Another way to put it: When you are making lots of garbage, you are rich. When you stop making garbage, you are poor. Unlike bonds, which continue to pay out whether you produce lots of garbage (and are rich) or not, stocks are likely to lose their value during bad times. Therefore, investors should want a large reward for putting their money in something whose value decreases at the same time as their overall wealth decreases.

Unfortunately, the data typically used to measure consumption (the US Government's figures for personal expenditure on nondurable goods and services category in the National Income and Product Account) don't have a lot of variation. So, they don't work very well as an explanatory variable. Savov finds that whe he uses EPA records on aggregate garbage production, they're exhibit a correlation with equity returns that are twice as high as the NIPA/Equity returs correlations. Here's the abstract of his paper (downloadable from the SSRN):

A new measure of consumption -- garbage -- is more volatile and more correlated with stocks than the standard measure, NIPA consumption expenditure. A garbage-based CCAPM matches the U.S. equity premium with relative risk aversion of 17 versus 81 and evades the joint equity premium-risk-free rate puzzle. These results carry through to European data. In a cross section of size, value, and industry portfolios, garbage growth is priced and drives out NIPA expenditure growth.

Read the whole thing here.

Getting Your Data Straight

I've made progress on the paper I'm working on. Unfortunately, this week has been a good illustration of a quote from McCloskey: I believe it went something like "90% of writing is getting your thoughts straight, and 90% of empirical work is getting your data straight."

Unfortunately, my data wasn't straight - I realized that I had used the wrong data code (a certain type of dividend distribution) from CRSP. So, my previous analysis was basically crap (that's a technical term for the unitiated) and had to be redone using the proper data set.

Luckily, it looks like my primary results after using the proper code, but with a few minor changes. For now, I'm still doing the preliminary descriptive stuff. Since I did the initial version of the paper in a hurry (hey - it was a conference deadline), I took a few shortcuts. This time, I'm going back to step 1 and going over every line of code, and (just as important), making sure I know how the sample changes at each point. As a result, I'm much more confident with my data this time around.

But doing the descriptive statistics is still (to me) about the most boring part of the paper. Still, it's gotta be done.

Momentum Effects and Firm Fundamentals

The more Long Chen's work I read, the more I like it. I recently mentioned one of his pieces on a new 3-factor model. Here's another, on the momentum effect, titled "Myopic Extrapolation, Price Momentum, and Price Reversal." In it, he links the well-known momentum effect to patterns in firm fundamentals. Here's the abstract:

The momentum profits are realized through price adjustments reflecting shocks to firm fundamentals after portfolio formation. In particular, there is a consistent cross - sectional trend, from short-term momentum to long-term reversal, that happens to earnings shocks, to revisions to expected future cash flows at all horizons, and to prices. The evidence suggests that investors myopically extrapolate current earnings shocks as if they were long lasting, which are then incorporated into prices and cash flow forecasts. Accordingly, the realized momentum profits can be completely explained by the cross - sectional variation of contemporaneous earnings shocks or revisions to future cash flows. Importantly, these cash flow variables dominate the lagged returns in explaining the realized momentum profits. As a result, the realized momentum profits represent cash flow news that has little to do with the ex ante expected returns. In fact, the ex ante expected momentum profits are significantly negative.

So, in essence, he finds that investors ignore mean-reverting patterns in firm earnings, and over-weight recent earnings shocks.

Very nice.

On an unrelated note, the Unknown Family will be traveling the next few days for a family reunion in West Virginia (the Unknown Wife's father grew up their, and that fork in the family tree has a get-together every year). So, unless I schedule a few pieces to post automatically, posting will likely be slim for the next few days.

Updating a Dataset Always Takes Longer Than Expected

I'm still working on updating my data. As usual, what I thought would be a "simple" three to four-day jobhas stretched out to almost two weeks of work. At least I'm not this person.

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.

Arrrrggghh! SAS is Evil!

Just another day (or two) of torturing data. Like I mentioned a couple of days ago, a week back I decided to update a data set to include the last year or so of data (the data sources I use were recently updated). Like most "simple" jobs, it's turned out to be much more of a hairball than I expected. Although the program I used was fairly simple to rewrite, I realized that I had to update not one, not tWo, but THREE datasets in order to bring everything up to the present.

Caution: SAS Geekspeak ahead

One of the data sets is pretty large (it was about 70 gigabytes, but with the updates and indexing I've done, it's almost 100 gig). So, adding the new data and checking it took quite a while (no matter how efficiently you code things, SAS simply takes a long time to read a 70 gigabyte file). I thought I had everything done except for the final step. Unfortunately, the program kept crashing due to "insufficient resources."

For the unitiated, when manipulating data (sorting, intermediate steps on SQL select statements, etc...) SAS sets up temporary ("scratch") files. They're supposed to be released when SAS terminates, but unfortunately, my system wasn't doing that. So, I had over 180 gigabytes of temporary files clogging up my hard drive. This means that there wasn't enough disk space on my 250 gigabyte drive for SAS to manipulate the large files I'm using.

Of course, I only realized this when my program crashed AFTER EIGHT HOURS OF RUNNING! TWICE!

I've now manually deleted all the temporary files, and I'm running the program overnight to see if this fixes the problem.

Ah well - if it was easy, anyone could do it.

update (next morning): Phew! It ran - it seems the unreleased temporary files were the issue. On to the next problem.

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