You Can't Measure Alpha Independent of Risk

When I teach investments, there's always a section on market efficiency. A key point I try to make is that any test of market efficiency suffers from the "joint hypothesis" problem - that the test is not tests market efficiency, but also assumes that you have the correct model for measuring the benchmark risk-adjusted return.

In other words, you can't say that you have "alpha" (an abnormal return) without correcting for risk.

Falkenblog makes exactly this point:

In my book Finding Alpha I describe these strategies, as they are built on the fact that alpha is a residual return, a risk-adjusted return, and as 'risk' is not definable, this gives people a lot of degrees of freedom. Further, it has long been the case that successful people are good at doing one thing while saying they are doing another.

Even better, he's got a pretty good video on the topic (it also touches on other topics). Enjoy.

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

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

A Pretty Good Week (and Month) In the Markets

I try not to get too excited about short-term market movements. At the same time, I have to keep up since I'm the faculty advisor for Unknown University's St udent-managed fund. Even so, it's been a pretty good week (and month and year) so far - almost every equity index I can think of is in the green for the last month (and even year to date). As an aside, our fund is up 11.4% YTD (but I'm sure that'll change).




























click for larger image (courtesy of investmentpostcards.com)

"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.

Asset Class Correlations Increase In Bad Times

It's a pretty well-known fact that correlations between asset classes increase in really bad markets. To get a sense of how much this effect matters in terms of portfolio diversification, read this Wall Street Journal piece (published Friday, 7/10) titled "Failure of a Fail-Safe Strategy Sends Investors Scrambling. Here's a snippet:

Correlation is a statistical measure of the degree to which investment returns move together. Between 1991 and 1994, the correlation between the S&P 500 index and high-yield bonds was low, at 0.2 or 0.3, according to Pimco statistics. (A correlation of 1 means returns move in perfect sync.) International stocks had a correlation with the S&P 500 of 0.3 or 0.4, and real-estate investment trusts had a correlation of 0.3, according to Pimco data. Commodities showed little correlation to U.S. stocks. By early 2008, investment categories of just about every stripe were moving significantly more in sync with the S&P 500. The correlation on international stocks and high-yield bonds rose to 0.7 or 0.8, and real-estate investment trusts to 0.6 or 0.7, according to Pimco's data for the previous three years

Read the whole thing here (note: subscription required).

The problem with portfolio diversification is that it is typically implemented using historical correlations (actually, on covariances, but the two are essentially the same). To provide optimal diversification, portfolio allocations should be made based on "forward looking" correlations. In practice, some managers adjust historical correlation estimates to reflect their views of future relationships. But that becomes far more complicated than simply using historical estimates and assuming that they'll continue unto the future.

Note: if you don't have an online subscription to the Journal, try searching for the article using Google News - if you click on the link there, it works around the WSJ subscription filter (however, not all WSJ articles can be accessed this way).

The Limits of Models

Here's an excellent piece on the Psi-Fi Blog, titled "Quibbles With Quants." Here's a choice part:

What the models failed to capture was that humans don’t behave in simple, predictable and uncorrelated ways. It’s impossible to overstate the importance of the way these models cope with correlation of peoples’ psychology. To sum it up: they don’t. Let me know if that’s too complex an analysis for the mathematical masters of the universe.

Anyone who’s ever been to a nightclub, a football game or even a very loud party will know that there are situations where we don’t act as individuals, buzzing about doing our own thing. These are occasions when we all suddenly stop being individuals and start doing the same thing – usually involving large quantities of drugs and some very bad singing. Although these sorts of events are specifically designed to trigger this behaviour – which is probably a deep evolutionary adaptation to sponsor group behaviour, useful when it comes to running down tasty antelope and dealing with giant, carnivorous sabre toothed beavers – it can also happen in other situations. Most stockmarket booms and busts are generated by similar group effects.

In general, people behave in an uncorrelated fashion right up until the point they don’t.

Read the whole thing here.

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.

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

A Simple (and Impressive) New Three Factor Return Model

First, a little background on "factor models": The CAPM model for estimating expected returns is the oldest and most widely know of all finance models. In it, exposure to systematic risk (i.e. beta) is only factor that gets "priced" (i.e. that's related to expected returns).

In 1993, Fama and French showed that a three factor model (the CAPM market factor plus a size factor and a value/growth factor), did a much better job of explaining cross-sectional returns when compard to the "plain vanilla" CAPM.

Since the FF model became popular, a number of studies have come out that identify other factors that seem to be associated with subsequent returns, such as momentum (Jegadeesh and Titman, 1993), distress (Campbell, Hilscher, and Szilagyi, 2008), stock issues (Fama and French, 2008) and asset growth (Cooper, Gulen, and Schill, 2008).

Now, on to the meat of this post - another factor model. This one is based on q-theory (i.e. on the marginal productivity of a firm's investments). Long Chen and Lu Zhang (from Washington University and Michigan, respectively) recently published a paper "A Better Three-Factor Model That Explains More Anomalies", in the Journal of Finance. They propose a three-factor model", with the three factors being the aggregate returns on the market, the firm's asset-scaled investments, and the firm's return on assets). Their model significantly outperforms the Fama-French (FF) model in explaining stock returns, does a better job (relative to FF) at explaining the size, momentum, and financial distress effects (i.e. you don't need to add additional factors for these effects), and does about as well as FF in capturing the Value (i.e. Book/Market) effect. Here's a taste of their results:

• The average return to the investment factor (i.e. the the difference between the low and high investment firms) is 0.43% per month over the 1972-2006 sample period. When measured only among small firms, the return difference between low and high investment firms is about 26% annually)
  
• The average return to the ROA factor (the difference between returns to the firms with the lowest and highest ROA) is 0.96% per month over the sample period (with a high/low spread of about 26% for the smallest firms).
• The differences in high vs. low portfolios persist (albeit in smaller magnitudes) after controlling for Fama-French and momentum factors.

It's definitely worth a read (in fact, it'll be on the reading list for my student-managed fund class). You can find an ungated version of the paper on SSRN here.

HT: CXO Advisory Group

A Good Paper on "Return Factors"

Robert Haugen is one of (if not THE) best-known figure in the behavioral finance (i.e. "markets are not efficient") camp. He wrote one of the earliest books on the topic in 1995 (The New Finance) and runs a quantitative finance shop based on much of his research. In a recent paper with Nardin Baker of UC-Irvine, he examines the explanatory and predictive ability of a wide array of observable factors. Here's the abstract

This article provides conclusive evidence that the U.S. stock market is highly inefficient. Our results, spanning a 45 year period, indicate dramatic, consistent, and negative payoffs to measures of risk, positive payoffs to measures of current profitability, positive payoffs to measures of cheapness, positive payoffs to momentum in stock return, and negative payoffs to recent stock performance. Our comprehensive expected return factor model successfully predicts future return, out of sample, in each of the forty-five years covered by our study save one. Stunningly, the ten percent of stocks with highest expected return, in aggregate, are low risk and highly profitable, with positive trends in profitability. They are cheap relative to current earnings, cash flow, sales, and dividends. They have relatively large market capitalization and positive price momentum over the previous year. The ten percent with lowest expected return (decile 1) have exactly the opposite profile, and we find a smooth transition in the profiles as we go from 1 through 10. We split the whole 45-year time period into five sub-periods, and find that the relative profiles hold over all periods. Undeniably, the highest expected return stocks are, collectively, highly attractive; the lowest expected return stocks are very scary - results fatal to the efficient market hypothesis. While this evidence is consistent with risk loving in the cross-section, we also present strong evidence consistent with risk aversion in the market aggregate's longitudinal behavior. These behaviors cannot simultaneously exist in an efficient market.

Here are some of the factors that they find statistically significant:

• Price Multiples such as price to cash flow, sales, book value, and earnings (negative relationship with subsequent returns
  
• Profitabiliy measures such as ROE, ROA, and Profit Margins (positive relationship)
• Volatility in returns, whether "raw" or "residual" (negative relationship)
• Momentum (positive relationship)
• Recent returns (positive rel;ationship with last year's return, negative with last month's return, and last month's "residual" return)

Read the whole thing here

It's worth reading. Haugen is clearly not an ubiased observer (he does run a shop based on the idea that markets are inefficient), and there's definitely some serious data mining going on here. Having said that, it's definitely worth reading. It gives a very good summary of many of the factors that prior research has found to be significantly related to subsequent returns. I'll be making the next group of student in Unknown University's student-managed fund read it.

HT: Empirical Finance Research

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).

Lake Wobegon Stock Recommendations

It's pretty well known that "Sell" recommendations on Wall Street are about as rare as honest politicians in Washington (they're out there, but don't expect to find a lot of them).

It seems like Merrill Lynch is trying to change its ways. They have a new standard for their analysts - Beginning in June, they will require that its analysts assign “underperform” ratings to 20% of all stocks they cover (currently, only 12% of covered stocks fall into that category). Their hope is that the new standard will make their recommendations more credible, since a "buy" will no longer be the default evaluation.

A similar movement is going on in academia. A number of schools (the Unknown Alma Mater among them) have put limitations on grade distributions (i.e. there's a maximum percentage of A's and B's that an instructor can assign). It's not as much of a problem in the Finance and Accounting areas, since we're generally tougher graders than those in the Liberal Arts areas. I'm not aware of how things are done in other areas, but Business Schools have been moving in this direction for a couple of years now. It may be one of the few cases where academia has actually moved faster than the business world. I guess even a stopped clock shows the right time a couple times a day.

Read the whole thing here.

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.

The R.I.S.E. Forum

I'm back safe and sound (even if a bit sleep deprived) from the R.I.S.E. Forum in Dayton. It was a great experience for my students (and pretty good for me too).

The first night of the conference, we'd just checked in my student s and I were talking in the hallway. One of the conference big shots (he's a regular talking head on MSNBC on stock market matters walked by, and we struck up a conversation. He said he was meeting at a local tavern with some students that had been out to his company , and invited my students along. So, they ended up having a beer or two with him, and talked stocks for about two hours. The next day, they got to hear Chris Gardener (the book The Pursuit of Happiness was based on his story), and even got their a picture taken with him.

The next two days, we all saw a number of excellent sessions, made a lot of good friends and contacts, and even got some ideas for our Student Managed Fund.

I'd recommend the conference for anyone who's considering it.

Off To Dayton

Blogging will be light for the next couple of days - I'm at the R.I.S.E. Forum in Dayton Ohio. I may post a thing or two, but only if the speakers get too boring (and they've been excellent so far).