New Video On Security Indexes

Due to severe inclement weather (ice storm), less than half of my investments class showed up on Wednesday. The topic (market indexes) seems to give some of them trouble. So, rather than either go over it again in the next class (and make those who attend have to sit through it twice) or just move on (and leave those who clearly had a reason for missing class hanging), I put together a video on the topic. Since it's done, figured I might as well share. It's not professionally done by any stretch, but it's not bad (it runs about 40 inutes, but has a table of contents that whould allow you to jump back and forth). Enjoy.

NOTE:if the video doesn't come up, try this link.

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

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.

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

Diversification Across Risk Premiums

Things have been crazy lately - we have two speakers this week at Unknown University's College of Business, and I'm involved in both visits. In addition, I'm getting ready for my CFA prep class and trying to get a paper out for a conference. So, blogging has been light this week (and will probably continue to be spotty for the rest of the week).

But in the meantime, here's an interesting paper to chew on. We were recently talking about different risk premia (size, market/book, momentum, etc...) in class. This paper, "Portfolio of Risk Premia: A New Approach to Diversification" by Remy Brian, Frank Nielsen, and Dan Stefek paper that takes the idea of risk premia combines it with a equally-weighted portfolio weighting scheme across assets with exposures to the various premiums.

Traditional approaches of structuring policy portfolios for strategic asset allocation have not provided the full potential of diversification. Portfolios based upon a 60/40 allocation between equities and bonds remain volatile and dominated by equity risk. In this paper, we introduce a different approach to portfolio diversification. This approach looks at structuring portfolios using available risk premia within the traditional asset classes or from systematic trading strategies rather than focusing on classic betas such as equities and bonds. We start by reviewing the various ways of dissecting asset classes into their underlying systematic drivers or risk premia and analyze the historical risk and return patterns for a number of risk premia across asset classes. In a second stage, we illustrate empirically that correlations between risk premia have been low, offering significant diversification potential. We then confirm the benefits of diversification with a simple asset allocation case study by comparing a typical 60/40 equity/fixed income allocation with an equal weighted allocation across eleven style and strategy risk premia. From 1995 to 2008, this simple combination had returns similar to the traditional allocation but with 65% less volatility.

You can read the whole thing on SSRN here

A Quantitative Approach to Tactical Asset Allocation

I'm not a big fan of market timing and/or technical trading rules. From what I've seen, the empirical evidence casts a lot of doubt on their effectiveness.

But I just read a very interesting paper titled "A Quantitative Approach to Tactical Asset Allocation", by Mebane Faber. Here's the abstract:

The purpose of this paper is to present a simple quantitative method that improves the risk-adjusted returns across various asset classes. A simple moving average timing model is tested since 1900 on the United States equity market before testing since 1973 on other diverse and publicly traded asset class indices, including the Morgan Stanley Capital International EAFE Index (MSCI EAFE), Goldman Sachs Commodity Index (GSCI), National Association of Real Estate Investment Trusts Index (NAREIT), and United States government 10-year Treasury bonds. The approach is then examined in a tactical asset allocation framework where the empirical results are equity-like returns with bond-like volatility and drawdown, together with over thirty-five consecutive years of positive performance.

The paper is definitely worth discussing in class. In most investment classes, there's at least some mention of the relations between arithmetic average returns, geometric average returns, holding period returns, and volatility. The reported returns to the strategy in the paper result in arithmetic average returns that are about the same (if not slightly less) than a buy/hold strategy. However, because of the lower volatility of this timing strategy,, it yields a significantly higher geometric average (and holding period) returns

It's also a good paper for a discussion on the return patterns to a market timing strategy vs. to a buy and hold one.

So, regardless of your views on market timing, it's worth a read: a short paper, interesting results, and written from a practitioner's viewpoint, so it's an easy read - even for an undergraduate.

You can download it from SSRN here.

Ah well, enough bloggery - back to work.

Wikinvest - Wikis Meet Investing

Here's an interesting cross-breeding of Internet technology and financial information: A wiki-style resource for investors called Wikinvest. In case the term is unfamiliar to you (other than Wikipedia, that is) a wiki is an Internet community where participants can load pages up on various topics and edit pages put up by others. Ideally, it serves as a self-editing source of information, where experts correct mistakes posted by those less informed. Wikinvest has the following areas:

• Information on companies (0ver 2200 posted to date)
• A Concepts section, covering topics ranging from industry-specific areas like Technology and the Internet and Energy to "Green Issues)
• ACommodities section (i.e. metals, energy, grains, etc.)
• An area coveringFunds and Indices (a handful of ETFs and 37 different indices, from the S&P to Baltic Dry Goods)
• And, of course, Global Markets, ranging from interest rates to investing in Brazil.

It's not a source of data, but rather a collection of entries by various parties. An initial perusal of the site found some pretty good stuff, some absolute dreck, and everything in between (kind of like on Wikipedia).

HT: Christine Hurt at The Conglomerate

Beware The Bid-Ask Spread in ETFs

When the average Joe (or Jane) looks at transactions costs from trading, they typically focus on the commission charged by the broker. But in the case of some thinly-traded ETFs (exchange-traded funds), the bid-ask spread can add significantly to that cost. Here's a good piece on the topic from Morningstar:

No one has a very precise definition of liquidity, but it roughly boils down to how easy it is to buy or sell a particular security and how much agreement there is in the marketplace upon the security's fair value. The most liquid funds or stocks have miniscule bid-ask spreads, where the prices differ by only a penny. On the other side, a brand new ETF tracking a selection of more thinly traded mortgage-backed securities has a bid-ask spread near 0.80% as I write this. That means that buying and selling the fund at market prices, even without any commissions charges or price changes, would result in a 0.80% loss. Not exactly a terrifying loss, especially compared with what we all saw in 2008, but still an unwelcome drag on portfolio returns if it can be avoided.

Read the whole thing here

It's Easy Buying A Stake in a Public Company

Here's one of the better explanations I've recently read on the idea of fundamental analysis or "value" investing (from the Ideas Report:

Buying a stake in a publicly traded company is deceptively easy. Log into your brokerage account, type in the ticker of the company whose stock you wish to buy, and—voilà!—you own a stake in the enterprise. Many investors don’t even refer to companies by their name; they simply invoke the ticker symbol. The ease with which stocks are bought and sold obscures the underlying nature of a stock market transaction and invites bad decision-making. The trick is to avoid thinking of a stock as a readily disposable piece of paper and instead consider that you are buying a percentage of a business whenever you purchase a share of stock.

Read the whole thing here

This Is Why I Don't Check My Portfolio Too Often

As usual, Scott Adams has a good take on things

Testosterone and Traders

Are successful traders born, or made? Here's some evidence supporting the latter, from the Washington Post:

A new study has found that men who were programmed in the womb to be the most responsive to testosterone tend to be the most successful financial traders, providing powerful support for the influence of the hormone over their decision-making.

Read the whole thing here

Levered ETF Math

Many people use levered ETFs to either leverage (i.e. double) or hedge their exposure to an index Unfortunately, their results can often differ from what they expect. This Wall Street Journal gives a good explanation why in this article. It's a good illustration how volatility makes geometric and arithmetic averages differ.

Stock Picking Ability and Value Investing

Andy Kern and Welsey Gray are two finance doctoral student who also run the blog Empirical Finance Research. They've just put a study up on SSRN titled "Fundamental Value Investors: Characteristics and Performance." In it, they examine the investment recommendations of a fairly large and sophisticated community of fundamental value investors (the folks at Valueinvestorclub.com):

The data in this study are collected from a private internet community called
Valueinvestorsclub.com (VIC), proclaimed by the founders to be an “exclusive online
investment club where top investors share their best ideas.”1 The site has been heralded in many business publications as a top-notch resource for anyone who can attain membership (Financial Times, Barron’s, Business Week, and Forbes among others). The site was founded by Joel Greenblatt and John Petry, both successful value investors and managers of the large hedge fund Gotham Capital. It was created with $400,000 of start-up capital to be the site with “the best-quality ideas on the Web” (Barker (2001)).

The investment ideas submitted on the club’s site are broad, but are best described as fundamental value plays.

What do they find? Here are their conclusions:

We find that value investors are not focused on high book-to-value stocks, but instead focus on intrinsic value (discounted value of after-tax free cash flows generated by a business) and signaling factors in the market (e.g. open market repurchases, insider buying, activist activity). These investors also tend to favor smaller stocks with a value bias for long positions and small growth stocks for short positions. We also determine that value investors are fairly one dimensional and utilize only a few tools when making their investment decisions. This suggests that professional investors may suffer from limited attention and resource deficiency.

Our analysis of value investors’ investments suggests that value investors do have
stock picking skills. Utilizing the BHAR and the calendar-time portfolio regression
approaches, we find evidence that value investors reliably outperform the market.

A very nice paper, and well worth a read. While you're at it, check out the Valueinvestorsclub site - as a guest you can read the recommendations with a three-month lag. Some of them are pretty interesting.

Interview With Baupost Group President Seth Klarman

Seth Klarman, is the president of Boston hedge fund the Baupost Group. He's known as one of the savviest value investors around, having earned a 26% average annual return over the last 26 years. In fact, he was asked to write the foreword to the latest edition of Graham and Dodd's classic "Value Investing."

Here's an interview he gave to the Harvard Business School Bulletin back in December. In it, he talks about why he's a value investor, target returns (he doesn't believe in them), the credit crisis, and much more.

Read it here