Big Ideas

Does the carry trade concept provide a useful framework for valuation of securities within and across all asset classes? In their July 2013 paper entitled “Carry”, Ralph Koijen, Tobias Moskowitz, Lasse Pedersen and Evert Vrugt investigate expected return across asset classes via decomposition into “carry” (expected return assuming price does not change) and expected price appreciation. They measure carry for: global equities; global 10-year bonds; global bond yield spread (10-year minus 2-year); currencies; commodities; U.S. Treasuries; credit; equity index call options; and equity index put options. Their measurements of carry vary by asset class (based on: futures prices for equity indexes, currencies and commodities, modeled futures prices for global bonds, U.S. Treasuries and credit; and, option prices for options). They further decompose carry returns into passive and dynamic components. The passive component is the return to a hedge (carry trade) portfolio designed to capture differences in average carry returns across securities, and the dynamic component indicates how well carry predicts future price appreciation. Finally, they determine the conditions under which carry strategies perform poorly across all asset classes. Using monthly price/yield data for multiple assets within each class as available during January 1972 through September 2012, they find that: Keep Reading

How can investors capture returns from widely accepted risk factors associated with asset classes and subclasses? In the June 2013 version of his book chapter entitled “Factor Investing”, Andrew Ang provides advice on capturing risk premiums associated with factors such as value, momentum, illiquidity, credit risk and volatility risk. Based on the body of research, he concludes that: Keep Reading

Is downside risk the critical driver of investor asset valuation? In the January 2013 version of their paper entitled “Conditional Risk Premia in Currency Markets and Other Asset Classes”, Martin Lettau, Matteo Maggiori and Michael Weber explore the ability of a simple downside risk capital asset pricing model (DR-CAPM) to explain and predict asset returns. Their approach captures the idea that downside risk aversion makes investors view assets with high beta during bad market conditions as particularly risky. For all asset classes (but focusing on currencies), they define bad market conditions as months when the excess return on the broad value-weighted U.S. stock market is less than 1.0 standard deviation below its sample period average. To test DR-CAPM on currencies, they rank a sample of 53 currencies by interest rates into six portfolios, excluding for some analyses those currencies in highest interest rate portfolio with annual inflation at least 10% higher than contemporaneous U.S. inflation. They calculate the monthly return for each currency as the sum of its excess interest rate relative to the dollar and its change in value relative to the dollar. They then calculate overall and downside betas relative to the U.S. stock market based on the full sample. They extend tests of DR-CAPM to six portfolios of U.S. stocks sorted by size and book-to-market ratio, five portfolios of commodities sorted by futures premium and six portfolios of government bonds sorted by probability of default, and to multi-asset class combinations. They also compare DR-CAPM to optimal models based on principal component analysis within and across asset classes. Using monthly prices and characteristics for currencies and U.S. stocks during January 1974 through March 2010, for commodities during January 1974 through December 2008 and for government bonds during January 1995 through March 2010, they find that: Keep Reading

Does use of alphas from linear factor models to identify anomalies in U.S. stock returns mislead investors? In the February 2013 draft of their paper entitled “Using Maximum Drawdowns to Capture Tail Risk”, Wesley Gray and Jack Vogel investigate maximum drawdown (largest peak-to-trough loss over a time series of compounded returns) as a simple measure of tail risk missed by linear factor models. Specifically, they quantify maximum drawdowns for 11 widely cited U.S. stock return anomalies identified via one-factor (market), three-factor (plus size and book-to-market ratio) and four-factor (plus momentum) linear models. These anomalies are: financial distress; O-score (probability of bankruptcy); net stock issuance; composite stock issuance; total accruals; net operating assets; momentum; gross profitability; asset growth; return on assets; and, investment-to-assets ratio. They calculate alphas for each anomaly by using the specified linear model risk factors to adjust gross monthly returns from a portfolio that is long (short) the value-weighted or equal-weighted tenth of stocks that are “good” (“bad”) according to that anomaly, reforming the portfolio annually or monthly depending on anomaly input frequency. Using monthly returns and firm fundamentals for a broad sample of U.S. stocks, and contemporaneous stock return model factor returns, during July 1963 through December 2012, they find that: Keep Reading

In his 2013 book The Little Book of Market Myths: How to Profit by Avoiding the Investing Mistakes Everyone Else Makes, author Ken Fisher, chairman and CEO of Fisher Investments, “covers some of the most widely believed market and economic myths–ones that routinely cause folks to see the world wrongly, leading to investment errors.” His hope is that “the book helps you improve your investing results by helping you see the world a bit clearer. And I hope the examples included here inspire you to do some sleuthing on your own so that you can uncover still more market mythology.” Some notable points from the book are: Keep Reading

How should investors measure the value of tactical deviations from a strategic asset class allocation? In their December 2012 draft paper entitled “A Framework for Examining Asset Allocation Alpha”, Jason Hsu and Omid Shakernia decompose sources of alpha for a diversified portfolio. Their decomposition assumes prior determination of the strategic asset allocation (policy portfolio), consisting of indexes that proxy for broad asset classes. They define tactical asset allocation (tactical portfolio), also consisting of indexes, as deviation from the strategic allocation. They define manager selection (implemented portfolio) as the set of tradable assets used to implement the tactical allocation. Total alpha is the return of the implemented portfolio in excess of that for the policy portfolio, a combination of excess returns from tactical allocation and manager selection. The excess return of the tactical portfolio over the policy portfolio is the asset allocation alpha, the focus of the paper. Based on prior research, they conclude that: Keep Reading

How special is the Fama-French three-factor model (market, size, book-to-market ratio) compared to other possible three-factor models? In their November 2012 paper entitled “Firm Characteristics and Empirical Factor Models: a Data-Mining Experiment”, Leonid Kogan and Mary Tian systematically compare explanatory breadth for all 351 three-factor and 2,925 four-factor (linear) models for predicting stock returns that can be formed from 27 firm characteristics other than size and book-to-market ratio. They measure explanatory breadth of a model by how well it captures the average future return differences across value-weighted deciles from annual sorts on the characteristics not used in the model. Using monthly returns and annual/quarterly firm characteristics for a broad sample of non-financial U.S. stocks during 1971 through 2011, they find that: Keep Reading

James Weatherall, physicist, mathematician and philosopher, introduces his 2012 book, The Physics of Wall Street: A Brief History of Predicting the Unpredictable, by stating: “This book tells the story of physicists in finance. …It is about how the quants came to be, and about how to understand the ‘complex mathematical models’ that have become central to modern finance.” Tracing the historical stream of key contributions by physicists and mathematicians to finance, he concludes that: Keep Reading

Can investors systematically earn a premium by holding relatively illiquid assets? In their December 2012 paper entitled “The Illiquidity Premium: International Evidence”, Yakov Amihud, Allaudeen Hameed, Wenjin Kang and Huiping Zhang examine the illiquidity premium in 26 developed and 19 emerging equity markets. They measure illiquidity as the average ratio of absolute daily stock return to trading volume (price impact per monetary volume traded). They define the illiquidity premium as the average gross return in excess of the risk-free rate for volatility-controlled portfolios that are long (short) high-illiquidity (low-illiquidity) stocks. Specifically, every three months, they: (1) sort stocks into three equal groups (terciles) based on return volatility (standard deviation of daily returns) over a lagged, rolling three-month window; (2) within each volatility tercile, sort stocks into fifths (quintiles) based on illiquidity over the same window; (3) skip one month to avoid any short-term reversal; and, (4) calculate the illiquidity premium as the average of returns of three portfolios that are long (short) the high-illiquidity (low-illiquidity) quintile within each volatility tercile. They groom the sample by excluding stocks that trade infrequently or exhibit extreme movements. They consider equal, value and monetary volume weightings for portfolios. Using daily price, trading volume and shares outstanding data for common stocks in 45 countries, along with estimates of market, size and book-to-market risk factors, during 1990 through 2011 (22 years), they find that: Keep Reading

When should an investor favor picking individual stocks over holding a stock index fund? In their November 2012 paper entitled “On Diversification”, Ben Jacobsen and Frans de Roon derive from Modern Portfolio Theory simple rules to compare concentrated investment in a portfolio of one or a few stocks to a broad, diversified (value-weighted) benchmark portfolio. The essential rule is that a concentrated portfolio is preferable to the benchmark portfolio if the product of its expected Sharpe ratio and the expected correlation of its returns with the benchmark’s returns exceeds the expected Sharpe ratio of the benchmark. They apply derivative thumb rules to real stocks to determine conditions under which stock picking is preferable to buying and holding a diversified benchmark portfolio. Using theoretical derivations and monthly returns and fundamentals for the 500 largest non-financial companies as of the end of the sample period with a history of at least five years during 1926 through 2011, they find that: Keep Reading