Big Ideas

Are the relatively placid financial markets of the American Century evolving to a high-volatility regime in a more evenly competitive world? In his December 2011 paper entitled “Adaptive Markets and the New World Order”, Andrew Lo examines the implications of the Adaptive Markets Hypothesis (AMH), wherein “markets are not always efficient, but they are usually highly competitive and adaptive, varying in their degree of efficiency as the economic environment and investor population change over time.” He believes that investors can prepare for occasional failures of market efficiency by viewing financial markets and institutions from the perspective of evolutionary biology. Applying this perspective to markets since 1926, he concludes that: More…

Is strategic asset class allocation or active management paramount for U.S. university endowment investment performance? In the October 2011 draft of their paper entitled “Do (Some) University Endowments Earn Alpha?”, Brad Barber and Guojun Wang explore the investment performance of U.S. university endowments with regard to overall alpha, performance persistence and sources of superior performance. They assess three groups of universities: Ivy League; other elite universities based on high average math SAT entrance scores; and, the balance of universities. They measure alpha as the residual return (from specific asset selection and tactical asset class allocation) after accounting for the combined returns of best-fit constant (strategic) asset class allocations to five indexes representing U.S. stocks (S&P 500 Index), non-U.S. stocks (MSCI non-U.S.), U.S. bonds (Barclays Capital Aggregate Bond Index), hedge funds (Hedge Fund Research Fund‐Weighted Composite Index) and private equity (Cambridge Associates U.S. Private Equity Index). Using annual voluntarily reported university endowment investment returns, benchmark index returns and math SAT score statistics for incoming freshmen during 1991 through 2010 (279 endowments report in all 20 years), they find that: More…

Should investors believe in the superiority of stocks for the long run and bonds for the short run? In his December 2011 paper entitled “Stocks, Bonds, Risk, and the Holding Period: An International Perspective”, Javier Estrada examines how the absolute and relative risks of stocks and bonds evolve as investment horizon grows (time diversification). Considering both annual and cumulative returns and various measures of variability/risk, he focuses on the question of whether stocks become less risky than bonds for long holding periods. Using annual total returns for stocks and bonds in 19 countries during 1900 through 2009, he finds that: More…

Do the poor returns and high volatility of the “buy-and-hold-is-dead” U.S. stock market since the beginning of 2000 represent a tailwind for market timers? In other words, is buy-and-hold effective as a benchmark for distinguishing between market timer luck and skill in recent years? To check, we measure the performances of various simple monthly market timing approaches (equal weighting with cash, 10-month simple moving average signals, momentum, and coin-flipping) during the 2000s. Using monthly closes for the dividend-adjusted S&P 500 Depository Receipts (SPY), the 3-month Treasury bill (T-bill) yield and the S&P 500 Index from December 1999 through October 2011 (earlier for S&P 500 Index signal calculations), we find that: More…

How can long-term investors maximize their edge of strategic patience? In their November 2011 paper entitled “Investing for the Long Run”, Andrew Ang and Knut Kjaer offer advice on successful long-term investing (such as by pension funds).  They define a long-term investor as one having no material short-term liabilities or liquidity demands. Using the California Public Employee’s Retirement System and other large institutions as examples, they conclude that: More…

Do somewhat abstract risk-based portfolio strategies translate to familiar stock/firm characteristic tilts? In their September 2011 paper entitled “Demystifying Equity Risk-Based Strategies: A Simple Alpha plus Beta Description”, Raul Leote de Carvalho, Xiao Lu and Pierre Moulin investigate how the following five risk-based equity allocation strategies relate to four common portfolio factors.

1. Equal Weight – long only, same portfolio weight for each stock.
2. Equal Risk Budget – long only, same portfolio weight times volatility for each stock.
3. Equal Risk Contribution – long only, similar to equal-risk budget, but tilted toward stocks with low market correlations (low beta).
4. Minimum Variance – long only or long-short, limited to stocks with the extreme volatilities and market correlations.
5. Maximum Diversification – long only or long-short, limited to stocks with extreme market correlations.

They map these five strategies to market capitalization (size), book-to-market ratio (value), market beta and residual (idiosyncratic) volatility factors based on quarterly rebalancing for empirical tests. Using weekly total returns for the stocks in the MSCI World Index of developed countries and the one-month U.S. Treasury bill yield during January 1997 through December 2010, they find that: More…

How long does it take stock markets to revert to their long-run means? In their April 2010 paper entitled “Mean Reversion in International Stock Markets: An Empirical Analysis of the 20 th Century”, Laura Spierdijk, Jacob Bikker and Pieter van den Hoek analyze mean reversion in 17 developed countries (Australia, Belgium, Canada, Denmark, France, Germany, Ireland, Italy, Japan, the Netherlands, Norway, South-Africa, Spain, Sweden, Switzerland, United Kingdom and the United States) over 109 years based on annual data. Using annual levels of 17 country stock market indexes and a composite worldwide index during 1900 through 2008, they find that: More…

The conventional binary animal metaphor for markets is bull (good returns, low volatility) and bear (poor returns, high volatility). Does rigorous analysis of empirical evidence support belief in (just) two market states? In their September 2011 paper entitled “The Number of Regimes Across Asset Returns: Identification and Economic Value”, Mathieu Gatumel and Florian Ielpo apply a regime-switching model and Monte Carlo simulations to determine the likely number of regimes implicit in the returns of 19 asset classes. Their general approach is to increase the number of regimes included in the model until adding a regime no longer materially improves the fit of the model to the actual return distribution. In other words, among statistically equivalent models, they always choose the one with the smallest number of regimes. They discuss the persistence of and performance under each regime discovered.  Using weekly return data for various stock  and bond indexes, currency exchange rates and commodity indexes over the period April 1998 through mid-December 2010 (650 weeks or 12.5 years), they find that: More…

Do sophisticated asset allocation strategies beat simple ones? In the December 2010 version of their paper entitled “Risk Parity Portfolio vs. Other Asset Allocation Heuristic Portfolios”, Denis Chaves, Jason Hsu Feifei Li and Omid Shakernia conduct a horse race among six asset allocation weighting strategies applied to nine asset classes:

1. 60/40: 60% S&P 500 Index/40% Barclays Capital U.S. Aggregate Bond Index.
2. U.S. Pension Model: 55% stocks (80% U.S. and 20% international); 35% bonds (60% U.S. Long Treasury, 20% investment-grade corporate and 20% global); and, 10% alternative investments (2.5% each commodities, REITs, emerging market equities and high-yield bonds).
3. Equal: simple equal weight for all nine asset classes.
4. Risk Parity: equal volatility contribution for all nine asset classes (asset classes contribute equally to expected portfolio fluctuations).
5. Mean-Variance: mean-variance optimization based on a five-year rolling return history.
6. Minimum-Variance: like mean-variance optimization but using only variances and ignoring returns, based on a five-year rolling return history.

The nine asset classes proxies are: Barclay’s Capital U.S Long Treasury Index; Barclay’s Capital U.S. Investment Grade Corporate Bond Index; J.P. Morgan Global Goernment Bond Index; Barclay’s Capital U.S. High Yield Corporate Bond Index; S&P 500 Index; MSCI EAFE Index; MSCI Emerging Market Index; Dow Jones UBS Commodity Index; and, FTSE NAREIT US Real Estate Index. Using annual and monthly data as available for these indexes over the period January 1980 through June 2010, they find that: More…

Analyses such as those described in “Demographic Headwind for U.S. Stock Market?” and “Classic Research: Demography and the Stock Market” assess the impact of demographic changes on the stock market by focusing on market valuation as measured by price-earnings ratio (P/E). What story would a more direct analysis of demographics and stock market returns tell? To investigate, we: (1) collect historical U.S. age demographics; (2) construct an annual series of the ratio of middle-age cohort (ages 40–49) population to the old-age cohort (ages 60–69) population (designated M/O, similar to the metric described in “Demographic Headwind for U.S. Stock Market?”) to capture the joint behavior of presumed equity investors and equity disinvestors; and, (3) relate M/O to annual U.S. stock market returns. Using estimated annual (July1) age demographics for 1900-2009, 2010 census age demographics, annual S&P 500 Index returns (June 30 to June 30) for 1950 through 2011, annual Dow Jones Industrial Average (DJIA) returns (June 30 – June 30) for 1929 through 2011 and annual Consumer Price Index (CPI) data (June) for 1913 through 2011, we find that: More…