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Strategic Allocation

Is there a best way to select and weight asset classes for long-term diversification benefits? These blog entries address this strategic allocation question.

A Few Notes on Trend Following

Michael Covel prefaces the 2017 Fifth Edition of his book, Trend Following: How to Make a Fortune in Bull, Bear, and Black Swan Markets, by stating that: “The 233,092 words in this book are the result of my near 20-year hazardous journey for the truth about this trading called trend following. …Trend following…aims to capture the majority of a connected market trend up or down for outsize profit. It is designed for potential gain in all major asset classes–stocks, bonds, metals, currencies, and hundreds of other commodities. …if you want outside-the-the-box different, the truth of how out-sized returns are made without any fundamental predictions or forecasts, this is it. And if you want the honest data-driven proof, I expect my digging will give everyone the necessary confidence to break their comfort addiction to the box they already know and go take a swing at making a fortune…” Based on his experience as a trader/portfolio manager and the body of trend following research, he concludes that: Keep Reading

U.S. Stock Market Crisis Hedge Strategies

What is the most effective way to hedge against equity market crashes? In their June 2017 paper entitled “The Best Strategies for the Worst Crises”, Michael Cook, Edward Hoyle, Matthew Sargaison, Dan Taylor and Otto Van Hemert examine active and passive strategies with potential to generate positive returns during the worst crises. They test these strategies across the seven S&P 500 Index drawdowns of more than 15% during 1985 through 2016. They focus on two active strategies:

  1. Time-series (intrinsic or absolute) momentum long-short portfolio comprised of 50 liquid futures and forwards series spanning currencies, equity indexes, bonds, agricultural products, energy and metals. They consider return lookback intervals of 1, 3 and 12 months. They apply risk adjustments, risk allocations by class and finally a scale factor targeting 10% annualized portfolio volatility. They consider three extensions of the strategy that preclude or restrict positive exposure to equity market beta.
  2. Quality factor long-short portfolios comprised of intermediate and large capitalization U.S. stocks. These portfolios ares long (short) the highest-ranked (lowest-ranked) stocks, as selected based on one of 18 metrics representing profitability, growth in profitability, safety and payout. Rankings are risk-adjusted and portfolios are equity market beta-neutral. They again apply a scale factor targeting 10% annualized portfolio volatility. They also consider several composite factor portfolios by averaging individual factor rankings and weighting for dollar neutrality, beta neutrality, sector neutrality and/or volatility balancing.

Using daily data for all indicated assets during 1985 through 2016, they find that: Keep Reading

A Better P/E10?

Is there a way to enhance the ability of the cyclically-adjusted price-to-earnings ratio (P/E10 or CAPE) to predict U.S. stock market returns by incorporating real interest rates? In their June 2017 paper entitled “Improving U.S. Stock Return Forecasts: A ‘Fair-Value’ Cape Approach”, Joseph Davis, Roger Aliaga-Diaz, Harshdeep Ahluwalia and Ravi Tolani introduce “fair-value” CAPE that accounts for a dynamic, positive relationship between real 10-year U.S. Treasury note (T-note) yield (cost of capital) and real earnings yield (return on equity). They hypothesize that a lower real T-note yield should imply a lower earnings yield and thus a higher fair-value CAPE. Their use of fair-value CAPE to forecast stock market return involves:

  • Each month, execute a multiple vector autoregression of the logarithms of the following five variables separately for each of the last 12 months: (1) inverse of CAPE; (2) expected real T-note yield based on a 10-year U.S. inflation forecast; (3) U.S. inflation; (4) realized S&P 500 Index price volatility over the last 12 months; and, (5) realized volatility of changes in real T-note yield over the last 12 months. Their 10-year inflation forecast is the average of 120 monthly forecasts generated via autoregression of the U.S. consumer price index over a 30-year rolling window.
  • Each month, forecast 10-year stock market return (see the chart below) by summing: (1) percentage change in CAPE from the preceding vector autoregression; (2) constant earnings growth equal to its long-term average; and, (3) dividend yield calculated as earnings yield times the historical payout ratio.

They then compare out-of-sample forecasts of 10-year U.S. stock market returns for 1960 through 2016 and 1985 through 2016 generated by fair-value CAPE and two conventional CAPEs: Shiller CAPE based on Generally Accepted Accounting Principles (GAAP); and, Siegel CAPE based on National Income and Product Accounts (NIPA) earnings. Using Shiller’s data and NIPA earnings during 1950 through 2016, they find that: Keep Reading

Global Multi-class Market Performance

What is the performance of the global multi-class market portfolio? In their June 2017 paper entitled “Historical Returns of the Market Portfolio”, Ronald Doeswijk, Trevin Lam and Laurens Swinkels estimate returns to a capitalization-weighted multi-class global market portfolio (GMP) during 1960 through 2015 in U.S. dollars. GMP encompasses all readily investable assets, allocated to four broad classes: equities, government bonds, corporate (nongovernment) bonds and real estate. They estimate nominal, real (relative to U.S. consumer inflation) and excess (relative to the risk-free rate) return and risk characteristics of GMP and its component asset classes over the full sample period, and during expansion/contraction and inflationary/disinflationary subperiods. They also compare GMP performance statistics to those for the following three heuristic (simple mean reversion) portfolios rebalanced annually to fixed weights:

  1. Equal-weighted (EW).
  2. Rank-weighted (RW), which assigns weights 40%, 30%, 20% and 10%, respectively, to equities, government bonds, corporate bonds and real estate.
  3. 50/50, which holds 50% equities and 50% government bonds.

Using annual data for the asset classes constructed according to Appendix A of the paper, annual yields for 3-month U.S. Treasury bills (T-bills) as the risk-free rate and annual U.S. consumer inflation rates, from the end of 1959 through 2015, they find that: Keep Reading

Zeta Risk and Future Stock Returns

Can investors predict the return of a stock from its relationship with the dispersion of returns across all stocks? In their May 2017 paper entitled “Building Efficient Portfolios Sensitive to Market Volatility”, Wei Liu, James Kolari and Jianhua Huang examine a 2-factor model which predicts the return on a stock based on its sensitivity to (1) the value-weighted stock market return (beta risk) and (2) the standard deviation of value-weighted returns for all stocks (zeta risk). They first each month estimate zeta for each stock via regressions of daily data over the past year. They then rank stocks by zeta into quantile portfolios and calculate next-month equal-weighted returns across these portfolios and various long-short combinations of these portfolios (hedge portfolios) to measure dependence of future returns on zeta. Finally, they generate performance data for aggregate zeta risk portfolios by adding value-weighted market index returns to returns for each of the long-short zeta-sorted portfolios. Using daily and monthly returns for a broad sample of U.S. stocks in the top 90% of market capitalizations for that year, monthly equity market returns and monthly U.S. Treasury bill yields as the risk-free rate during January 1965 through December 2015, they find that: Keep Reading

Carry Trade Across Futures Asset Classes

Does a carry trade derived from roll yields of futures/forward contracts work within asset classes (undiversified) and across asset classes (iversified)? In his May 2017 paper entitled “Optimising Cross-Asset Carry”, Nick Baltas explores the profitability of cross-sectional (relative) and time-series (absolute) carry strategies within and across futures/forward markets for currencies, stock indexes, commodities and government bonds. He posits that contracts in backwardation (contango) present a positive (negative) roll yield and should generally be overweighted (underweighted) in a carry portfolio. He considers three types of carry portfolios, each reformed monthly:

  1. Cross-sectional (XS) or Relative – Rank all assets within a class by strength of carry, demean the rankings such that half are positive and half are negative and then assign weights proportional to demeaned ranks to create a balanced long-short portfolio. Combine asset classes by applying inverse volatility weights (based on 100-day rolling windows of returns) to each class portfolio.
  2. Times-series (TS) or Absolute – Go long (short) each asset within a class that is in backwardation (contango), such that the class may be net long or short. Combine asset classes in the same way as XS.
  3. Optimized (OPT) – Apply both relative strength and sign of carry to determine gross magnitude and direction (long or short) of positions for all assets, and further apply asset volatilities and correlations (based on 100-day rolling windows of returns) to optimize return/risk allocations.

Using daily data for 52 futures series (20 commodities, eight 10-year government bonds, nine currency exchange rates versus the U.S. dollar and 15 country stock indexes) during January 1990 through January 2016, he finds that: Keep Reading

Making Minimum Variance Stock Portfolios Work

What modifications must investors make to minimum variance portfolios to make them more attractive than equal weighting? In their April 2017 paper entitled “Asset Allocation with Correlation: A Composite Trade-Off”, Rachael Carroll, Thomas Conlon, John Cotter and Enrique Salvador assess conditions under which a minimum variance portfolio (requiring only estimates of asset covariances) beats an equally weighted portfolio. In particular, they test minimum variance portfolios that:

  • Employ one of three ways (one constant and two dynamic) to estimate future asset return correlations.
  • Consider a range of correlation forecasting horizons.
  • Do and do not have shorting restrictions.
  • Limit turnover by rebalancing only when: (1) any weight drifts outside a fixed percentage band; or, (2) any asset drifts outside a no-trade range based on its volatility, such that each asset has the same probability of triggering (allowing riskier assets more latitude).
  • Have rebalancing frictions of either 0.2% or 0.5% of traded value.

These variations enable analyses of trade-offs among parameter estimation error, correlation forecasting horizon, turnover and rebalancing frictions. Their key portfolio performance metrics are volatility, Sharpe ratio and turnover. They consider seven asset universes for forming minimum variance portfolios: 10, 30 or 48 U.S. industry portfolios during January 1970 through December 2013; 20 portfolios of U.S. stocks sorted by size and book-to-market ratio during January 1970 through December 2013; stock indexes for nine developed countries during January 1980 through December 2013; the 30 stocks in the Dow Jones Industrial Average during January 2003 through December 2012; and, the 197 stocks continuously listed in the S&P 500 Index during January 1996 through December 2012. Using daily returns in excess of the risk-free rate for the assets in these universes, they find that: Keep Reading

Smart Life Cycle Investing?

Can investors improve retirement glide paths via judicious use of smart beta funds? In their March 2017 paper entitled “Life Cycle Investing and Smart Beta Strategies”, Bill Carson, Sara Shores and Nicholas Nefouse augment a conventional equities-bonds life cycle investing glide path with smart beta strategies. They use a conventional glide path, which gradually decreases the allocation to equities with age to a constant after retirement, to determine target risk levels over the life cycle. When the investor is young, they tilt equities toward the MSCI USA Diversified Multiple-Factor (DMF) Index to boost returns via value, size momentum and quality beta exposures. As the investor approaches retirement, they shift equities to the MSCI USA Minimum Volatility Index, designed to match the market return at lower risk. For bonds, they use the Barclays Constant Weights Index, which has greater diversification and higher Sharpe ratio than a conventional market capitalization-based bond index. They incorporate the specified smart beta indexes into the glide path via a procedure that maximizes Sharpe ratio while matching the risk of the conventional glide path. Specifically, they: (1) deviate no more than 3% from conventional glide path risk; (2) constrain smart beta equities beta relative to the Russell 1000 Index and the MSCI World Index ex U.S. to within 5% of the benchmark equities beta; (3) constrain smart beta bond index duration to within 0.05 years of the benchmark bonds duration; and, (4) require at least 1% allocation to bonds for all target date portfolios. Using monthly data for conventional capitalization-weighted U.S. equity and bond indexes and for the specified smart beta indexes during 2007 through 2016, they find that: Keep Reading

SACEMS and SACEVS Changes for Coordination and Liquidity

We developed the Simple Asset Class ETF Momentum Strategy (SACEMS) about six years ago and the Simple Asset Class ETF Value Strategy (SACEVS) about two years ago independently, focusing on the separate logic of asset choices for each. As tested in “SACEMS-SACEVS Mutual Diversification”, these two strategies are mutually diversifying, so combining them works better in some ways than using one or the other. Beginning May 2017, we are making four changes to these strategies for ease of implementation and combination, with modest compromises in logic. Specifically, we are: Keep Reading

Value-at-Risk Estimation Tutorial

What are the ins and outs of crash risk measurement via Value at Risk (VaR)? In their March 2017 paper entitled “A Gentle Introduction to Value at Risk”, Laura Ballotta and Gianluca Fusai provide an introduction to VaR in financial markets, with examples mainly from commodity markets. They address problems related to VaR estimation and backtesting at single asset and portfolio levels. Based largely on mathematics and empirical considerations, they conclude that: Keep Reading

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