Strategic Allocation

How stable are risk premiums? How should investors respond to instabilities? In his August 2010 paper entitled “A New ‘Risky’ World Order: Unstable Risk Premiums: Implications for Practice”, Aswath Damodaran presents approaches for estimating equity, bond and real asset risk premiums that are imprecise, unstable and linked across markets. He also explores the implications of dynamic, linked premiums for asset allocation, market timing and asset valuation. Using long-run data for all three asset classes, he concludes that: Keep Reading

How do stock index volatility and variance futures contracts compare with stock index put options as hedges against market crashes? In their August 2010 paper entitled “Using Volatility Instruments as Extreme Downside Hedges”, Bernard Lee and Yueh-Neng Lin investigate the effectiveness of stock index volatility and variance futures contracts as extreme downside hedges and compare this effectiveness to that of out-of-the-money index put options. Specifically, they compare the outcomes of hedging a long Standard & Poor’s Depository Receipts (SPY) position via 1-month and 3-month rolling positions in S&P 500 Volatility Index (VIX) futures contracts, S&P 500 3-month Variance Futures (VT) contracts and 10% out-of-the-money (OTM) S&P 500 Index put options with reasonable hedge trading frictions. Using price data for SPY, VIX and VT futures contracts and index put options spanning 6/10/04-10/14/09 for 1-month rolling hedges and 7/19/04-9/9/09 for 3-month rolling hedges, they find that: Keep Reading

Is diversification across equity anomalies beneficial? In his December 2009 preliminary paper entitled “Diversification Across Characteristics”, Erik Hjalmarsson combines long-short portfolios formed on seven stock anomalies:

1. Short-term (one-month) reversal (ST-R)
2. Medium-term (11 months plus skip-month) momentum (Mom)
3. Long-term (four years plus skip-year) reversal (LT-R)
4. Book-to-market value (B/M)
5. Cash flow-to-price ratio (C/P)
6. Earnings-to-price ratio (E/P)
7. Market capitalization (Size)

The portfolio for each anomaly is long (short) on an equally weighted basis the tenth of stocks expected to generate the most positive (negative) returns, reformed each month. Using monthly firm characteristics and return data for all NYSE, AMEX and NASDAQ stocks over the period July 1951 through December 2008, he finds that: Keep Reading

Is there a way to optimize dynamically the degree of leverage for an investment? In his November 2009 paper entitled “On the Performance of Leveraged and Optimally Leveraged Investment Funds”, Guido Giese derives a general model for leveraged multi-asset investment strategies with daily re-balancing applicable to leveraged long and short Exchange-Traded Funds (ETF) and leveraged carry trades. Using daily data for the Dow Jones EURO STOXX 50 Index, the Dow Jones EURO STOXX 50 Volatility Index (VSTOXX) and the Euro OverNight Index Average (EONIA) rate from end of 1991 through May 2009, he concludes that: Keep Reading

Are there alternatives to traditional beta and capitalization weighting strategies for asset allocation that improve investing outcomes? In the October 2009 version of their paper entitled “Beyond Cap-Weight: The Empirical Evidence for a Diversified Beta”, Rob Arnott, Vitali Kalesnik, Paul Moghtader and Craig Scholl explore diversification of beta risk by comparing the merits of four basic major strategies for portfolio weighting from a global perspective: Cap Weight; Equal Weight; Minimum Variance weighting; and, Economic Scale weighting. They also examine two combination strategies: Efficient Beta, an equal weighting of Cap Weight, Economic Scale and Minimum Variance; and, an equal weighting of all four basic strategies. Using dollarized returns and other data necessary for construction of indexes comprised of the 1,000 largest (by market capitalization) companies across 23 developed countries over the period January 1993 through June 2009, they conclude that: Keep Reading

What is the best way to deploy assets for retirement? In his September 2009 paper entitled “Life is Non-linear: Structuring Retirement Portfolios for the Long Haul”, Joachim Klement analyzes six common retirement portfolio strategies in terms of their longevity and income generation over a retiree’s expected lifetime. The study emphasizes that income requirements vary during retirement, first declining with age and then accelerating near the end of life. The study applies Monte Carlo simulation based on the following assumptions: annual rebalancing of assets to strategic portfolio weights; total annual fees of 1% of portfolio value; inflation rate of 3%; 15% tax rate on portfolio cash flow; normal distributions of annual returns with means (standard deviations) of 9.4% (15%) for stocks and 5.3% (5.4%) for bonds; and, correlation between stock and bond returns of 0.20. Using this model, he concludes that: Keep Reading

Based on Modern Portfolio Theory (MPT) and inferences from historical asset class returns, what are the best portfolio allocations for different levels of risk? In their February 2009 paper entitled “Strategic Asset Allocation: Determining the Optimal Portfolio with Ten Asset Classes”, Niels Bekkers, Ronald Doeswijk and Trevin Lam explore which asset classes add mean-variance diversification value to a traditional portfolio of stocks, bonds and cash and determine the weights of asset classes in optimal portfolios (maximum Sharpe ratio). Their total set of ten asset classes consists of stocks, government bonds, cash, private equity, real estate, hedge funds, commodities, high yield bonds, credits and inflation-linked bonds. Using mostly U.S. data as available for historical asset class returns and volatilities, they conclude that: Keep Reading

Is a “Rip Van Winkle” asset allocation strategy, wherein an investor gradually migrates from stocks to fixed income in pre-specified steps, optimum? Or, is there some simple, less passive alternative that takes equity bull and bear markets into account? In their November 2008 paper entitled “Dynamic Lifecycle Strategies for Target Date Retirement Funds”, Anup Basu, Alistair Byrne and Michael Drew question the rationale for pre-determined lifecycle equity/fixed income rebalancing and compare it to an alternative 40-year dynamic lifecycle strategy that flexibly rebalances depending on success to date. The dynamic strategy holds 100% stocks for the first 20 or 30 years and then annually switches partially to fixed income or remains 100% in stocks depending on whether or not it is achieving a target 10% annualized rate of return. The authors include 100% stocks and static balanced 60/30/10 stocks/bonds/cash strategies as benchmarks. Using bootstrapping to augment a dataset of annual nominal returns for U.S. stocks, bonds and bills spanning 1900-2004 (105 years), they conclude that: Keep Reading

Does increasingly powerful and more automated trading technology create the need for more sophisticated equity return benchmarks? In the December 2007 version of their paper entitled “130/30: The New Long-Only”, Andrew Lo and Pankaj Patel present a passive but dynamic “plain-vanilla” 130% long/30% short (130/30) benchmark index based on: (1) simple factors (encompassing value, growth, profitability, momentum and technical) to rank stocks; and, (2) standard methods for constructing a portfolio based on these rankings. Applying a standard portfolio optimizer to 10 well-known and commercially available valuation factors for S&P 500 stocks, with monthly rebalancing during 1/96-9/07, they find that: Keep Reading

With increasing business globalization and financial markets integration, can equity investors still get good risk reduction by diversifying their portfolios across country markets? Or, have other kinds of diversification become more important? In their paper entitled “The Changing Roles of Industry and Country Effects in the Global Equity Markets”, Kate Phylaktis and Lichuan Xia examine the evolution of country and industry effects on stock returns and diversification. Using weekly returns from the Dow Jones Global Indexes (DJGI) encompassing 4,801 companies in 51 industry groups across 34 countries over the period 1992 to 2001, they find that: Keep Reading