Strategic Allocation

Should investors consider a broader framework to encompass trend-following/momentum investing strategies? In his March 2013 paper entitled “Asset Price Trend Theory: Reframing Portfolio Theory from the Ground Up”, Robert Dubois presents a portfolio allocation strategy that explicitly includes an assumption that asset prices trend (exhibit return autocorrelation or intrinsic momentum). His approach augments risk management by including stop-loss protocols to exit allocations to some assets/strategies based on their trends relative to specified thresholds. He defines three strategic allocation segments: (RB1) risk-free assets; (RB2) liquid risky assets/strategies subject to stop-loss exits at the asset and/or portfolio level; and, (RB3) both liquid and illiquid risky assets not subject to stop-loss exits. All three segments are subject to asset/strategy selection, position sizing and entry rules. RB2 arguably allows strong risk management (containment) via trend-related removal of exposure to risky assets with below-trend performance. Elaborating on this framework, he concludes that: Keep Reading

How stable is the mean-variance efficient frontier specified by Modern Portfolio Theory (MPT), and what drives changes to it? In his March 2013 paper entitled “Principal Component Analysis of Time Variations in the Mean-Variance Efficient Frontier”, Andreas Steiner applies principal component analysis to explore sources of the variability in the efficient frontier. He uses weekly data and rolling 52-week intervals to calculate the efficient frontier (via asset returns, volatilities and correlations) for a long-only, unleveraged portfolio of 22 Swiss stocks. He next discovers purely statistical independent linear factors needed to describe efficient frontier variability (using ten sample points along each efficient frontier curve). He then measures the relative importance of the factors and relates them to common investment performance statistics (average returns, volatilities and correlations). Using weekly returns for the specified stocks during late June 2002 through December 2012 (549 weeks), he finds that: Keep Reading

Are target‐date (glidepath) funds that periodically decrease (increase) allocation to stocks (bonds and cash) as the investor ages competitive with alternative strategies? In his February 2013 paper entitled “The Glidepath Illusion: An International Perspective”, Javier Estrada evaluates three alternative types of strategies, all based on a working life of 40 years with annual retirement fund contributions of $1,000 (inflation‐adjusted for a cumulative contribution of $40,000 in real terms):

1. Lifecycle strategies with initial stock-bond allocations (in percent) of 100‐0, 90‐10, 80‐20, 70‐30 and 60‐40, and respective final allocations of 0-100, 10‐90, 20‐80, 30‐70 and 40‐60. Allocation adjustments are annual on linear glidepaths.
2. Mirror image strategies that start with 0-100, 10‐90, 20‐80, 30‐70 and 40‐60 allocations and end with 100‐0, 90‐10, 80‐20, 70‐30 and 60‐40 allocations.
3. Five alternative strategies: fully invested in stocks throughout the 40‐year working life (100×40); fully invested in stocks for the first 20 or 30 years, then shifting annually and linearly out of stocks and into bonds for the remaining 20 or 10 years to end with a 50‐50 stock‐bond allocation (100×20 or 100×30); and, a constant stock-bond allocation of 50‐50 or 60‐40 over 40 years, rebalanced annually.

For each of the World, Europe and 19 developed countries, he generates terminal wealth statistics for 71 overlapping 40-year intervals, the first spanning 1900-1939 and the last spanning 1970-2009. Using real total returns for individual countries (in local currencies adjusted by local inflation) and for the World and Europe (in dollars adjusted by U.S. inflation) during 1900 through 2009, he finds that: Keep Reading

In the current environment of low bond yields, what is a safe investment withdrawal rate during retirement? In their January 2013 paper entitled “The 4% Rule is Not Safe in a Low-Yield World”, Michael Finke, Wade Pfau and David Blanchett model the risk of exhausting wealth for different retirement durations, withdrawal rates, stocks-bonds portfolio mixes and assumptions about future market conditions (stock and bond returns). They model retirement portfolio dynamics via Monte Carlo simulations applied to log-normal return distributions, based on an inflation-adjusted withdrawal rate set at a percentage of investment portfolio value at retirement and annual rebalancing to a target asset allocation (with failure whenever a withdrawal results in a zero balance). They assume withdrawals cover any taxes due and ignore any portfolio fees/frictions. They focus on a 30-year retirement under current market conditions, with bonds yielding an average annual real return of -1.4% (based on current yields for 5-year Treasury Inflation-Protected Securities) and stocks yielding an average annual real return of 4.6% (6.0% equity risk premium over the bond yield). They also consider a case based on 1926-2011 average annual real returns of 2.6% for bonds and 8.6% for stocks, and cases for which current low returns revert to historical averages five or ten years into retirement. Using the specified modeling assumptions and parameters, they find that: Keep Reading

How should investors choose among alternative tactical asset allocation strategies? In their January 2013 paper entitled “Rethinking the Asset Allocation Approach for Plan Sponsors”, Pranay Gupta and Sven Skallsjo present a multi-strategy tactical asset allocation framework for very large (institutional) investors. They assume that the strategic asset allocation (portfolio policy) is to maximize capital appreciation with “the highest efficiency” and 90% confidence that annual drawdown will not exceed 10%. In developing their allocation framework development, they consider recent statistics describing the performance and interaction of eight asset class indexes, each able to absorb large investments/rebalancing actions (four global regional equities, global government bonds, global corporate bonds, global high-yield bonds and gold). Using illustrations based on monthly asset class index returns during September 2000 through September 2012, they conclude that: Keep Reading

What research is available on investment approaches, allocations and results for U.S. university endowments? In their January 2013 paper entitled “A Survey of University Endowment Management Research”, Georg Cejnek, Richard Franz, Otto Randl and Neal Stoughton summarize available research on university endowment money management and performance. They identify four streams of research: (1) governance structure and investment policy statement; (2) asset allocation; (3) performance; and, (4) spending. Based on this research, they conclude that: Keep Reading

Which risk-based asset allocation method is best? In their January 2013 preliminary paper entitled “Generalized Risk-Based Investing”, Emmanuel Jurczenko, Thierry Michel and Jerome Teletche present a general framework for risk-based asset allocation depending on two parameters: (1) level of sensitivity to asset return variance and correlation estimates; and, (2) level of tolerance for assets with high return volatilities. Popular strategies such as Minimum Variance (MV, which assumes assets have equal expected returns), Maximum Diversification (MD, which assumes assets have equal expected Sharpe ratios), Equal Weight (EW, which makes no assumptions about expected asset returns or volatilities) and Risk Parity (RP, which assumes assets have equal expected Sharpe ratios and pairwise correlations) are special cases of this general framework. They investigate the theoretical properties of this class of strategies, categorizing them by volatility, market beta, market tracking error, concentration and turnover. They illustrate theoretical conclusions with empirical findings for portfolios reformed monthly based on analysis of a two-year rolling window of individual stock returns. Using mathematical derivations and a large sample of developed market stocks (based on MSCI World composition) during January 2002 through October 2012, they conclude that: 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

Does combining different portfolio performance enhancement concepts actually improve outcome? In their December 2012 paper entitled “Generalized Momentum and Flexible Asset Allocation (FAA): An Heuristic Approach”, Wouter Keller and Hugo van Putten investigate the effects of combining momentum, volatility and correlation selection criteria to form an equally weighted portfolio of the three best funds from a set of mutual fund proxies for seven asset classes, as follows:

1. To follow trend, rank funds from highest to lowest lagged total return (relative momentum).
2. To suppress volatility, rank funds from lowest to highest volatility (standard deviation of daily returns).
3. To enhance diversification, rank funds from lowest to highest average pairwise correlation of daily returns.
4. To avoid drawdown, replace with cash any selected fund that has a negative lagged return (intrinsic or absolute momentum). 

Their seven asset class proxies are index mutual funds for U.S. stocks (VTSMX), developed market stocks outside the U.S. and Canada (FDIVX), emerging market stocks (VEIEX), mid-term U.S. Treasuries (VBMFX), short-term U.S. Treasuries (VFISX), commodities (QRAAX) and real estate (VGSIX). They use a default lagged measurement interval of four months for all four selection criteria. Their method of combining rankings for relative momentum, volatility and correlation is simple weighted average (with default weightings of 1, 0.5 and 0.5, respectively). They assume momentum calculations occur at the end of each month, with portfolio changes at the beginning of the next month. Using daily closing prices in U.S. dollars for the seven mutual funds from mid-1997 through mid-December 2012, they find that: Keep Reading

Does mean-variance optimization enhance the performance of currency carry trades (long currencies with high interest rates and short currencies with low interest rates)? In their November 2012 paper entitled “On the Risk and Return of the Carry Trade”, Fabian Ackermann, Walt Pohl and Karl Schmedders compare a dynamic mean-variance optimal carry trade strategy to naive ones. Specifically, they consider a series of monthly investments that are long (short) those of the following currencies with the highest (lowest) associated interest rates: U.S. dollar (base currency), Swiss franc, Euro, Japanese yen, British pound, Australian dollar, Canadian dollar, Norwegian krone, Swedish krona, Singapore dollar and New Zealand dollar. For monthly mean-variance optimization, they estimate currency correlations based on the last 250 days (one year) of daily data and set an annual excess return target of 5% (relative to the risk-free rate), the approximate excess return on the S&P 500 Index over the same period. For naive portfolios, they consider 1 long/1 short currencies, 3 long/3 short equally weighted currencies and the S&P 500 Index total return. Using daily currency values and monthly S&P 500 Index data during January 1989 through June 2012 (using the first year for initial optimization), they find that: Keep Reading