Strategic Allocation

Considering taxes, in what order should U.S. retirees consume different sources of retirement savings/income? In their August 2018 paper entitled “Constructing Tax Efficient Withdrawal Strategies for Retirees with Traditional 401(k)/IRAs, Roth 401(k)/IRAs, and Taxable Accounts”, James DiLellio and Daniel Ostrov describe and illustrate an algorithm that computes individualized tax-efficient consumption for U.S. retirees of:

• Tax-deferred retirement accounts [Traditional IRA/401(k)].
• Post-tax retirement accounts [Roth IRA/Roth 401(k)].
• Other taxable retirement accounts.
• Other sources of money subject to income tax, including: earned income, some pensions, annuities bought with pre-tax money, earnings from annuities bought with post-tax money and sometimes Social Security benefits.
• Other sources of money that do not affect tax rates of retirement accounts, such as: tax-free gifts, Health Savings Accounts, some pensions, principal from annuities bought with post-tax money and sometimes Social Security benefits.

Their model adapts to individual retiree circumstances and accommodates typical changes in tax policies (changes in marginal rates and number of brackets). For tractability, they make simplifying assumptions. The principal simplification is that  return on stocks, stock dividend yield, inflation rate, tax brackets and rates, other income sources and consumption rates are known each year (not random variables). When the goal is to optimize a bequest, inputs also include year of retiree death, marginal tax rate of the heir and rate the heir consumes inherited retirement accounts. They do not attempt to determine the optimal mix of  stocks and bonds/cash within retirement accounts (their deterministic model would prefer all stocks). Using illustrations of algorithm outputs based on varying input assumptions, they find that: Keep Reading

Are mean-variance (MV) strategies preferable for allocations to asset classes and equal-weight (EW) preferable for allocations to much noisier individual assets? In their May 2019 paper entitled “Horses for Courses: Mean-Variance for Asset Allocation and 1/N for Stock Selection”, Emmanouil Platanakis, Charles Sutcliffe and Xiaoxia Ye address this question. They focus on the Bayes-Stein shrinkage MV strategy, with 10 U.S. equity sector indexes as asset classes and the 10 stocks with the largest initial market capitalizations within each sector (except only three for telecommunications) as individual assets. The Bayes–Stein shrinkage approach dampens the typically large effects of return estimation errors on MV allocations. For estimation of MV return and return covariance inputs, they use an expanding (inception-to-date) 12-month historical window. They focus on one-month-ahead performances of portfolios formed in four ways via a 2-stage process:

1. MV-EW, which uses MV to determine sector allocations and EW to determine stock allocations within sectors.
2. EW-EW, which uses EW for both deteriminations.
3. EW-MV, which uses EW to determine sector allocations and MV to determine stock allocations within sectors.
4. MV-MV, which uses MV for both deteriminations.

They consider four net performance metrics: annualized certainty equivalent return (CER) gain for moderately risk-averse investors; annualized Sharpe ratio (reward for risk); Omega ratio (average gain to average loss); and, Dowd ratio (reward for value at risk). They assume constant trading frictions of 0.5% of value traded. They perform robustness tests for U.S. data by using alternative MV strategies, different parameter settings and simulations. They perform a global robustness test using value-weighted equity indexes for UK, U.S., Germany, Switzerland, France, Canada and Brazil as asset classes and the 10 stocks with the largest initial market capitalizations within each index as individual assets (all in U.S. dollars). Using monthly total returns for asset classes and individual assets as specified and 1-month U.S. Treasury bill yield as the risk-free rate during January 1994 through August 2017, they find that: Keep Reading

For investors embracing the concept of portfolios based on factor premiums (rather than asset classes), what is the best factor allocation approach? In their March 2019 paper entitled “Factor-Based Allocation: Is There a Superior Strategy?”, Hubert Dichtl, Wolfgang Drobetz and Viktoria-Sophie Wendt search for the best way of combining factors in a portfolio after accounting for bias introduced from snooping many alternative allocation strategies. They consider the following 10 factors (mostly long-short) suitable for a U.S. institutional investor constrained to global equity and fixed income securities: equity, value, size, momentum, quality, low-volatility, term, real rates, credit and high-yield. They construct factors using associated published indexes denominated in U.S. dollars, with 1-month U.S. Treasury bill (T-bill) yield as the risk-free rate. They consider 17 factor allocation strategies: equal weight, minimum variance, equal risk, maximum diversification, volatility timing, reward-to-risk timing, mean-variance optimization without and with shrinkage, Black-Litterman and eight combinations of these strategies. Their test portfolio holds a 100% position in cash and a fully hedged (long-short, or zero net investment) factor portfolio, subject to 0.5% trading frictions on portfolio turnover. Using monthly data required to construct factors and T-bill yield during January 2001 though December 2018, with the first 60 months set aside to estimate strategy inputs, they find that:

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How should investors manage their portfolios to withstand market crashes. In his March 2019 paper entitled “Managing the Downside of Active and Passive Strategies: Convexity and Fragilities”, Raphael Douady discusses how to construct an “antifragile” portfolio given that most equity market risk is not readily observable. He describes ways to monitor the probability of a new crisis. Based on in-depth analysis of market behaviors during past speculative bubbles and other crises, he concludes that:

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What are optimal allocations during retirement years for a portfolio of stocks and bonds, without and with a trend following overlay? In their March 2019 paper entitled “Absolute Momentum, Sustainable Withdrawal Rates and Glidepath Investing in US Retirement Portfolios from 1925”, Andrew Clare, James Seaton, Peter Smith and Steve Thomas compare outcomes across two sets of U.S. retirement portfolios since 1925:

1. Standard – allocations to the S&P 500 Index and a bond index ranging from all stocks to all bonds in increments of 10%, rebalanced at the end of each month.
2. Trend following – the same portfolios with a trend following overlay that shifts stock index and bond index allocations to U.S. Treasury bills (T-bills) when below respective 10-month simple moving averages at the end of the preceding month.

They consider investment horizons of 2 to 30 years to assess glidepath effects. They consider both U.S. Treasury bonds and U.S. corporate bonds to assess credit effects. For comparison of portfolio outcomes, they use real (inflation-adjusted) returns and focus on Perfect Withdrawal Rate (PWR), the maximum annual withdrawal rate that results in zero terminal value (requiring perfect foresight). Using monthly data for the S&P 500 Index, U.S. government and corporate bond indexes and U.S. inflation during 1926 through 2016, they find that: Keep Reading

Do exposures to long-short factor (alternative risk) premiums (ARP) protect portfolios from stock and bond market crashes? In their February 2019 paper entitled “A Framework for Risk Premia Investing: Anywhere to Hide?”, Kari Vatanen and Antti Suhonen examine weekly correlations of 28 ARP composite returns with those of stocks (MSCI World Equity Market Index), bonds (Barclays Global Treasury Index) and commodities (Bloomberg Commodity Index), overall and during crashes, over an 11-year sample period. They form each ARP composite using both backtested and live data for at least three related strategies from different investment banks, weighted by inverse full-sample volatility and rebalanced weekly. They focus on ARP composite performances when stocks and bonds are weak. Based on findings, they then designate ARP composites as:

• Offensive (benefiting from high economic growth but suffering from low growth and economic turbulence) or defensive (diversifying risks of offensive strategies).
• Fundamental (based on investor risk aversion), behavioral (based on typical investor behavior) or structural (based on seasonal asset flows or on market inefficiencies and liquidity imbalances).

Using daily data as specified for long-short alternative risk premium strategies from seven global investment banks during January 2007 to the beginning of May 2018, they find that:

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A subscriber asked about an annually rebalanced portfolio of 50% stocks and 50% trend following managed futures as recommended in a 2014 Greyserman and Kaminski book [Trend Following with Managed Futures: The Search for Crisis Alpha], suggesting Equinox Campbell Strategy I (EBSIX) as an accessible managed futures fund. To investigate, we consider not only EBSIX (inception March 2013) but also a longer trend following hedge fund index with monthly returns back to December 1999. This alternative “is an equally weighted index of 37 constituent funds…designed to provide a broad measure of the performance of underlying hedge fund managers who invest with a trend following strategy.” The correlation of monthly returns between this index and EBSIX during April 2013 through February 2019 is 0.84, indicating strong similarity. We use SPDR S&P 500 (SPY) as a proxy for stocks. Using annual returns for EBSIX during 2014-2018 and for the trend following hedge fund index and SPY during 2000-2018, we find that: Keep Reading

A subscriber asked about applying the “Simple Asset Class ETF Momentum Strategy” to the funds available to U.S. federal government employees via the Thrift Savings Plan (TSP). To investigate, we test the strategy on the following five funds:

G Fund: Government Securities Investment Fund (G)
F Fund: Fixed Income Index Investment Fund (F)
C Fund: Common Stock Index Investment Fund (C)
S Fund: Small Cap Stock Index Investment Fund (S)
I Fund: International Stock Index Investment Fund (I)

We each month rank these funds based on returns over past (lookback) intervals of one to 12 months. We test Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly fund winners. We employ as a benchmark a naively diversified EW portfolio of all five funds, rebalanced monthly (EW All). Using monthly returns for the five funds from initial availability of all five (January 2001) through February 2019, we find that:

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Subscribers asked whether the monthly winner (Top 1) of the Simple Asset Class ETF Momentum Strategy (SACEMS) is more prone to mean reversion than momentum, thereby justifying its exclusion from or lower weight within SACEMS portfolios. SACEMS each month picks winners from the following universe of eight asset class exchange-traded funds (ETF), plus cash:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

To investigate, we review relevant past research and conduct in-depth robustness tests of SACEMS monthly returns and volatilities across all ranks 1 through 9 and ranking (lookback) intervals one to 12 months. Limited by availability of DBC (inception February 2006) and a 12-month lookback interval, we start the comparison with March 2007. Using monthly dividend adjusted closing prices for the asset class proxies and the yield for Cash during February 2006 through February 2019, we find that: Keep Reading

How should investors feel about factor/multi-factor investing? In their February 2019 paper entitled “Alice’s Adventures in Factorland: Three Blunders That Plague Factor Investing”, Robert Arnott, Campbell Harvey, Vitali Kalesnik and Juhani Linnainmaa explore three critical failures of U.S. equity factor investing:

1. Returns are far short of expectations due to overfitting and/or trade crowding.
2. Drawdowns far exceed expectations.
3. Diversification of factors occasionally disappears when correlations soar.

They focus on 15 factors most closely followed by investors: the market factor; a set of six factors from widely used academic multi-factor models (size, value, operating profitability, investment, momentum and low beta); and, a set of eight other popular factors (idiosyncratic volatility, short-term reversal, illiquidity, accruals, cash flow-to-price, earnings-to-price, long-term reversal and net share issuance). For some analyses they employ a broader set of 46 factors. They consider both long-term (July 1963-June 2018) and short-term (July 2003-June 2018) factor performances. Using returns for the specified factors during July 1963 through June 2018, they conclude that:

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