# 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.

**January 2, 2018** - Fundamental Valuation, Momentum Investing, Strategic Allocation

The Value Strategy tracks the performance of two versions of the “Simple Asset Class ETF Value Strategy” (SACEVS), which seeks diversification across a small set of asset class exchange-traded funds (ETF) plus a monthly tactical edge from potential undervaluation of term, credit and equity risk premiums relative to historical averages. The two versions are: (1) most undervalued premium (**Best Value**); and, (2) weighting all undervalued premiums according to respective degree of undervaluation (**Weighted**).

The Momentum Strategy tracks the performance of three versions of the “Simple Asset Class ETF Momentum Strategy” (SACEMS), which seeks strategic diversification across asset classes via ETFs plus a monthly tactical edge from intermediate-term momentum. The three versions, all based on total ETF returns over recent months, are: (1) top one of nine ETFs (**Top 1**); (2) equally weighted top two (**EW Top 2**); and, (3) equally weighted top three (**EW Top 3**).

As of today, we commence tracking performance of Combined Value-Momentum Strategy (**SACEVS-SACEMS**), seeking diversification across asset classes and two widely accepted anomalies. This strategy holds SACEVS Best Value and SACEMS EW Top 3 with equal weights and end-of-month rebalancing coincident with SACEVS and SACEMS portfolio reformations.

**December 20, 2017** - Currency Trading, Strategic Allocation

Are cryptocurrencies potentially useful portfolio diversifiers? In their November 2017 paper entitled “Exploring the Dynamic Relationships between Cryptocurrencies and Other Financial Assets”, Shaen Corbet, Andrew Meegan, Charles Larkin, Brian Lucey and Larisa Yarovaya apply a battery of tests to analyze relationships: (1) among three cryptocurrencies; and, (2) between the cryptocurrencies and conventional asset classes. They consider cryptocurrencies with market values over $1B at the end July 2017: Bitcoin, Ripple and Litecoin. They consider equities (S&P 500 Index), bonds (Markit ITTR110), commodities (S&P GSCI Total Returns Index), currencies (U.S. Dollar Broad Index), gold (COMEX close) and S&P 500 implied volatility (VIX) as conventional asset classes. Using daily data for Bitcoin, Ripple and Litecoin and for conventional asset classes as specified during April 29, 2013 through April 30, 2017, *they find that:* Keep Reading

**December 11, 2017** - Fundamental Valuation, Strategic Allocation

Is leveraging with margin a good way to boost the performance of the “Simple Asset Class ETF Value Strategy” (SACEVS)? SACEVS each month allocates funds to one or more of the following three asset class exchange-traded funds (ETF), plus cash, based on relative valuations:

3-month Treasury bills (Cash)

iShares 20+ Year Treasury Bond (TLT)

iShares iBoxx $ Investment Grade Corporate Bond (LQD)

SPDR S&P 500 (SPY)

To investigate effects of margin, we augment SACEVS by: (1) initially applying 2X leverage via margin (limited by Federal Reserve Regulation T); (2) for each month with a positive portfolio return, adding margin at the end of the month to restore 2X leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore 2X leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratio, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics for Best Value (which picks the most undervalued premium) and Weighted (which weights all undervalued premiums according to degree of undervaluation) variations of SACEVS. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate and consider a range of margin interest rates as increments to this yield. Using monthly total (dividend-adjusted) returns for the specified assets during July 2002 (limited by TLT and LQD) through October 2017, *we find that:* Keep Reading

**December 8, 2017** - Momentum Investing, Strategic Allocation

Is leveraging with margin a good way to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS)? SACEMS each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

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 effects of margin, we augment SACEMS by: (1) initially applying 2X leverage via margin (limited by Federal Reserve Regulation T); (2) for each month with a positive portfolio return, adding margin at the end of the month to restore 2X leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore 2X leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratio, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate and consider a range of margin interest rates as increments to this yield. Using monthly total (dividend-adjusted) returns for the specified assets during February 2006 (limited by DBC) through October 2017, *we find that:* Keep Reading

**December 1, 2017** - Equity Options, Strategic Allocation, Volatility Effects

Can investors refine portfolio rebalancing while capturing a volatility risk premium (VRP) by systematically shorting options matched to target allocations of the underlying asset? In their October 2017 paper entitled “An Alternative Option to Portfolio Rebalancing”, Roni Israelov and Harsha Tummala explore multi-asset class portfolio rebalancing via an option selling overlay. The overlay sells out-of-the-money options such that, if stocks rise (fall), counterparties exercise call (put) options and the portfolio must sell (buy) shares. They intend their approach to counter short-term momentum exposure between rebalancings (when the portfolio is overweight winners and underweight losers) with short-term reversal exposure inherent in short options. For testing, they assume: (1) a simple 60%-40% stocks-bonds portfolio; (2) bond returns are small compared to stock returns (so only the stock allocation requires rebalancing); and, (3) option settlement via share transfer, as for SPDR S&P 500 (SPY) as the stock/option positions. They each month sell nearest out-of-the-money S&P 500 Index call and put options across multiple economically priced strikes and update the overlay intramonth if new economically priced strikes become available. Once sold, they hold the options to expiration. Using daily S&P 500 Total Return Index returns, Barclays US Aggregate Bond Index returns and closing bid/ask quotes for S&P 500 Index options equity options (with returns calculated in excess of the risk-free rate) during 1996 through 2015, *they find that:*

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**November 30, 2017** - Calendar Effects, Momentum Investing, Strategic Allocation

“Optimal Monthly Cycle for SACEMS?” investigates whether using a monthly cycle other than end-of-month (EOM) to pick winning assets improves performance of the Simple Asset Class ETF Momentum Strategy (SACEMS). This strategy each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

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)

In response, a subscriber asked whether sticking with an EOM cycle for determining the winner, but delaying signal execution, affects strategy performance. To investigate, we compare 23 variations of SACEMS portfolios that all use EOM to pick winners but shift execution from the contemporaneous EOM to the next open or to closes over the next 21 trading days (about one month). For example, EOM+5 uses an EOM cycle to determine winners but delays execution until the close five trading days after EOM. We focus on gross compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using daily dividend-adjusted opens and closes for the asset class proxies and the yield for Cash from the end of July 2006 (limited by DBC) through mid-November 2017, *we find that:* Keep Reading

**November 27, 2017** - Momentum Investing, Strategic Allocation

How lucky would a asset class picker with no skill have to be to match the performance of the Simple Asset Class Momentum Strategy (SACEMS), which each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

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 run 1,000 trials of a “strategy” that each month allocates funds to one, the equally weighted two or the equally weighted three of these nine assets picked at random. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics. Using monthly total (dividend-adjusted) returns and for the specified assets during February 2006 (limited by DBC) through October 2017, *we find that:*

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**November 15, 2017** - Strategic Allocation, Technical Trading, Volatility Effects

A subscriber requested comparison of four variations of an “Ivy 5” asset class allocation strategy, as follows:

- Ivy 5 EW: Assign equal weight (EW), meaning 20%, to each of the five positions and rebalance annually.
- Ivy 5 EW + SMA10: Same as Ivy 5 EW, but take to cash any position for which the asset is below its 10-month simple moving average (SMA10).
- Ivy 5 Volatility Cap: Allocate to each position a percentage up to 20% such that the position has an expected annualized volatility of no more than 10% based on daily volatility over the past month, recalculated monthly. If under 20%, allocate the balance of the position to cash.
- Ivy 5 Volatility Cap + SMA10: Same as Ivy 5 Volatility Cap, but take completely to cash any position for which the asset is below its SMA10.

To perform the tests, we employ the following five asset class proxies:

iShares 7-10 Year Treasury Bond (IEF)

SPDR S&P 500 (SPY)

Vanguard REIT ETF (VNQ)

iShares MSCI EAFE Index (EFA)

PowerShares DB Commodity Index Tracking (DBC)

We consider monthly performance statistics, annual performance statistics, and full-sample compound annual growth rate (CAGR) and maximum drawdown (MaxDD). The DBC series in combination with the SMA10 rule are limiting with respect to sample start date and the first return calculations. Using daily and monthly dividend-adjusted closing prices for the five asset class proxies and the yield on 13-week U.S. Treasury bills (T-bills) as a proxy for return on cash during February 2006 through October 2017 (141 months), *we find that:* Keep Reading

**November 8, 2017** - Momentum Investing, Strategic Allocation

Subscribers have asked whether substituting leveraged exchange-traded funds (ETF) in the “Simple Asset Class ETF Momentum Strategy” (SACEMS) might enhance performance. To investigate, we execute the strategy with the following eight 2X leveraged ETFs, plus cash:

DB Commodity Double Long (DYY)

ProShares Ultra MSCI Emerging Markets (EET)

ProShares Ultra MSCI EAFE (EFO)

ProShares Ultra Gold (UGL)

ProShares Ultra S&P500 (SSO)

ProShares Ultra Russell 2000 (UWM)

ProShares Ultra Real Estate (URE)

ProShares Ultra 20+ Year Treasury (UBT)

3-month Treasury bills (Cash)

We consider portfolios of Top 1, equally weighted (EW) Top 2 and EW Top 3 past winners. We include as benchmarks: an equally weighted portfolio of all ETFs, rebalanced monthly (EW All); buying and holding SSO (SSO); and, holding SSO when the S&P 500 Index is above its 10-month simple moving average (SMA10) and Cash when the index is below its SMA10 (SSO:SMA10). Using monthly adjusted closing prices for the specified ETFs and the yield for Cash over the period January 2010 (the earliest month prices for all eight ETFs are available) through September 2017, *we find that:* Keep Reading

**November 3, 2017** - Fundamental Valuation, Strategic Allocation

Are there stock return forecasts good enough to make mean-variance optimization work as a stock portfolio allocation strategy? In their October 2017 paper entitled “Mean-Variance Optimization Using Forward-Looking Return Estimates”, Patrick Bielstein and Matthias Hanauer test whether firm implied cost of capital (ICC) based on analyst earnings forecasts is effective as a stock return forecast for mean-variance portfolio optimization. They derive ICC annually for each stock as the internal rate of return (discount rate) implied by a valuation model that equates forecasted cash flows, derived from analyst earnings forecasts, to market valuation. To refine ICC estimates, they correct predictable analyst forecast errors (slow reactions to news) by including a standardized, rescaled momentum variable based on return from 12 months ago to one month ago (ICC_{adj}). They then employ ICC_{adj} to specify annual (each June 30) mean-variance optimized (maximum Sharpe ratio) long-only stock allocations (with maximum weight 5%) based on stock return covariances calculated from returns over the last 60 months. For benchmarks, they consider the value-weighted market portfolio (VW), the equal-weighted market portfolio (EW), the minimum variance portfolio (MVP) and a maximum Sharpe ratio portfolio based on 5-year moving average actual returns (HIST). They focus on U.S. stocks, which have relatively broad analyst coverage. They test robustness of findings with data from selected international developed markets, different return variable specifications, different subperiods and impact of transaction costs. Using monthly data for the 1,000 U.S. common stocks with the biggest prior-month market capitalizations since June 1985 and the 250 biggest stocks in each of Europe, UK and Japan since 1990, all through June 2015, *they find that:*

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