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

The Simple Asset Class ETF Momentum Strategy (SACEMS) seeks diversification across asset classes via ETFs plus a monthly tactical edge from intermediate-term momentum. The three versions of SACEMS, 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).

Based on feedback from subscribers about combinations of interest, we look at three equal-weighted (50-50) diversifying combinations of SACEVS and SACEMS, rebalanced monthly:

1. 50-50 Best Value – EW Top 2: SACEVS Best Value paired with SACEMS Equally Weighted (EW) Top 2 (aggressive value and somewhat aggressive momentum).
2. 50-50 Best Value – EW Top 3: SACEVS Best Value paired with SACEMS EW Top 3 (aggressive value and diversified momentum).
3. 50-50 Weighted – EW Top 3: SACEVS Weighted paired with SACEMS EW Top 3 (diversified value and diversified momentum).

Supporting research includes (items may at times be unavailable for a few days during updates):

• “SACEMS-SACEVS for Value-Momentum Diversification” tests benefits of diversifying across asset class ETFs based on both relative momentum and relative value.
• “SACEVS and SACEMS Strategy Momentum?” examines momentum of SACEVS and SACEMS returns to guide relative weighting of the two in a combined value-momentum strategy.
• “SACEVS Best Value + SACEMS EW Top 2?” tests an alternative to the tracked combined value-momentum strategy.
• “Forcing SACEMS to Agree with SACEVS” tests effects on Simple Asset Class ETF Momentum Strategy (SACEMS) performance of forcing SACEMS to agree with SACEVS when the latter assigns zero weight to stocks or government bonds.
• “SACEMS-SACEVS Diversification with Mutual Funds” provides an extended test of the benefits of diversifying across asset classes based on both relative momentum and relative value with sets of mutual funds.
• “SACEMS and SACEVS Changes for Coordination and Liquidity” documents minor April 2017 adjustments that trade purity of logic for practicality.
• “SACEVS-SACEMS Leverage Sensitivity Tests” explores use of margin rebalanced monthly to boost performance.  “Conditionally Substitute SSO for SPY in SACEVS and SACEMS?” test a single specific leveraged asset substitution in SACEVS and SACEMS.

Some additional relevant but less directly applicable research is in the last list of items in “What Works Best?“.

Some investors may want to follow one of the 50-50 combined strategies. Others may want to modify the strategy with other than equal weights for SACEVS and SACEMS, as explored in “SACEMS-SACEVS for Value-Momentum Diversification”.

Cumulative Performance

The following chart tracks gross cumulative values of $100,000 initial investments in each of the above three combination strategies since the end of June 2006. It includes as a benchmark a simple technical strategy (SPY:SMA10) that holds SPDR S&P 500 ETF Trust (SPY) when the S&P 500 Index is above its 10-month simple moving average and 3-month U.S. Treasury bills (Cash, or T-bills) when below. 

For perspective, we look at an array of performance metrics.

Performance Statistics

The following table summarizes annual/annualized returns for these three strategies, and for SPY and SPY:SMA10. Annualized returns are compound annual growth rates. Maximum drawdown is the deepest peak-to-trough drawdown for these strategies based on monthly measurements over the sample period. For Sharpe ratio, to calculate excess annual return, we use average monthly yield on 3-month Treasury bills during a year as the risk-free rate for that year.

Portfolio performance calculations are based on assumptions as summarized in Value Strategy and Momentum Strategy.

Something to keep in mind is that testing different SACEMS-SACEVS combinations and/or adjusting weights based on sensitivity tests incorporates data snooping bias, such that the best-performing combination overstates expectations.

Government securities, corporate bonds and equities arguably compete for investments at increasing levels of inherent risk based on: (1) valuations relative to each other, measured by risk premiums; and, (2) attractiveness of these risk premiums relative to their respective historical norms.

The Simple Asset Class ETF Value Strategy (SACEVS) seeks diversification across a small set of U.S. Treasury note, corporate bond and stock ETFs [iShares 20+ Year Treasury Bond (TLT), iShares iBoxx $ Investment Grade Corporate Bond (LQD) and SPDR S&P 500 (SPY)], plus a monthly tactical edge from timing the following three risk premiums associated with these asset classes:

1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
2. Credit – monthly difference between the  Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

There are two versions of SACEVS: (1) Best Value, which at the end of each month picks the most undervalued premium (if any); and, (2) Weighted, which at the end of each month weights all undervalued premiums (if any) according to degree of undervaluation. Based on the assets considered, the principal benchmark is a monthly rebalanced portfolio of 60% SPY-40% TLT (60-40).

Supporting research includes (items may at times be unavailable for a few days during updates):

• “U.S. Federal Taxes and SACEVS, SACEMS” explores the effects of federal taxes (in taxable accounts) on SACEVS performance.
• “Forcing SACEMS to Agree with SACEVS” tests effects on Simple Asset Class ETF Momentum Strategy (SACEMS) performance of forcing SACEMS to agree with SACEVS when the latter assigns zero weight to stocks or government bonds.
• “SACEVS with Quarterly Allocation Updates” tests the effects on performance of quarterly rather than monthly portfolio reformation.
• “SACEVS with SMA Filter” examines whether applying a simple moving average (SMA) filter to SACEVS selections improves strategy performance.
• “SACEVS Applied to Mutual Funds” extends testing backward in time by using mutual funds rather than ETFs to capture undervalued premiums.
• “SACEVS and SACEMS from a European Perspective” investigates the effects of the U.S. dollar-euro exchange rate on strategy performances, addressing the perspective of a European investor.
• “Add REITs to SACEVS?” tests the addition of a real estate risk premium, derived from the yield on equity Real Estate Investment Trusts (REIT).  “Substitute VIG for SPY in SACEVS and SACEMS?” tests a single specific asset substitution in SACEVS. “Conditionally Substitute SSO for SPY in SACEVS and SACEMS?” test a single specific leveraged asset substitution in SACEVS.
• “Effects of Execution Delay on SACEVS” looks at effects of delaying action on changes in SACEVS portfolio weights for up to 21 trading days.
• “SACEVS Performance When Interest Rates Rise” examines effects of interest rates changes on the strategy.
• “SACEVS Performance When Stocks Rise and Fall” investigates how the strategy performs when the U.S. stock market rises and falls.
• “SACEVS Input Risk Premiums and EFFR” tests how the strategy might react to increases in the Federal Funds Rate.
• “SACEVS with Margin” and “SACEVS-SACEMS Leverage Sensitivity Tests” explore use of margin rebalanced monthly to boost SACEVS performance.
• “SACEMS and SACEVS Changes for Coordination and Liquidity” documents minor April 2017 adjustments that trade purity of logic for practicality.

Some investors may want to follow one of the two strategy alternatives tracked here. Others may want to adapt them with modifications suited to their individual goals and constraints. Still others may want to apply the analysis approaches to test other strategies. Something to keep in mind is that adding complexity to the strategy with refining variables/parameters increases the number of ways to optimize and thereby elevates potential for data snooping bias.

The next section summarizes historical (backtest) performance data.

Historical Performance

The following chart shows the gross cumulative values of $100,000 initial investments in the Best Value and Weighted portfolios since the end of July 2002 (when all ETFs considered are first available). The chart includes the 60-40 portfolio as a benchmark and buying and holding SPY for reference.

The following table summarizes some monthly statistics for these same strategies and their ETF components over the available sample period. Return/Risk is average return divided by standard deviation. Maximum (peak-to-trough) drawdowns are based on monthly measurements over the available sample period. 

The next table summarizes annual/annualized returns for these strategies over different intervals commonly used to describe performance of funds. The annualized returns are compound annual growth rates (CAGR). For Sharpe ratio, to calculate excess annual return, we use average monthly yield on 3-month Treasury bills during a year as the risk-free rate for that year.

The next section offers a discussion of this performance.

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Do investors require a predictably substantial expected stock market real earnings yield (aggregate expected corporate operating earnings minus the expected inflation over the next 12 months, divided by stock index level)? In other words, when the forward real earnings yield is relatively high (low), do they bid stock prices up (down) to restore a normal gap between forward earnings yield and expected inflation. To investigate whether such a Real Earnings Yield (REY) model works, we relate the combined evolution of an operating earnings forecast for the S&P 500 and an inflation forecast to the behavior of the S&P 500 Index. To convert from the quarterly earnings cycle to a monthly frequency, we assume 50% / 40% / 10% of new earnings data for a quarter becomes known during the first / second / third month after quarter end. Availability of S&P 500 lagged (trailing 12 month) operating earnings from Standard and Poor’s as an earnings forecast input limits the sample period. Using monthly earnings forecast and S&P 500 Index data during March 1989 through December 2014, we find that: (more…)

Do investors require a predictably substantial expected stock market earnings yield (aggregate expected corporate operating earnings over the next 12 months divided by stock index level)? In other words, when the forward earnings yield is relatively high (low), do they bid stock prices up (down) to restore a normal expected yield. To investigate whether such a Reversion-to-Value (RTV) model works, we relate the evolution of an operating earnings forecast for the S&P 500 to the behavior of the S&P 500 Index. To convert from the quarterly earnings cycle to a monthly frequency, we assume 50% / 40% / 10% of new earnings data for a quarter becomes known during the first / second / third month after quarter end. Availability of S&P 500 lagged (trailing 12 month) operating earnings from Standard and Poor’s as an earnings forecast input limits the sample period. Using monthly earnings forecast and S&P 500 Index data during March 1989 through December 2014, we find that: (more…)

Section 8-2 examines in detail the attractiveness of a short-term trading strategy offered in the quasi-advisory (“educational”) marketplace. Assessing this strategy entails considerable work, only to find that it is not attractive. This chapter covers more broadly the delegation of investment strategy development, ranging from following an expert’s public advice on market timing to deposit of funds for professional management. Such practices relieve investors (at a cost) of some or all of the burdens of learning, data collection/analysis, strategy design and disciplined implementation.

However, such delegation entails agency issues (conflicts of interest). Potentially more than they want to help their readers/subscribers/clients earn exceptional investment returns:

• Media that present investing advice want subscription fees or attention to advertisements. Media company interest in the usefulness of what they present is arguably secondary to attracting attention. In general, contributors to free media also have motives that bias what they present (attracting their own subscribers or clients).
• Academics studying financial markets want employment (and tenure) and funding of future research. They therefore must attract the attention of peers and publishers. They often have no stake in whether their research findings are useful to investors. They do have an incentive to attract the attention of investors when making a transition to investment management.
• Expert equity analysts want employment by brokers and asset managers, and access to industry sources. The interests of their bosses may not always coincide with the interests of the clients of their bosses or other investors.
• Newsletter sellers want subscription fees. Getting the attention of potential subscribers is essential to their business model. They sometimes seek attention by uncritically presenting snooped, gross trading system results as an “educational” service.
• Financial advisors want advisory fees. They must attract the attention of potential clients. As with newsletter sellers, the font used for marketing copy is much larger than that used for the legal disclaimer.
• Investment managers, mutual fund managers and hedge fund managers want management fees, normally as a percentage of account balance. They have to get the account before they can debit the balance. They have to get the attention of a potential clients before they get the account.

A common motive across the range of investment service providers is attention-seeking, which tends to drive offerors toward extreme representations (possible but low-probability scenarios, the tails of the distribution of potential outcomes). The most extreme representations offer the “holy grail” of amazingly large and reliable returns (appealing to investor greed) or the “safety of Noah’s ark” from impending doom (appealing to investor fear).

Conflict-of-interest materiality persists because investors have great difficulty distinguishing luck from skill when outcomes involve a high degree of randomness. (more…)