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SACEMS Optimization in Depth

The Simple Asset Class ETF Momentum Strategy (SACEMS) each month picks the one, two or three of nine asset class proxies with the highest cumulative total returns over a specified lookback interval. A subscriber proposed instead using the optimal intrinsic (time series or absolute) momentum lookback interval for each asset rather than a common lookback interval for all assets. SACEMS and the proposed approach represent different beliefs (which could both be somewhat true), as follows:

  • Many investors adjust asset class allocations with some regularity, such that behaviors of classes are important and coordinated.
  • Many investors switch between specific asset classes and cash with some regularity, such that each class may exhibit distinct times series behavior. 

To investigate, we consider two ways to measure intrinsic momentum for each asset class proxy:

  1. Correlation between next-month return and average monthly return over the past one to 12 months. The lookback interval with the highest correlation has the strongest (linear) relationship between past and future returns and is optimal.
  2. Intrinsic momentum, measured as compound annual growth rate (CAGR) for a strategy that is in the asset (cash) when its total return over the past one to 12 months is positive (zero or negative). The lookback interval with the highest CAGR is optimal.

We use the two sets of optimal lookback intervals (optimization-in-depth) to calculate momentum for each asset class proxy as its average monthly return over its optimal lookback interval. We then compare performance statistics for these two alternatives to those for base SACEMS, focusing on: gross CAGR for several intervals; average gross annual return; standard deviation of annual returns; gross annual Sharpe ratio; and, gross maximum drawdown (MaxDD). Using monthly dividend-adjusted prices for SACEMS asset class proxies during February 2006 through September 2019, we find that:

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Combine Market Trend and Economic Trend Signals?

A subscriber requested review of an analysis concluding that combining economic trend and market trend signals enhances market timing performance. Specifically, per the example in the referenced analysis, we look at combining:

  • The 10-month simple moving average (SMA10) for the broad U.S. stock market. The trend is positive (negative) when the market is above (below) its SMA10.
  • The 12-month simple moving average (SMA12) for the U.S. unemployment rate (UR). The trend is positive (negative) when UR is below (above) its SMA12.

We consider scenarios when the stock market trend is positive, the UR trend is positive, either trend is positive or both trends are positive. We consider two samples: (1) dividend-adjusted SPDR S&P 500 (SPY) since inception at the end of January 1993 (nearly 26 years); and, (2) the S&P 500 Index (SP500) since January 1948 (limited by UR availability), adjusted monthly by estimated dividends from the Shiller dataset, for longer-term robustness tests (nearly 71 years). Per the referenced analysis, we use the seasonally adjusted civilian UR, which comes ultimately from the Bureau of Labor Statistics (BLS). BLS generally releases UR monthly within a few days after the end of the measured month. We make the simplifying assumptions that UR for a given month is available for SMA12 calculation and signal execution at the market close for that same month. When not in the stock market, we assume return on cash from the broker is the yield on 3-month U.S. Treasury bills (T-bill). We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance metrics. We use the average monthly T-bill yield during a year as the risk-free rate for that year in Sharpe ratio calculations. While we do not apply any stocks-cash switching frictions or tax considerations, we do calculate the number of switches for each scenario. Using specified monthly data through September 2019, we find that: Keep Reading

Weekly Summary of Research Findings: 10/28/19 – 11/1/19

Below is a weekly summary of our research findings for 10/28/19 through 11/1/19. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Jim Cramer Using the S&P Oscillator

A reader asked about the usefulness of the S&P Short-range Oscillator as sometimes used by Jim Cramer to forecast U.S. stock market returns. The self-reported “Performance” of the oscillator, relying on in-sample visual inspection with snooped thresholds, is of small use. Since continuous historical values of the indicator are not publicly available, we conduct an out-of-sample test by:

  1. Searching CNBC.com for “Oscillator” “Mad Money” and just “Oscillator” on October 3, 2019 and identifying articles with U.S. stock market forecasts from Jim Cramer based on the S&P Short-range Oscillator.
  2. Extracting the date for each forecast and determining whether it is call to be “In” or “Out” of the market.
  3. Calculating for each call a cumulative S&P 500 Index return starting at the next open after the article date (generally timestamped after the market close) for 21 trading days.
  4. Computing average cumulative performances of “In” and “Out” calls.
  5. Comparing these averages to that for all days spanning the search results.

Using the 15 qualifying articles and daily opening levels of the S&P 500 Index during June 16, 2008 through October 31, 2019, we find that: Keep Reading

SACEVS with Quarterly Allocation Updates

Do quarterly allocation updates for the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS) work as well as monthly updates? These strategies allocate funds to the following asset class exchange-traded funds (ETF) according to valuations of term, credit and equity risk premiums, or to cash if no premiums are undervalued:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

Changing from monthly to quarterly allocation updates does not sacrifice information about lagged quarterly S&P 500 Index earnings, but it does sacrifice currency of term and credit premiums. To assess alternatives, we compare cumulative performances and the following key metrics for quarterly and monthly allocation updates: gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD), annual gross returns and volatilities and annual gross Sharpe ratios. Using monthly dividend-adjusted closes for the above ETFs during September 2002 (earliest alignment of months and quarters) through September 2019, we find that:

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Stock Market Performance Perspectives

How different are stock market performance metrics for:

  • Capital gains only, capital gains plus dividends accrued as cash (spent or saved), and capital gains plus dividends reinvested in the stock market?
  • Nominal versus real returns?
  • Simple return-to-risk calculations versus Sharpe ratio?

Using quarterly S&P 500 Index levels and dividends, quarterly U.S. Consumer Price Index (CPI) data (all items) and monthly 3-month U.S. Treasury bill (T-bill) yield as the risk-free rate/return on cash during the first quarter of 1988 through the second quarter of 2019, we find that: Keep Reading

Stock Index Earnings-returns Lead-lag

A subscriber asked about the lead-lag relationship between S&P 500 earnings and S&P 500 Index returns. To investigate, we relate actual aggregate S&P 500 operating and as-reported earnings to S&P 500 Index returns at both quarterly and annual frequencies. Earnings forecasts are available well in advance of returns. Actual earnings releases for a quarter occur throughout the next quarter. Using quarterly S&P 500 earnings and index levels during March 1988 through June 2019 and September 2019, respectively, we find that: Keep Reading

Economic Policy Uncertainty and the Stock Market

Does quantified uncertainty in government economic policy reliably predict stock market returns? To investigate, we consider the U.S. Economic Policy Uncertainty (EPU) Index, created by Scott Baker, Nicholas Bloom and Steven Davis and constructed from three components:

  1. Coverage of policy-related economic uncertainty by prominent newspapers.
  2. Number of temporary federal tax code provisions set to expire in future years.
  3. Level of disagreement in one-year forecasts among participants in the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters for both (a) the consumer price index (CPI) and (b) purchasing of goods and services by federal, state and local governments.

They normalize each component by its own standard deviation prior to 2012 and then compute a weighted average of components, assigning a weight of one half to news coverage and one sixth each to tax code uncertainty, CPI forecast disagreement and government purchasing forecast disagreement. They update the index monthly at the beginning of the following month, potentially revising recent months. Using monthly levels of the EPU Index and the S&P 500 Index during January 1985 through September 2019, we find that: Keep Reading

Weekly Summary of Research Findings: 10/21/19 – 10/25/19

Below is a weekly summary of our research findings for 10/21/19 through 10/25/19. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

The Decision Moose Asset Allocation Framework

A reader requested review of the Decision Moose asset allocation framework. Decision Moose is “an automated stock, bond, and gold momentum model developed in 1989. Index Moose uses technical analysis and exchange traded index funds (ETFs) to track global investment flows in the Americas, Europe and Asia, and to generate a market timing signal.” The trading system allocates 100% of funds to the index projected to perform best. The site includes a history of switch recommendations since the end of August 1996, with gross performance. To evaluate Decision Moose, we assume that switches and associated trading returns are as described (out of sample, not backtested) and compare the returns to those for dividend-adjusted SPDR S&P 500 (SPY) over the same intervals. Using Decision Moose signals/performance data and contemporaneous SPY prices during 8/30/96 through 9/30/19 (23+ years), we find that: Keep Reading

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