Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for March 2024 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for March 2024 (Final)
1st ETF 2nd ETF 3rd ETF

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.

SACEMS, SACEVS and Trading Calendar Updates

We have updated monthly allocations and performance data for the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS). We have also updated performance data for the Combined Value-Momentum Strategy.

We have updated the Trading Calendar to incorporate data for February 2024.

Preliminary SACEMS and SACEVS Allocation Updates

The home page, Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) now show preliminary positions for March 2024. SACEMS rankings are unlikely to change by the close. SACEVS allocations are unlikely to change by the close.

Substitute QQQ for SPY in SACEVS and SACEMS?

Subscribers asked whether substituting Invesco QQQ Trust (QQQ) for SPDR S&P 500 (SPY) in the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS) improves outcomes. To investigate, we substitute monthly QQQ dividend-adjusted returns for SPY dividend-adjusted returns in the two model strategies. We then compare the modified performance with the original baseline performance, including: gross compound annual growth rates (CAGR) at various horizons, average gross annual returns, standard deviations of gross annual returns, gross annual Sharpe ratios and maximum drawdowns (MaxDD) based on monthly measurements. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using the specified methodology and data to generate SACEVS monthly returns starting August 2002 and SACEMS monthly returns starting July 2006, all through January 2024, we find that:

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SACEMS with Inverse VIX-based Lookback Intervals

One concern about simple momentum strategies is data snooping bias impounded in selection of the lookback interval(s) used to measure asset momentum. To circumvent this concern, we consider the following argument:

  • The CBOE Volatility Index (VIX) broadly indicates the level of financial markets distress and thereby the tendency of investors to act complacently (when VIX is low) or to act in panic (when VIX is high).
  • Complacency translates to resistance in changing market outlook (long memory and lookback intervals), while panic translates to rapid changes of mind (short memory and short lookback intervals).
  • The inverse of VIX is therefore indicative of the actual aggregate current lookback interval affecting investor actions.

We test this argument by:

  • Setting a range for VIX using monthly historical closes from January 1990 through July 2002, before the sample period used for any tests of the Simple Asset Class ETF Momentum Strategy (SACEMS).
  • Applying buffer factors to the bottom and top of this actual inverse VIX range to recognize that it could break above or below the historical range in the future.
  • Segmenting the buffer-extended inverse VIX range into 12 equal increments and mapping these increments by rounding into momentum lookback intervals of 1 month (lowest segment) to 12 months (highest segment).
  • Applying this same method to future end-of-month inverse VIX levels to select the SACEMS lookback interval for the next month.

We test the top one (Top 1), the equally weighted top two (EW Top 2) and the equally weighted top three (EW Top 3) SACEMS portfolios. We focus on compound annual growth rate (CAGR), maximum drawdown based on monthly measurements, annual returns and Sharpe ratio as key performance statistics. To calculate excess annual returns for the Sharpe ratio, we use average monthly yield on 3-month Treasury bills during a year as the risk-free rate for that year. Benchmarks are these same statistics for tracked SACEMS. Using monthly levels of VIX since inception in January 1990 and monthly dividend-adjusted prices of SACEMS assets since February 2006 (initial availability of a commodities ETF), all through January 2024, we find that: Keep Reading

SACEVS and SACEMS Strategy Momentum?

A subscriber suggested that the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS) may each exhibit return momentum at the strategy level, such that an investor holding both as in Combined Value-Momentum Strategy (SACEVS-SACEMS) may want to tilt each month toward the one with stronger recent returns. To investigate, we test a SACEVS Best Value-SACEMS Equal-Weighted (EW) Top 2 combination strategy that each month assigns 60% weight to the strategy with the higher return over a specified lookback interval and 40% to the one with the lower return (60-40). We consider lookback intervals of 1 to 12 months. We also look at a “full tilt” version for a selected lookback interval. We use standalone SACEVS Best Value, standalone SACEMS EW Top 2 and monthly rebalanced 50% SACEVS Best Value-50% SACEMS EW Top 2 (50-50) as benchmarks. We look at average gross monthly return, standard deviation of monthly returns, monthly gross reward/risk (average monthly return divided by standard deviation), gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio as key performance metrics. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using SACEVS Best Value and SACEMS EW Top 2 gross monthly returns during July 2006 (limited by SACEMS) through January 2024, we find that:

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More International Equity Market Granularity for SACEMS?

A subscriber asked whether more granularity in international equity choices for the “Simple Asset Class ETF Momentum Strategy” (SACEMS), such as considered by Decision Moose, would improve performance. To investigate, we augment/replace international developed and emerging equity market exchange-traded funds (ETF) such that the universe of assets becomes:

  • SPDR S&P 500 (SPY)
  • iShares Russell 2000 Index (IWM)
  • iShares Europe (IEV)
  • iShares MSCI Japan (EWJ)
  • iShares MSCI Pacific ex Japan (EPP)
  • iShares MSCI Emerging Markets Index (EEM)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
  • iShares Latin America 40 (ILF)
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • Vanguard REIT ETF (VNQ)
  • SPDR Gold Shares (GLD)
  • Invesco DB Commodity Index Tracking (DBC)
  • 3-month Treasury bills (Cash)

We compare original (SACEMS Base) and modified (SACEMS Granular), each month picking winners from their respective sets of ETFs based on total returns over a fixed lookback interval. We focus on gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD) and gross annual Sharpe ratio (average annual excess return divided by standard deviation of annual excess returns, using average monthly T-bill yield during a year to calculate excess returns) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns for the specified assets during February 2006 through December 2023, we find that: Keep Reading

Testing the All Weather Portfolio

A subscriber requested a test of Ray Dalio‘s All Weather (AW) portfolio with different rebalancing frequencies, allocated to exchange-traded funds (ETF) as asset class proxies as follows:

30% – Vanguard Total Stock Market (VTI)
40% – iShares 20+ Year Treasury (TLT)
15% – iShares 7-10 Year Treasury (IEF)
7.5% – SPDR Gold Shares (GLD)
7.5% – Invesco DB Commodity Tracking (DBC)

To investigate, we test:

We consider the following gross performance metrics, all based on monthly measurements: average monthly return, standard deviation of monthly returns, compound annual growth rate (CAGR), maximum drawdown (MaxDD) and Sharpe ratio (with the 3-month Treasury bill yield as the risk-free rate). We also compare number of rebalance actions for each portfolio. Using monthly dividend-adjusted returns for the specified assets during February 2006 (limited by DBC) through December 2023, we find that: Keep Reading

Optimal SACEMS Lookback Interval Update

How sensitive is performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) to choice of momentum calculation lookback interval, and what interval works best? To investigate, we generate gross compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for SACEMS Top 1, equally weighted (EW) EW Top 2 and EW Top 3 portfolios over lookback intervals ranging from one to 12 months. All calculations start at the end of February 2007 based on inception of the commodities exchange-traded fund and the longest lookback interval. Using end-of-month total (dividend-adjusted) returns for the SACEMS asset universe during February 2006 through November 2023, we find that: Keep Reading

A Few Notes on The Missing Billionaires

In their 2023 book, The Missing Billionaires: A Guide to Better Financial Decisions, authors Victor Haghani and James White seek “to give you a practical framework, consistent with the consensus of university finance textbooks, for making good financial decisions that are right for you. Good decisions will take account of your personal circumstances, financial preferences, and your considered views on the risks and expected returns of available investments. …You will likely get the most out of this book if you have already accumulated a decent amount of financial capital or if you are young with a healthy measure of human capital. …The book is written from the perspective of a US individual or family…” Based on their many years of wealth management experience and portfolio systems development, they conclude that:

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Review of the Quantified Market Psychology Strategy

At the suggestion of one of his subscribers, Willi Bambach requested independent review of his 1g QMP [Quantified Market Psychology] strategy, tracked since December 2007 on TimerTrac. To facilitate a review, he provided a brief description of the strategy and a medallion (https://timertrac.com/private/medallion.asp?mlid={CDD4AEE6-2A1D-4917-A571-DF23C884D1D3}) to enable public access to the strategy on TimerTrac (very slow to load and may no longer work). The strategy has asset universe, asset allocation and position leverage components as follows:

  • Asset universe:
    1. Cash in a money market fund (with assumed 2% fixed yield).
    2. SPDR S&P 500 ETF Trust (SPY)
    3. iShares 20+ Year Treasury Bond ETF (TLT).
  • Allocations as signaled mostly per the following three steps:
    1. Examine differences between FactSet consensus analyst earnings forecasts and actual earnings for S&P 500 stocks.
    2. Relate these differences to earnings release price reactions of respective stocks.
    3. Translate this relationship into a sentiment signal that specifies allocations for Cash, SPY and TLT.
  • Leverage (with assumed 0.5% fixed financing cost) for SPY and TLT positions added in 0.5 increments as long as three conditions hold for inception-to-date data (as the sample grew, this approach evolved to constant 2X leverage over the last five years):
    1. Standard deviation of 1g QMP returns is lower than than that for the S&P 500 Index.
    2. Downside standard deviation of 1g QMP returns is lower than that for the S&P 500 Index.
    3. 1g QMP Ulcer Index is lower than that for the S&P 500 Index.

Data available via this medallion include a list of 1g QMP allocation changes by date (see the table at the end). For testing 1g QMP, we do not attempt to replicate allocations. Instead, we apply a set of tractable assumptions to them and test versions of 1g QMP with 1X (no leverage) and 2X leverage. We use SPDR Bloomberg 1-3 Month T-Bill ETF (BIL) for cash to approximate money market yields and avoid estimating settlement delays. We supply 2X leverage by substituting ProShares Ultra S&P500 (SSO) for SPY and ProShares Ultra 20+ Year Treasury (UBT) for TLT. We focus on net average daily return, standard deviation of daily returns, daily return/risk (average divided by standard deviation), compound annual growth rate (CAGR), maximum drawdown and annual Sharpe ratio. We use average end-of-month 3-month U.S. Treasury bill (T-bill) yield during a year as the risk-free rate for that year in Sharpe ratio calculations. We do not include partial years in Sharpe ratio calculations. Using the list of strategy allocation changes and daily dividend-adjusted prices of BIL, SPY, TLT, SSO and UBT during 1/25/2008 through 11/30/2023, we find that:

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