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Value Investing Strategy (Strategy Overview)

Allocations for March 2021 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for March 2021 (Final)
1st ETF 2nd ETF 3rd ETF

Momentum Investing

Do financial market prices reliably exhibit momentum? If so, why, and how can traders best exploit it? These blog entries relate to momentum investing/trading.

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 2021.

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 2021. The top three SACEMS ETFs are unlikely to change by the close. SACEVS allocations are unlikely to change by the close.

Simple Sector ETF Momentum Strategy Update/Extension

“Simple Sector ETF Momentum Strategy” investigates performances of simple momentum trading strategies for the following nine sector exchange-traded funds (ETF) executed with Standard & Poor’s Depository Receipts (SPDR):

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

Here, we update the principal strategy and extend it by adding equally weighted combinations of the top two and top three sector ETFs, along with corresponding robustness tests and benchmarks. We present findings in formats similar to those used for the Simple Asset Class ETF Momentum Strategy and the Simple Asset Class ETF Value Strategy. Using monthly dividend-adjusted closing prices for the sector ETFs and SPDR S&P 500 (SPY), 3-month U.S. Treasury bill (T-bill) yield and S&P 500 Index level during December 1998 through December 2020, we find that: Keep Reading

Recent Weaknesses of Factor Investing

How have value, quality, low-volatility and momentum equity factors, and combinations of these factors, performed in recent years. In their October 2020 paper entitled “Equity Factor Investing: Historical Perspective of Recent Performance”, Benoit Bellone, Thomas Heckel, François Soupé and Raul Leote de Carvalho review and put into context recent performances of these these factors/combinations as applied to medium-capitalization and large-capitalization World, U.S. and European stock universes. They consider both long-short and long-only factor portfolios and further investigate effects of (1) neutralizing beta and sector dependencies, (2) using multiple metrics for each factor and (3) including small stocks. Using firm accounting data and stock returns to support factor portfolio construction during 1995 through early 2020, they find that:

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SACEVS-SACEMS for Value-Momentum Diversification

Are the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) mutually diversifying. To check, based on feedback from subscribers about combinations of interest, we look at three equal-weighted (50-50) combinations of the two strategies, 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).

We consider 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. We also test sensitivity of results to deviating from equal SACEVS-SACEMS weights. Using monthly gross returns for SACEVS, SACEMS, SPY and T-bills during July 2006 through November 2020, we find that: Keep Reading

Stock Option Momentum and Seasonality

Do options of individual stocks exhibit momentum and seasonality patterns? In their November 2020 paper entitled “Momentum, Reversal, and Seasonality in Option Returns”, Christopher Jones, Mehdi Khorram and Haitao Mo investigate momentum and seasonality effects for options on U.S. common stocks. They focus on performance of straddles, combining a put and a call with the same strike price and expiration date. They balance needs for liquidity and sample size by requiring positive open interest during the holding period but not the momentum calculation interval. Specifically, on each monthly option expiration date, they:

  1. Form two straddles from near-the-money options expiring next month for each for each stock: (1) the pair with call delta closest to 0.5 for calculating momentum; and, (2) the pair with call delta closest to 0.5 and with positive open interest for both the put and the call when selected for calculating momentum portfolio return.
  2. Construct from these pairs zero-delta straddles using bid-ask midpoints as prices and calculate monthly straddle excess returns relative to the 1-month Treasury bill yield. This process generates about 1,600 straddles per month with average monthly excess return -5.6% and very large standard deviations.
  3. Calculate momentum as average monthly excess return over a specified lookback interval (rather than cumulative return, to suppress effects of return outliers).
  4. Rank straddle returns into equal-weighted fifths (quintiles) based on momentum and calculate average return for each quintile and for a portfolio that is long the top quintile and short the bottom quintile.

Using end-of-day open interest and bid-ask quotes for call and put options on U.S. common stocks from OptionMetric and trading data for underlying stocks during January 1996 through June 2019, they find that: Keep Reading

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)
  • PowerShares 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 daily and monthly total (dividend-adjusted) returns for the specified assets during February 2006 through September 2020, we find that: Keep Reading

Asset Class Momentum Faster During Bear Markets?

A subscriber asked whether the optimal momentum ranking (lookback) interval for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) shrinks during bear markets for U.S. stocks. To investigate, we compare SACEMS monthly performance statistics when the S&P 500 Index at the previous monthly close is above (bull market) or below (bear market) its 10-month simple moving average. We consider Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners for the baseline SACEMS lookback interval. We focus on monthly reward/risk (average monthly return divided by standard deviation of monthly returns) as a key performance metric. In a robustness test for the EW Top 3 portfolio, we consider lookback intervals ranging from one to 12 months. Using monthly total (dividend-adjusted) returns for SACEMS assets since February 2006 and monthly S&P 500 Index level since September 2005, all through September 2020, we find that:

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Sector Breadth as Market Return Indicator

Does breadth of equity sector performance predict overall stock market return? To investigate, we relate next-month stock market return to sector breadth (number of sectors with positive past returns) over lookback intervals ranging from 1 to 12 months. We consider the following nine sector exchange-traded funds (ETF) offered as Standard & Poor’s Depository Receipts (SPDR):

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

We use SPDR S&P 500 (SPY) to represent the overall stock market. Using monthly dividend-adjusted returns for SPY and the sector ETFs during December 1998 through September 2020, we find that: Keep Reading

Breaking Asset Ranking Systems into Pairs

Is there a better way to identify attractive and unattractive assets than simply ranking them? In the August 2020 version of their paper entitled “Decoding Systematic Relative Investing: A Pairs Approach”, Christian Goulding, Campbell Harvey and Alex Pickard examine a long-short strategy that periodically reforms a portfolio by evaluating all possible pairs within an asset universe based on:

  1. High positive signal-future return correlation for each asset on its own in a pair.
  2. Low (or negative) signal correlation between assets in the pair.
  3. Low (or negative) signal-future return correlations between one asset and the other in the pair.

They use these three inputs to calculate a (somewhat complex) composite score for each pair. Among pairs with the highest composite scores, the member with the higher (lower) signal goes to the long (short) side of the portfolio. They assess usefulness of the three conditions and the composite score using a momentum signal calculated as average past monthly return over a specified lookback interval minus its inception-to-date mean and divided by its inception-to-date standard deviation. They split their sample roughly in half and use the first half for detection of profitable pair strategies and the second half to measure out-of-sample performance. They further test an explicit tactical allocation strategy using a 12-month momentum lookback interval, a rolling 10-year monthly composite score and a scheme that weights the top four asset pairs according to respective composite scores. As a benchmark, they use a comparable conventional relative momentum strategy that simply ranks assets on momentum signal. Using monthly returns for 13 broad asset-class indexes encompassing equities, bonds, real estate investment trusts (REIT) and commodities (78 possible pairs) as available through May 2020, they find that:

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