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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.

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SACEMS with Margin

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 ratiocompound 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

Effects of Execution Delay on SACEMS

“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

SACEMS vs. Luck

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:

Keep Reading

Firm Innovation Group Performance Persistence

Do firms that acquire patents in similar technologies persistently perform similarly? In the October 2017 draft of their paper entitled “Technology and Return Predictability”, Jiaping Qiu, Jin Wang and Yi Zhou examine monthly performance persistence of stocks grouped by similarity in recent firm patent activity. Specifically, they:

  1. Record the patent activity of each firm by patent class over the most recent three calendar years.
  2. Quantify similarity of this patent activity for each pair of firms.
  3. Segregate firms into innovation groups based on patent activity similarity (top fifth of quantified similarities).
  4. For each month during the next calendar year:
    • Rank stocks into fifths (quintiles) based on average prior-month, similarity-weighted return of their respective groups.
    • Form a hedge portfolio that is long (short) the equal-weighted or value-weighted stocks in the highest (lowest) return quintile.

They focus on gross average monthly return and stock return factor model alphas of the hedge portfolio as evidence of firm innovation group performance persistence. Using firm patent information by technology class during 1968 through 2010, and monthly stock data, quarterly institutional holdings and analyst coverage for a broad sample of U.S. stocks priced greater than $1 during 1968 through 2011, they find that:

Keep Reading

Earnings Acceleration as Stock Return Predictor

Do strongly accelerating firm earnings identify future outperforming stocks? In the October 2017 revision of their paper entitled “Earnings Acceleration and Stock Returns”, Shuoyuan He and Ganapathi Narayanamoorthy investigate the power of earnings acceleration (quarter-over-quarter change in earnings growth, which is year-over-year change in quarterly earnings) to predict abnormal stock returns. They test a hedged trading strategy that long (short) the equal-weighted tenth, or decile, of stocks with the highest (lowest) earnings acceleration for two holding intervals: (1) starting two days after earnings announcement and ending on day 30; and, (2) starting two days after earnings announcement and ending one day after the next quarterly earnings announcement. They allocate new earnings accelerations to deciles based on the prior-quarter distribution of values of earnings acceleration. They define abnormal return as that in excess of the capitalization-weighted market return. Using quarterly firm characteristics and earnings data and daily returns for a broad sample of U.S. stocks, excluding financial and utility stocks, during January 1972 through December 2015, they find that: Keep Reading

Simple Asset Class Leveraged ETF Momentum Strategy

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

Asset Class Momentum Faster During Bear Markets?

A subscriber asked whether the optimal momentum measurement (lookback) interval for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) shrinks during bear markets for U.S. stocks. 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)

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. 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 the specified assets since February 2006 (limited by DBC) and the monthly level of the S&P 500 Index since October 2005, all through September 2017, we find that:

Keep Reading

Momentum Risk Premium Theory

What makes momentum investing tick? In their September 2017 paper entitled “Understanding the Momentum Risk Premium: An In-Depth Journey Through Trend-Following Strategies”, Paul Jusselin, Edmond Lezmi, Hassan Malongo, Côme Masselin, Thierry Roncalli and Tung-Lam Dao present a theoretical analysis of the momentum risk premium. They assume that asset prices generally exhibit geometric Brownian motion (randomness) with constant volatility, but with a time-varying trend. They examine momentum strategy performance based on this model and test some conclusions empirically on a multi-class set of asset indexes. Based on mathematical derivations and using monthly returns for a universe of four equity, four government bond, three interest rate, five currency and four commodity indexes during January 2000 through July 2017, they find that: Keep Reading

How Best to Diversify Smart Betas

Is it better to build equity multifactor portfolios by holding distinct single-factor sub-portfolios, or by picking only stocks that satisfy multiple factor criteria? In their September 2017 paper entitled “Smart Beta Multi-Factor Construction Methodology: Mixing vs. Integrating”, Tzee-man Chow, Feifei Li and Yoseop Shim compare long-only multifactor portfolios constructed in two ways:

  1. Integrated – each quarter, pick the 20% of stocks with the highest average standardized factor scores and weight by market capitalization.
  2. Mixed – each quarter, hold an equal-weighted combination of single-factor portfolios, each comprised of the capitalization-weighted 20% of stocks with the highest expected returns for that factor. 

They consider five factors: value (book-to-market ratio), momentum (return from 12 months ago to one month ago), operating profitability, investment (asset growth) and low-beta. They reform factor portfolios annually for all except momentum and low-beta, which they reform quarterly. Using firm data required for factor calculations and associated stock returns for a broad sample of U.S. stocks during June 1968 through December 2016, they find that: Keep Reading

SACEMS with Three Copies of Cash

Subscribers have expressed concern about the “Simple Asset Class ETF Momentum Strategy” (SACEMS) selecting assets with negative past returns. Inclusion of Cash as one of the assets in the SACEMS universe of exchange-traded funds (ETF) already prevents the SACEMS Top 1 portfolio from holding an asset with negative past returns. To test full dual momentum versions of SACEMS equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios, we add two more copies of Cash to the universe, thereby preventing both of them from holding assets with negative past returns. The SACEMS universe thus becomes:

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)
3-month Treasury bills (Cash)
3-month Treasury bills (Cash)

We focus on the effects of adding two copies of Cash on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) of SACEMS EW Top 2 and EW Top 3 portfolios. Using monthly dividend adjusted closing prices for the asset class proxies and the yield for Cash over the period February 2006 (the earliest all ETFs are available) through September 2017 (140 months), we find that: Keep Reading

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