# 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.

**December 28, 2017** - Momentum Investing

How efficiently can a sophisticated fund manager implement long-only stock momentum portfolios? In their December 2017 paper entitled “Implementing Momentum: What Have We Learned?”, Adrienne Ross, Tobias Moskowitz, Ronen Israel and Laura Serban use seven years of live data for long-only U.S. and international momentum funds to measure the import of implementation frictions. They segment these frictions into turnover/trading costs, tax impacts and mitigating portfolio construction choices. The underlying momentum strategies converge on the top third of stocks based on a combination of market capitalization and momentum signal strength (using multiple measures of momentum), reformed monthly. Portfolio construction employs a transaction cost model to minimize costs by: substituting stocks with similar momentum that are cheaper to trade, trading patiently and employing algorithmic trading rules designed to suppress price impacts of trades. Using detailed trade and performance data for the specified momentum funds during July 9, 2009 through December 31, 2016, *they find that:* Keep Reading

**December 14, 2017** - Equity Premium, Momentum Investing

Do investors underestimate the adverse import of large left tails for future stock returns? In their November 2017 paper entitled “Left-Tail Momentum: Limited Attention of Individual Investors and Expected Equity Returns”, Yigit Atilgan, Turan Bali, Ozgur Demirtas and Doruk Gunaydin investigate the relationship between left-tail risk and next-month returns for U.S. and international stocks. They measure left-tail risk at the end of each month via either of:

- Value-at-risk (VaR) – daily return of a stock at the first (VAR1) or fifth (VAR5) percentile of its returns over the past one year (250 trading days).
- Expected shortfall – average daily return of a stock for the bottom 1% (ES1) or bottom 5% (ES5) of its returns over the past year (250 trading days).

They then sort stocks into tenths (deciles) based on left-tail risk and examine variation in next-month average gross returns across deciles. Using daily prices and monthly firm characteristics and risk factors for U.S. stocks with month-end prices at least $5 during January 1962 through December 2014, *they find that:* Keep Reading

**December 8, 2017** - Momentum Investing, Strategic Allocation

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 ratio, compound 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

**November 27, 2017** - Momentum Investing, Strategic Allocation

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

**November 22, 2017** - Fundamental Valuation, Momentum Investing

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:

- Record the patent activity of each firm by patent class over the most recent three calendar years.
- Quantify similarity of this patent activity for each pair of firms.
- Segregate firms into innovation groups based on patent activity similarity (top fifth of quantified similarities).
- 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

**November 16, 2017** - Fundamental Valuation, Momentum Investing

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

**October 31, 2017** - Equity Premium, Momentum Investing, Strategic Allocation

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

**October 30, 2017** - Momentum Investing

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

**October 24, 2017** - Equity Premium, Momentum Investing, Value Premium, Volatility Effects

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:

- Integrated – each quarter, pick the 20% of stocks with the highest average standardized factor scores and weight by market capitalization.
- 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

**October 23, 2017** - Momentum Investing, Strategic Allocation

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