# 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.

**January 31, 2018** - Momentum Investing

How, in a nutshell, do momentum investing strategies work? In his December 2017 paper entitled “Keep Up the Momentum”, Thierry Roncalli summarizes the nature of the momentum premium in a less mathematical way than in the previously available “Understanding the Momentum Risk Premium: An In-Depth Journey Through Trend-Following Strategies”. He distinguishes between:

- Time-series or trend-following or intrinsic or absolute momentum (long assets with a positive past trend and short assets with a negative past trend).
- Cross-sectional or relative or winners-minus-losers momentum (long assets that have outperformed and short assets that have underperformed relative to each other).

Based on mathematical derivations and prior research, *he concludes that:* Keep Reading

**January 16, 2018** - Momentum Investing, Strategic Allocation

Subscribers have suggested an alternative approach for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) designed to suppress trading by holding past winners until they fall further in the rankings than in the baseline specification. 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)

There are three versions of SACEMS: (1) top one of the nine ETFs (Top 1); (2) equally weighted top two (EW Top 2); and, (3) equally weighted top three (EW Top 3). To test the suggestion, we specify three “sticky” versions of SACEMS as follows:

- Top 1 Sticky – retains the past winner until it drops out of the top 2.
- EW Top 2 Sticky – retains past winners until they drop out of the top 3.
- EW Top 3 Sticky – retains past winners until they drop out of the top 4.

We compare sticky and baseline strategies using the tabular performance statistics used for the baseline. Using monthly total (dividend-adjusted) returns for the specified assets during February 2006 (limited by DBC) through December 2017, *we find that:*

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**January 11, 2018** - Equity Premium, Momentum Investing

“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. 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) and 3-month U.S. Treasury bill (T-bill) yields since December 1998, and S&P 500 Index levels since September 1998, all through December 2017, *we find that:* Keep Reading

**January 9, 2018** - Big Ideas, Momentum Investing, Size Effect, Value Premium, Volatility Effects

What is the best way to think about reliabilities and risks of various anomaly premiums commonly that investors believe to be available for exploitation? In their December 2017 paper entitled “A Framework for Risk Premia Investing”, Kari Vatanen and Antti Suhonen present a framework for categorizing widely accepted anomaly premiums to facilitate construction of balanced investment strategies. They first categorize each premium as fundamental, behavioral or structural based on its robustness as indicated by clarity, economic rationale and capacity. They then designate each premium in each category as either defensive or offensive depending on whether it is feasible as long-only or requires short-selling and leverage, and on its return skewness and tail risk. Based on expected robustness and riskiness of selected premiums as described in the body of research, *they conclude that:* Keep Reading

**January 8, 2018** - Fundamental Valuation, Momentum Investing, Strategic Allocation

“SACEVS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Value Strategy” (SACEVS). “SACEMS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS). In response, a subscriber requested a sensitivity test of 1.25X, 1.50X and 1.75X leverage targets. To investigate effects of these leverage targets, we separately augment SACEVS Best Value, SACEMS EW Top 3 and the equally weighted combination of these two strategies by: (1) initially applying target leverage via margin; (2) for each month with a positive portfolio return, adding margin at the end of the month to restore target 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 target 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. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate. Using monthly total (dividend-adjusted) returns for the specified assets since July 2002 for SACEVS and since July 2006 for SACEMS, both through December 2017, *we find that:*

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**January 4, 2018** - Commodity Futures, Momentum Investing, Volatility Effects

What is the best way to implement futures momentum and manage its risk? In their November 2017 paper entitled “Risk Adjusted Momentum Strategies: A Comparison between Constant and Dynamic Volatility Scaling Approaches”, Minyou Fan, Youwei Li and Jiadong Liu compare performances of five futures momentum strategies and two benchmarks:

- Cross-sectional, or relative, momentum (XSMOM) – each month long (short) the equally weighted tenth of futures contract series with the highest (lowest) returns over the past six months.
- XSMOM with constant volatility scaling (CVS) – each month scales the XSMOM portfolio by the ratio of a 12% target volatility to annualized realized standard deviation of daily XSMOM portfolio returns over the past six months.
- XSMOM with dynamic volatility scaling (DVS) – each month scales the XSMOM portfolio by the the ratio of next-month expected market return (a function of realized portfolio volatility and whether MSCI return over the last 24 months is positive or negative) to realized variance of XSMOM portfolio daily returns over the past six months.
- Time-series, or intrinsic, momentum (TSMOM) – each month long (short) the equally weighted futures contract series with positive (negative) returns over the past six months.
- TSMOM with time-varying volatility scaling (TSMOM Scaled) – each month scales the TSMOM portfolio by the ratio of 22.6% (the volatility of an equally weighted portfolio of all future series) to annualized exponentially weighted variance of TSMOM returns over the past six months.
- Equally weighted, monthly rebalanced portfolio of all futures contract series (Buy-and-Hold).
- Buy-and-Hold with time-varying volatility scaling (Buy-and-Hold Scaled) – each month scales the Buy-and-Hold portfolio as for TSMOM Scaled.

They test these strategies on a multi-class universe of 55 global liquid futures contract series, starting when at least 45 series are available in November 1991. They focus on average annualized gross return, annualized volatility, annualized gross Sharpe ratio, cumulative return and maximum (peak-to-trough) drawdown (MaxDD) as comparison metrics. Using monthly prices for the 55 futures contract series (24 commodities, 13 government bonds, 9 currencies and 9 equity indexes) during June 1986 through May 2017, *they find that:*

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**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 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:*

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