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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.

Long-term Tests of Intrinsic Momentum Across Asset Classes

Does time series (intrinsic or absolute) momentum work across asset classes prior to the Great Moderation (secular decline in interest rates)? In their August 2016 paper entitled “Trend Following: Equity and Bond Crisis Alpha”, Carl Hamill, Sandy Rattray and Otto Van Hemert test several time series momentum portfolios as applied to groups of bonds, commodities, currencies and equity indexes as far back as 1960. They consider 10 developed country equity indexes, 11 developed country government bond series, 25 agricultural/energy/metal futures series and nine U.S. dollar currency exchange rate series. They calculate return momentum for each asset as the weighted sum of its past monthly returns (up to 11 months) divided by the normalized standard deviation of those monthly returns. They then divide each signal again by volatility and apply a gearing factor to specify a 10% annual volatility target for each holding. Within each of equity index, bond and currency groups, they weight components equally. Within commodities, they weight agriculture, energy and metal sectors equally after weighting individual commodities equally within each sector. They report strategy performance based on excess return, roughly equal to real (inflation-adjusted) return. They commence strategy performance analyses in 1960 to include an extreme bond bear market. Using monthly price series that dovetail futures/forwards from inception with preceding spot (cash) data as available starting as early as January 1950 and as late as April 1990, all through 2015, they find that: Keep Reading

Optimal Portfolio Sorting

Are the widely used stock characteristic/factor sorting practices of ranked fifth (quintile) or ranked tenth (decile) portfolios optimal in terms of interpretative power? In their August 2016 paper entitled “Characteristic-Sorted Portfolios: Estimation and Inference”, Matias Cattaneo, Richard Crump, Max Farrell and Ernst Schaumburg formalize the portfolio sorting process. Specifically, they describe how to choose the number of quantile portfolios best suited to source data via a trade-off between variability of outputs and effects of data abnormalities (such as outliers). They illustrate implications of the procedure for the:

  • Size effect – each month sorting stocks by market capitalization and measuring the difference in value-weighted average next-month returns between small stocks and large stocks.
  • Momentum effect – each month sorting stocks by cumulative return from 12 months ago to one month ago and measuring the difference in value-weighted average next-month returns between past winners and past losers.

Using monthly data for a broad sample of U.S. common stocks during January 1927 through December 2015, they find that: Keep Reading

Combining Asset Class Diversification, Value/Momentum and Crash Avoidance

How can investors integrate global asset class diversification, pre-eminent factor premiums and crash protection? In his July 2016 paper entitled “The Trinity Portfolio: A Long-Term Investing Framework Engineered for Simplicity, Safety, and Outperformance”, Mebane Faber summarizes a portfolio combining these three principles, as follows:

  1. Global diversification: Include U.S. stocks, non-U.S. developed markets stocks, emerging markets stocks, corporate bonds, 30-year U.S. Treasury bonds, 10-year foreign government bonds, U.S. Treasury Inflation-Protected Securities (TIPS), commodities, gold and Real Estate Investment Trusts (REIT) .
  2. Value/momentum screens: For U.S. stocks, each month first rank stocks by value and momentum metrics and then pick those with the highest average ranks. For non-U.S. stocks, each month pick the cheapest overall markets. For bonds, each month pick those with the highest yields.
  3. Trend following for crash avoidance: For each asset each month, hold the asset (cash) if its price is above (below) its 10-month SMA at the end of the prior month.

The featured “Trinity” portfolio allocates 50% to a sub-portfolio based on principles 1 and 2 and 50% to a sub-portfolio based on principles 1, 2 and 3. Using monthly returns for the specified asset classes during 1973 through 2015, he finds that: Keep Reading

Stock Momentum Effect Update

Is recent weakness in the stock return momentum anomaly, perhaps representing market adaptation to widespread anomaly exploitation, permanent or transitory? In their July 2016 paper entitled “Where Has the Trend Gone? An Update on Momentum Returns in the U.S. Stock Market”, Steven Dolvin and Bryan Foltice explore recent profitability of stock return momentum trading in the U.S. market. They each month rank stocks into tenths (deciles) based on cumulative return over the past six months and measure returns of equally weighted decile portfolios over the next 12 months (designating the strategy “6/12”), resulting in overlapping portfolios. To assess trends in momentum strategy performance, they examine average performance of decile portfolios during three subperiods: 1986-2006, 2007-2015 and 2010-2015. For robustness, they repeat some tests for 6/6, 3/6, and 3/12 ranking/holding intervals. Using monthly returns for a broad sample of U.S. stocks during July 1985 through December 2015, they find that: Keep Reading

Long-term Reversal for Stocks Everywhere?

Do global equity market behaviors support the hypothesis that intermediate-term momentum drives stock prices beyond fundamental values, thereafter driving long-term reversion? In their June 2016 paper entitled “Overreaction and the Cross-Section of Returns: International Evidence”, Douglas Blackburn and Nusret Cakici investigate whether long-term reversion is evident in global equity markets allocated to four regions: North America, Europe, Japan and Asia. They define long-term (LT) stock return as cumulative return over the last three years. They define momentum, or short-term (ST) stock return, as return from 12 months ago to one month ago. They measure LT and ST return effects in each region based on average monthly returns of hedge portfolios that are each month long (short) the equal-weighted or value-weighted ranked fifth (quintile) of stocks with highest (lowest) LT or ST returns. They designate these portfolios winners-minus-losers (WML). They examine a size effect by calculating hedge portfolio returns separately for big stocks (the largest stocks, comprising 90% of market capitalization) and small stocks (the rest) within each region. They test interactions of LT reversion with each of ST momentum, book-to-market ratio and size via hedge portfolios constructed from independent double sorts. Using monthly excess returns (in U.S. dollars relative to the U.S. Treasury bill yield) and characteristics for all stocks in the MSCI World Index, past and present, encompassing 23 developed equity markets during 1993 through 2014, they find that: Keep Reading

Integrating Momentum and Value Stock Exposures

What is the best way to combine styles (smart betas) in one portfolio? In their June 2016 paper entitled “Long-Only Style Investing: Don’t Just Mix, Integrate”, Shaun Fitzgibbons, Jacques Friedman, Lukasz Pomorski and Laura Serban compare two approaches to long-only combined equity style investing:

  1. Mixed portfolio – simply picks stocks from single-style portfolios.
  2. Integrated portfolio – first combines single-style rankings into an overall score for each stock and then builds a portfolio based on top overall scores.

They focus on combining momentum stocks (highest return from 12 months ago to one month ago) and value stocks (high book-to-market ratio). They first employ simulated data to illustrate differences in stock selection between the two approaches. They then compare net performances for equally weighted, monthly rebalanced mixed and integrated combinations of liquid global stocks. Using monthly data for large-capitalization stocks from developed markets (roughly the MSCI World Index components) during February 1993 through December 2015, they find that: Keep Reading

Factor Portfolio Valuation and Timing of Factor Premiums

Does timing of factor premiums work? In his June 2016 paper entitled “My Factor Philippic”, Clifford Asness addresses three critiques of the exploitability of stock factor premiums:

  1. Most factors are currently very overvalued (expected premiums are small), perhaps because of crowded bets on them.
  2. Factor portfolios may therefore crash.
  3. In fact, increasing factor valuations account for most of the historical premium (there are no essential premiums).

He considers five long-short factors: (1) value based on book-to-price ratio (B/P); (2) value based on sales-to-price ratio (S/P); (3) momentum (total return from 12 months ago to one month ago); (4) profitability (gross profits-to-assets); and, (5) betting-against-beta (long leveraged low-beta assets and short high-beta assets). He calculates each factor premium as average return to a capitalization-weighted portfolio that is each month long (short) the third of large-capitalization U.S. stocks with the best (worst) expected returns based on that factor. He estimates the time-varying valuation of a factor via a value spread, the ratio of the capitalization-weighted B/P (or S/P) of the long side of the factor portfolio to that of its short side. He tests a simple factor timing strategy that holds no position if the factor’s value spread is at its historical median and scales linearly up (down) to a 100% (-100%) position in the factor portfolio as the factor’s value spread increases to its 95th (decreases to its 5th) historical percentile. The initial look-back interval is 20 years (such that testing begins in 1988), expanding as more data become available. Using the specified factor premium data for January 1968 through January 2016, he finds that: Keep Reading

Implications of 52-Week Highs and Lows for Stock Returns

Is nearness to 52-week highs or lows informative about future stock returns? In their June 2016 paper entitled “Nearness to the 52-Week High and Low Prices, Past Returns, and Average Stock Returns”, Li-Wen Chen and Hsin-Yi Yu examine the power of extreme price levels (52-week highs and lows) to predict stock returns, and whether any such predictive power is distinct from the momentum effect. They focus on the left (right) tail of nearness to 52-week low (high), because these stocks may attract the most investor attention. They determine 52-week highs and lows with monthly data. Specifically, they each month form value-weighted portfolios that are:

  1. Long the bottom 10% and short the top 90% of stocks sorted on nearness to 52-week low.
  2. Long the top 10% and short the bottom 90% of stocks sorted on nearness to 52-week high.
  3. For comparison, long the top 10% and short bottom 10% based on returns from 12 months ago to one month ago (momentum strategy).

Using monthly prices (ignoring dividends) for a broad sample of non-financial common U.S. stocks and monthly factor portfolio returns during July 1962 through December 2014, they find that: Keep Reading

Turn-of-the-Year Effects on Country Stock Market Value and Momentum

Does the January (turn-of-the-year) stock return anomaly affect value and momentum strategies applied at the country stock market level? In his June 2015 paper entitled “The January Seasonality and the Performance of Country-Level Value and Momentum Strategies”, Adam Zaremba investigates this question using four value and two momentum firm/stock metrics. The four value metrics, each measured over four prior quarters with a one-quarter lag and weighted by company according to the methodology of the associated stock index, are:

  1. Earnings-to-price ratio (EP).
  2. Earnings before interest, taxes, depreciation and amortization (EBITDA)-to-enterprise value (EV) ratio (EBEV).
  3. EBITDA-to-price ratio (EBP).
  4. Sales-to-EV ratio (SEV).

The two momentum metrics are:

  1. Stock index return from 12 months ago to one month ago (LtMom).
  2. Stock index return from 12 months ago to six months ago (IntMom).

He assesses strategy performance via returns in U.S. dollars in excess of one-month U.S. Treasury bill yield from hedge portfolios that are each month long (short) the equally weighted fifth of country stock indexes with the highest (lowest) expected returns based on each metric. He first reviews performances for all months and then focuses on turn-of-the-year (December and January) performances. Using monthly data for 78 existing and discontinued country stock market indexes during June 1995 through May 2015, he finds that: Keep Reading

Exploiting Multiple Stock Factors for Stock Selection

How good can factor investing get? In his May 2016 paper entitled “Quantitative Style Investing”, Mike Dickson examines strategies that:

  1. Aggregate return forecasting power of four or six theoretically-motivated stock factors (or characteristics) via monthly multivariate regressions.
  2. Use inception-to-date simple averages of regression coefficients, starting after the first 60 months and updating annually, to suppress estimation and sampling error.
  3. Create equally weighted portfolios that are long (short) the 50%, 20%, 10%, 4%, 2% or 1% of stocks with the highest (lowest) expected returns.

The six stock characteristics are: (1) market capitalization; (2), book-to-market ratio; (3) gross profit-to-asset ratio; (4) investment (annual total asset growth); (5) last-month return; and, (6) momentum (return from 12 months ago to two months ago). He considers strategies employing all six characteristics (Model 1) or just the first four, slow-moving ones (Model 2). He considers samples with or without microcaps (capitalizations less than the 20% percentile for NYSE stocks). He estimates trading frictions as 1% of the value traded each month in rebalancing to equal weight. Using monthly data for a broad sample of U.S. common stocks during July 1963 through December 2013 (with evaluated returns commencing July 1968), he finds that: Keep Reading

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