Momentum Investing

Do stock indexes behave predictably after extreme price levels, such as 52-week highs and 52-week lows? To investigate, we consider the behaviors of the Dow Jones Industrial Average (DJIA), the S&P 500 Index and the NASDAQ Composite Index over the 13 weeks after 52-week highs and lows during their available histories. Using weekly levels of these indexes from October 1928, January 1950 and February 1971, respectively, through January 2013, we find that: Keep Reading

Does purifying stock returns (by using only the parts of returns unexplained by the Fama-French market, size and value factors) improve momentum strategy performance? Does avoiding extreme losers that may sharply reverse further enhance performance? In their November 2012 paper entitled “Some Simple Tricks to Boost Price Momentum Performance”, Andrew Lapthorne, Rui Antunes, John Carson, Georgios Oikonomou, Charles Malafosse and Michael Suen investigate the effects on stock momentum strategy performance of:

• Ranking stocks on cumulative lagged residual (idiosyncratic) rather than raw total return, with residual return calculated monthly as that unexplained by one-factor (market) or three-factor (plus size and book-to-market ratio) models based on 36-month lagged rolling regressions, and alternatively adjusting residual returns for each stock by dividing by their volatilities.
• Avoiding distressed stocks that may be about to recover sharply, with distress measured as the percentage by which a stock’s current price is below its rolling lagged 12-month high.

They define momentum strategy performance as the return on a portfolio that is each month long (short) the tenth of stocks with the highest (lowest) cumulative residual returns over the past 12 months, with a skip-month between ranking interval and portfolio formation month. Using total returns in U.S. dollars and other data for FTSE World Index stocks, and contemporaneous regional Fama-French model factors, during June 1993 through September 2012, they find that: Keep Reading

Does combining different portfolio performance enhancement concepts actually improve outcome? In their December 2012 paper entitled “Generalized Momentum and Flexible Asset Allocation (FAA): An Heuristic Approach”, Wouter Keller and Hugo van Putten investigate the effects of combining momentum, volatility and correlation selection criteria to form an equally weighted portfolio of the three best funds from a set of mutual fund proxies for seven asset classes, as follows:

1. To follow trend, rank funds from highest to lowest lagged total return (relative momentum).
2. To suppress volatility, rank funds from lowest to highest volatility (standard deviation of daily returns).
3. To enhance diversification, rank funds from lowest to highest average pairwise correlation of daily returns.
4. To avoid drawdown, replace with cash any selected fund that has a negative lagged return (intrinsic or absolute momentum). 

Their seven asset class proxies are index mutual funds for U.S. stocks (VTSMX), developed market stocks outside the U.S. and Canada (FDIVX), emerging market stocks (VEIEX), mid-term U.S. Treasuries (VBMFX), short-term U.S. Treasuries (VFISX), commodities (QRAAX) and real estate (VGSIX). They use a default lagged measurement interval of four months for all four selection criteria. Their method of combining rankings for relative momentum, volatility and correlation is simple weighted average (with default weightings of 1, 0.5 and 0.5, respectively). They assume momentum calculations occur at the end of each month, with portfolio changes at the beginning of the next month. Using daily closing prices in U.S. dollars for the seven mutual funds from mid-1997 through mid-December 2012, they find that: Keep Reading

What causes asset price momentum? In his May 2012 paper entitled “Is Momentum a Self-fulfilling Prophecy?”, Steven Jordan presents a simple, abstract model explaining the pervasiveness and robustness of evidence for intermediate-term momentum and long-term reversal. The essential assumptions of his model are: (1) demand for an asset is noisy and flat or downward sloping with price; (2) supply of an asset is noisy and flat or upward sloping with price; and, (3) some traders believe that lagged price trends tend to persist and act on this belief, with their actions scaled by the magnitude of lagged noise. He assumes that demand and supply slopes are linear to simplify formulas. Deriving time series behaviors from this model, he concludes that: Keep Reading

Andrew Abraham, founder of Abraham Investment Management, introduces his 2012 book, The Trend Following Bible: How Professional Traders Compound Wealth and Manage Risk, by stating: “I want to teach you to think like a successful trend follower. I am giving you exactly the methodologies I have used on a daily basis for the last 18 years. They are not any magical holy grail; rather, they are robust ideas that give you the ability to make low-risk trades and try to catch trends when they are present.” Using examples based on his trading experience and the results for other trend followers, he concludes that: Keep Reading

When should an investor favor picking individual stocks over holding a stock index fund? In their November 2012 paper entitled “On Diversification”, Ben Jacobsen and Frans de Roon derive from Modern Portfolio Theory simple rules to compare concentrated investment in a portfolio of one or a few stocks to a broad, diversified (value-weighted) benchmark portfolio. The essential rule is that a concentrated portfolio is preferable to the benchmark portfolio if the product of its expected Sharpe ratio and the expected correlation of its returns with the benchmark’s returns exceeds the expected Sharpe ratio of the benchmark. They apply derivative thumb rules to real stocks to determine conditions under which stock picking is preferable to buying and holding a diversified benchmark portfolio. Using theoretical derivations and monthly returns and fundamentals for the 500 largest non-financial companies as of the end of the sample period with a history of at least five years during 1926 through 2011, they find that: Keep Reading

A subscriber asked whether limiting choices in the Simple Asset Class ETF Momentum Strategy (SACEMS) to IWB, IWM, RWR, EFA and EEM (TLT, GLD, DBC and Cash) when above (below) the 200-day simple moving average improves model performance. To investigate, we assume the simple moving average (SMA) is for the S&P 500 Index as proxy for the equity market and use a 10-month rather than 200-day SMA to simplify calculations. If we interpret the equity market to be in a bull (bear) state when the S&P 500 Index is above (below) its 10-month SMA, the question is whether limiting momentum strategy choices to equity-like (alternative class) assets during equity bull (bear) markets is advantageous. Specifically, we test this combination strategy on the following eight asset class exchange-traded funds (ETF), plus cash:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

At the end of each month, when the S&P 500 Index is above (below) its 10-month SMA, we allocate all funds to the equity-like (alternative class) asset with the highest total return over the past five months. Using monthly closes for the S&P 500 Index since April 2002 and adjusted closing prices for the asset class proxies and the yield for Cash since July 2002 (or inception if not available then) through November 2012, we find that: Keep Reading

How do specialized stock indexes relate to commonly used equity risk factors? In his February 2012 paper entitled “Evaluating Alternative Beta Strategies”, Xiaowei Kang examines risk exposures (betas), construction methodologies and historical performances of alternative stock indexes such as those based on value, low-volatility and diversification strategies. He considers five risk factors: (1) market, representing excess return of the market capitalization-weighted U.S. stock market; (2) size, representing return from a portfolio that is long small-cap stocks and short large-cap stocks; (3) value, representing return from a portfolio that is long high book-to-market stocks and short low book-to-market stocks; (4) momentum, representing return from a portfolio that is long past winning stocks and short past losing stocks; and, (5) volatility, representing return from a portfolio that is long high-volatility stocks and short low-volatility stocks. Using monthly returns for several specialized indexes and the specified risk factors as available through 2011, he finds that: Keep Reading

The model “Simple Asset Class ETF Momentum Strategy” (SACEMS) explores combinations of diversification and momentum as applied to exchange-traded fund (ETF) proxies for asset classes. As introduced, this strategy employed a baseline momentum ranking interval (six-month lagged ETF total return) to the following asset class ETFs, plus cash:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

However, “Simple Asset Class ETF Momentum Strategy Robustness/Sensitivity Tests” shows that the six-month momentum ranking interval is not optimal in terms of average monthly return or cumulative return over the available sample period. With angst over data snooping bias, we are revising the model strategy by substituting an historically optimal momentum ranking interval. Using the SACEMS dataset from (data through August 2012) to compare performances of the baseline and optimal calculation intervals, we find that: Keep Reading

What does price momentum of stocks, whether total or risk-adjusted, imply about future returns of associated corporate bonds? In their August 2012 paper entitled “Residual Equity Momentum for Corporate Bonds”, Daniel Haesen, Patrick Houweling and Jeroen Van Zundert compare the predictive powers of total stock price momentum and risk-adjusted (residual) stock price momentum to predict returns of same-firm bonds. To focus on firm effects, they remove the influence of interest rates by measuring bond returns in excess of duration-matched U.S. Treasury instruments. They form (overlapping) bond portfolios monthly by: (1) ranking firms into fifths (quintiles) based on cumulative stock returns in excess of the risk-free rate over a past interval (base case, six months); (2) skipping a month; and, (3) forming a hedge portfolio that is long (short) for the next 1, 3, 6 or 12 months the equally weighted bonds of firms in the quintile with the highest (lowest) past stock returns. They calculate residual stock returns via 36-month lagged rolling regressions of excess stock returns versus the Fama-French model risk factors (market, size, book-to-market). Using monthly returns for U.S. investment grade and high-yield corporate bonds and associated stocks (2,442 firms), and for duration-matched U.S. Treasury instruments and the three equity risk factors, during January 1994 through September 2011, they find that: Keep Reading