Momentum Investing

Do time series carry (intrinsic value) and time series momentum (intrinsic momentum) strategies work across asset classes? What drives their returns, and how do they interact? In the January 2013 very preliminary version of their paper entitled “The Returns to Carry and Momentum Strategies: Business Cycles, Hedge Fund Capital and Limits to Arbitrage”, Jan Danilo Ahmerkamp and James Grant examine intrinsic value strategy and intrinsic momentum strategy returns for 55 worldwide futures contract series spanning equities, bonds, currencies, commodities and metals, including the effects of business cycle/economic conditions and institutional ownership. They study futures (rather than spot/cash) markets to minimize trading frictions and avoid shorting constraints. They calculate futures contract returns relative to the nearest-to-maturity futures contract (not spot/cash market) price. The momentum signal is lagged 12-month cumulative raw return. The carry (value) signal is the lagged 12-month average normalized price difference between second nearest-to-maturity and nearest contracts. They test strategies that are each month long (short) contracts with positive (negative) value or momentum signals. They also test a combination strategy that is long (short) contracts with both value and momentum signals positive (negative). For comparability of assets, they weight contract series within multi-asset portfolios by inverse volatility, estimated as the average absolute value of daily returns over the past three months. Their benchmark is a long-only portfolio of all contracts weighted by inverse volatility. Using daily settlement prices for the nearest and second nearest futures contracts of the 55 series (10 equities, 12 bonds, 17 commodities, nine currencies and seven metals) as available during 1980 through 2012, they find that: Keep Reading

Do industries exhibit the market beta, value and momentum anomalies overall and in recent data? In his August 2012 paper entitled “The Failure of the Capital Asset Pricing Model (CAPM): An Update and Discussion”, Graham Bornholt examines the beta, value and momentum anomalies using returns for 48 U.S. industries. Each month, he forms three groups of eight equally weighted portfolios of industries ranked separately by: (1) beta based on rolling regressions of industry returns versus value-weighted market returns over the past 60 months; (2) value based on the latest available industry book-to-market ratios (value-weighted composites of component firm book-to-market ratios, updated annually); and, momentum based on lagged six-month industry returns. There are therefore six industries in each portfolio. Using monthly industry returns from Kenneth French’s website, monthly returns for the value-weighted U.S. stock market in excess of the one-month U.S. Treasury bill yield, and industry component book-to-market ratios during July 1963 through December 2009 he finds that: Keep Reading

Do time-series (intrinsic) momentum rules for timing stocks beat comparable simple moving average (SMA) rules? In the February 2013 version of their paper entitled “Time-Series Momentum Versus Moving Average Trading Rules”, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti compare and contrast the stock portfolio timing results of intrinsic momentum and SMA rules. They compare intrinsic momentum timing rules that buy (sell) when price moves above (below) its value 10, 50, 100 or 200 trading days ago to SMA timing rules that buy (sell) when price moves above (below) its SMA over the same look-back intervals. They focus on a long-only strategy applied to five value-weighted size (quintile) portfolios of U.S. stocks, switching to U.S. Treasury bills (T-bill) when on sell signals. As an alternative, they consider shorting stocks when on sell signals. They also test some timing rules on ten international stock markets (Australia, Canada, France, Germany, Italy, Japan, the Netherlands, Sweden, Switzerland and the UK). Using data for U.S. size portfolios from Ken French’s website during 1963 through 2011 and for international stock market indexes during 1973 through 2011, along with contemporaneous T-bill yields, they find that: Keep Reading

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