Technical Trading

Does an asset class breadth rule work better than a class-by-class exclusion rule for momentum strategy crash protection? In their July 2017 paper entitled “Breadth Momentum and Vigilant Asset Allocation (VAA): Winning More by Losing Less”, Wouter Keller and Jan Keuning introduce VAA as a dual momentum asset class strategy aiming at returns above 10% with drawdowns less than -20% deep. They specify momentum as the average of annualized total returns over the past 1, 3, 6 and 12 months. This specification gives greater weight to short lookback intervals than a simple average of past returns over these intervals. Specifically, they:

1. Each month rank asset class proxies based on momentum.
2. Each month select a “cash” holding as the one of short-term U.S. Treasury, intermediate-term U.S. Treasury and investment grade corporate bond funds with the highest momentum. 
3. Set (via backtest) a breadth protection threshold (B). When the number of asset class proxies with negative momentum (b) is equal to or greater than B, the allocation to “cash” is 100%. When b is less than B, the base allocation to “cash” is b/B.
4. Set (via backtest) the number of top-performing asset class proxies to hold (T) in equal weights. When the base allocation to “cash” is less than 100% (so when b<B), allocate the balance to the top (1-b/B)T asset class proxies with highest momentum (irrespective of sign).
5. Mitigate portfolio rebalancing intensity (when B and T are different) by rounding fractions b/B to multiples of 1/T.

They construct four test universes from: a short sample of 17 (mostly simulated) exchange traded fund (ETF)-like global asset class proxies spanning December 1969 through December 2016; and, a long sample of 21 index-like U.S. asset classes spanning December 1925 through December 2016. After reserving the first year for initial momentum calculations, they segment each sample into halves for in-sample optimization of B and T and out-of-sample testing. For all cases, they apply 0.1% one-way trading frictions for portfolio changes. Their key portfolio performance metrics are compound annual growth rate (CAGR), maximum drawdown (MaxDD) and a composite of the two. Using monthly returns for the selected ETF-like and index-like assets over respective sample periods, they find that:

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A subscriber requested tests exploring whether a recent death cross for the Dow Jones Industrial Average (DJIA) portends an index crash. To investigate, we consider two ways of evaluating DJIA performance after death crosses and conversely defined golden crosses:

1. Behavior of the index during the 126 trading days (six months) after death and golden crosses.
2. Behavior of the index between converse crosses (death cross-to-golden cross, and golden cross-to-death cross).

We focus on distributions of average returns and maximum drawdowns (MaxDD) during specified periods. We also check robustness by repeating DJIA tests on the S&P 500 Index. Using daily DJIA closes since October 1928 and daily S&P 500 Index closes since January 1950, both through May 2017, we find that: Keep Reading

What makes trend-following tick? In the April 2017 version of his paper entitled “What Drives Trend-Following Profits?”, Adrian Zoicas-Ienciu investigates sources of trend-following profits in equity indexes and stocks. He focuses on daily trading signals for Dow Jones Industrial Average (DJIA) closing levels, as follows:

• Each day after the close, he compares the DJIA close to its simple moving average (SMA) plus or minus a buffer to suppress signal noise. If the close is above (below) the SMA plus (minus) the buffer, the signal is buy (sell). Otherwise the signal is neutral. He considers SMAs ranging from 2 to 250 trading days and signal buffers ranging from 1% to 5% for total of 1,245 rules.
• He implements signal changes at the next daily close by taking a 100% position in DJIA after a neutral signal, a (100%+x) position after a buy signal and a (100%-y) position after a sell signal. This approach allows separation of trend-following versus allocation effects. He assumes rebalancing friction 0.5% of traded value, cost of leverage (x) as the risk-free rate and return on cash (y) as the risk-free rate.
• He assesses rule performance principally as excess daily return versus buy-and-hold (B&H). He considers as alternative benchmarks the risk-free rate or a combination benchmark that is each day: B&H for a neutral signal; B&H for a buy signal; and, (100%-y) times B&H plus y times the risk-free rate for a sell signal.
• He assesses overall trend-following performance as the average performance of the 1,245 rules. He also considers the performance of an equally weighted portfolio of the top tenth (decile) of rules in each of 64 sequential 370-day subperiods.

He also evaluates the role of signal volatility (volume-weighted trading frequency) as a determinant of profitability. Using daily DJIA closing prices and 1-month U.S. Treasury bill (T-bill) yields as the risk-free rate during March 1926 through early October 2016, he finds that: Keep Reading

Is there an easy way to turn conventional stock momentum crashes into gains? In the March 2017 version of her paper entitled “Dynamic Momentum and Contrarian Trading”, Victoria Dobrynskaya examines the timing of momentum crashes and tests a simple dynamic strategy designed to turn the crashes into gains. This strategy follows a conventional stock momentum strategy most of the time, but flips to a contrarian strategy for three months after each market plunge with a lag of one month. The conventional momentum hedge portfolio is each month long the tenth (decile) or third (tercile), depending on sample breadth, of stocks with the highest cumulative returns from 12 months ago to one month ago and short the tenth or third with the lowest cumulative returns. The contrarian hedge portfolio flips the long and short positions. For her baseline case, she defines a market plunge as a monthly return more than 1.5 standard deviations of monthly returns below the average monthly market return (measured in-sample). For most analyses, she employs the Fama-French U.S. equal-weighted and value-weighted extreme decile momentum hedge portfolios during January 1927 through July 2015. For global developed market analyses, she employs extreme tercile momentum hedge portfolios from various sources during November 1990 through March 2016. She also considers long-only momentum portfolios for emerging markets: one broad during June 1991 through March 2016) and one narrow (Latin American only) during June 1995 through March 2016. Using this data, she finds that: Keep Reading

Should market timers use moving averages of different lengths for trading uptrends and downtrends? In his January 2017 paper entitled “Asymmetry between Uptrend and Downtrend Identification: A Tale of Moving Average Trading Strategy”, flagged by a subscriber, Carlin chun-fai Chu investigates whether the use of different (asymmetric) moving average lookback intervals for uptrends and downtrends outperforms using the same lookback interval for both. He considers three types of moving averages: Simple Moving Average, Exponential Moving Average and Triangular Moving Average. He calculates these moving averages separately for each of seven market indexes: S&P 500, FTSE 100, Nikkei 225, Deutscher Aktien, TSX Composite, ASX 200 and Hang Seng. The price series is in uptrend (downtrend) when above (below) a specified moving average. He takes a long (short) position in an index when it crosses above (below) the moving average used during downtrends (uptrends). Using the earliest daily data available from Yahoo!Finance for each of the seven indexes through October 2016, he finds that: Keep Reading

Do attention-driven retail stock investors bid prices of the biggest daily movers to overvaluation? In their March 2017 paper entitled “Daily Winners and Losers”, Alok Kumar, Stefan Ruenzi and Michael Ungeheuer examine the subsequent return behaviors of the biggest daily winning and losing stocks. Specifically, they each day identify the 80 stocks with the highest daily returns (winners) and the 80 stocks with the lowest daily returns (losers). Each month, they group stocks as:

• Winners – a daily winner, but not a daily loser, at least once during the prior month.
• Losers – a daily loser, but not a daily winner, at least once during the prior month.
• Both – a daily winner and a daily loser at least each once during the prior month.
• Never – not a daily winner or a daily loser during the prior month.

They then reform monthly value-weighted and equal-weighted portfolios of the groups to measure performance differences. Using daily returns and characteristis for a broad sample of U.S. common stocks with price above $5, along with stock factor model returns, during July 1963 through December 2015, they find that: Keep Reading

Does adjusting stocks-bonds allocations according to trend following rules improve the performance of 30-year retirement portfolios? In their November 2016 paper entitled “Applying a Systematic Investment Process to Distributive Portfolios: A 150 Year Study Demonstrating Enhanced Outcomes Through Trend Following”, Jon Robinson, Brandon Langley, David Childs, Joe Crawford and Ira Ross compare retirement portfolio performances for variations of the following three strategies that may hold a broad stock market index, a 10-year government bond index or cash (3-month government bills) in the U.S., UK or Japan:

1. Buy and Hold – each month rebalance to fixed 60%-40% or 80%-20% stock-bond allocations.
2. T8 – each month set allocations among stocks, bonds and cash according to whether each of stocks and bonds are above (uptrend) or below (downtrend) respective 8-month exponential moving averages (EMA).
3. T12 – same as T8 but using a 12-month EMA.

See the first two tables below for precise T8 and T12 allocation rules. The authors consider annual portfolio distributions of 0%, 4%, 4.5% or 5% over a 30-year holding interval. They employ the S&P 500, FTSE 100 and TOPIX total return indexes for the U.S., UK and Japan, respectively. When 3-month government bill data are unavailable for the UK or Japan, they insert U.S. data. Using monthly total returns for the specified asset class proxies since 1865 for the U.S., 1935 for the UK and 1925 for Japan, all through 2015, they find that: Keep Reading

Is there an exploitable short-term momentum effect after asset price jumps? In his January 2017 paper entitled “Profitability of Trading in the Direction of Asset Price Jumps – Analysis of Multiple Assets and Frequencies”, Milan Ficura tests the profitability of trading based on continuation of jumps up or down in the price series of each of four currency exchange rates (EUR/USD, GBP/USD, USD/CHF and USD/JPY) and three futures (Light Crude Oil, E-Mini S&P 500 and VIX futures). For each series, he looks for jumps in prices measured at seven intervals (1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 4-hour and 1-day). His statistical specification for jumps uses returns normalized by local historical volatility. He separately tests the last 4, 8, 16, 32, 64, 128 or 256 measurement intervals for the local volatility calculation, and he considers jump identification confidence levels of 90%, 95%, 99% or 99.9%. His trading system enters a trade in the direction of a price jump at the end of the interval in which the jump occurs and holds for a fixed number of intervals (1, 2, 4, 8 or 16). He thus considers a total of 6,860 strategy variations across asset price series. He divides each price series into halves, employing the first half to optimize number of volatility calculation measurement intervals, confidence level and number of holding intervals for each measurement frequency. He then tests the optimal parameters in the second half. He assumes trading frictions of one pip for currencies, and one tick plus broker commission for futures. He focuses on drawdown ratio (average annual profit divided by maximum drawdown) as the key performance metric. He excludes price gaps over weekends and for rolling futures contracts. Using currency exchange rate data during November 1999 through mid-June 2015, Light Crude Oil futures data during January 1987 through early December 2015, E-Mini S&P 500 futures during mid-September 1999 through early December 2015 and VIX futures during late March 2004 through early December 2015, he finds that: Keep Reading

Is there pervasive yield momentum among U.S. corporate bonds? In their November 2016 paper entitled “Is Momentum Spanned Over Corporate Bonds of Different Ratings?”, Hai Lin, Chunchi Wu and Guofu Zhou investigate whether momentum exists in all segments of the U.S. corporate bond market. Their approach to momentum measurement is unconventional, involving cross-sectional regression of bond returns on multiple simple moving averages (SMA) of bond yields. They call their result “trend momentum” to distinguish it from conventional momentum based on simple past return. Specifically, they each month:

1. Calculate yield SMAs over the last 1, 3, 6, 12, 24, 36, 48 and 60 months for each bond.
2. Regress returns for all bonds on respective prior-month yield SMAs to generate correlations (betas) between returns and past yield SMAs, thereby dynamically determining relative importance of yield SMA measurement intervals.
3. Calculate expected (for next month) yield SMA betas as average calculated betas over the past 12 months.
4. Estimate expected return (for next month) for each bond based on current yield SMAs and expected yield SMA betas.
5. Rank bonds based on expected returns into fifths (quintiles) or tenths (deciles).
6. Calculate gross trend momentum factor return as the difference in average (equal-weighted) actual returns between quintiles/deciles with the highest and lowest expected returns.

Using yields, returns, ratings and other characteristics for a broad sample of U.S. corporate bonds during January 1973 through September 2015, they find that: Keep Reading

How do moving averages work for timing asset prices, and how do different kinds of moving averages work differently? In his October 2016 paper entitled “Moving Averages for Market Timing”, Valeriy Zakamulin presents in three chapters: (1) financial series trend detection and turning point identification using moving averages; (2) the mathematics and properties of moving averages, with focus on smoothness and lag time; and, (3) types of moving averages, including regular moving averages, moving averages of moving averages and mixed moving averages that suppress lag time, with focus on differences in smoothness and lag time. Based on mathematical analyses, he concludes that: Keep Reading