Technical Trading

How does performance of short-term technical strategies related to portfolio turnover and volatility? In their December 2015 paper entitled “101 Formulaic Alphas”, Zura Kakushadze, Geoffrey Lauprete and Igor Tulchinsky explore return relationships among 101 real-life short-term quantitative trading strategies, noting that 80 are still in use as of the publication date. They follow common trader lingo in calling expected return “alpha.” The strategies, relying mostly on price and volume data, generally exploit mean reversion and/or momentum. Performance ignores trading frictions. Using gross trading data for the specified strategies during January 2010 through December 2013, they find that: Keep Reading

Does asset allocation based on both trend following via a simple moving average (SMA) and return momentum work well? In the July 2015 update of their paper entitled “The Trend is Our Friend: Risk Parity, Momentum and Trend Following in Global Asset Allocation”, Andrew Clare, James Seaton, Peter Smith and Stephen Thomas examine the effectiveness of trend following based on SMAs and momentum screens in forming portfolios across and within asset classes. They consider five asset classes: developed equity markets (24 component country indexes); emerging equity markets (16 component country indexes); bonds (19 component country indexes); commodities (23 component commodity indexes); and, real estate (13 country REIT indexes). They compare equal weight and risk parity (proportional to inverse 12-month volatility) strategic allocations. They define trend following as buying (selling) an asset when its price moves above (below) a moving average of 6, 8, 10 or 12 months. They consider both simple momentum (12-month lagged total return) and volatility-adjusted momentum (dividing by standard deviation of monthly returns over the same 12 months) for momentum screens. They ignore trading frictions, exclude shorting and assume monthly trend/momentum calculations and associated trade executions are coincident. Using monthly total returns in U.S. dollars for the five broad value-weighted asset class indexes and for the 95 components of these indexes during January 1993 through March 2015, along with contemporaneous 3-month Treasury bill yields as the return on cash, they find that: Keep Reading

Does the information in short, intermediate and long stock price trends combined by relating multiple simple moving averages (SMA) to future returns usefully predict stock returns? In the September 2015 update of their paper entitled “A Trend Factor: Any Economic Gains from Using Information over Investment Horizons?”, Yufeng Han and Guofu Zhou examine a trend factor that simultaneously captures short, intermediate and long stock price trends. Specifically, at the end of each month for each sampled stock, they:

1. Calculate SMAs over the past 3, 5, 10, 20, 50, 100, 200, 400, 600, 800 and 1,000 trading days.
2. Normalize SMAs by dividing by the final close.
3. Regress monthly SMAs against next-month stock returns to estimate historical linear coefficients for all SMAs.
4. Predict the return for the stock next month based on average SMA coefficients for the past 12 months applied to the most recent set of SMAs.

They define the trend factor as the average monthly gross return for a portfolio that is each month long (short) the equally weighted fifth (quintile) of stocks with the highest (lowest) expected returns. Using daily prices and associated stock/firm characteristics for a broad sample of U.S. common stocks during January 1926 through December 2014, they find that: Keep Reading

Do stop-losses usefully mitigate downside risk in realistic scenarios? In their November 2015 paper entitled “Stop-Loss Strategies with Serial Correlation, Regime Switching, and Transactions Costs”, Andrew Lo and Alexander Remorov analyze the value of stop-losses when asset returns are autocorrelated (trending), regime switching (bull and bear) and subject to trading costs. They consider daily and 10-day measurement intervals, with respective stop-loss ranges of 0% to -6% and 0% to -14%. If at any daily close the cumulative return on the risky asset over the measurement interval falls below a specified threshold, they immediately switch to the risk-free asset (U.S. Treasury bills). They consider two ways to execute stop-loss signals: (1) assume it is possible to estimate signals just before the close and sell at the same close; or, (2) use a signal from the prior close to trigger a market-on-close sell order the next day (delayed execution). They re-enter the risky asset when its cumulative return over a specified interval exceeds a specified threshold. They employ both simulations and empirical tests. For simulations, they estimate trading cost as 0.2%, the average half bid-ask spread of all sampled stocks during 2013-2014. For empirical tests, they use actual half bid-ask spreads as available and estimates otherwise. Empirical findings are most relevant to short-term traders who employ tight stop-losses. Using daily returns and bid-ask spreads as available for a broad sample of U.S. common stocks during 1964 through 2014, they find that: Keep Reading

Does timing the U.S. stock market with moving averages work? In his October 2015 paper entitled “A Comprehensive Look at the Real-Life Performance of Moving Average Trading Strategies”, Valeriy Zakamulin employs a very long dataset to estimate out-of-sample performance and robustness (subsample performance) of four distinct technical trading rules. Specifically, he seeks answers to the following questions:

• How well does market timing really work?
• Does overweighting or underweighting recent prices improve market timing?
• Do timing rules have optimal lookback intervals?
• Can timing rules accurately exploit bull and bear market states?

The four trading rules are:

1. Momentum (MOM) – final price minus initial price across the measurement interval.
2. Price minus Simple Moving-Average (P-SMA) – final price minus linearly decreasing weighted average of past prices backward over the measurement interval.
3. Price minus Reverse Exponential Moving Average (P-REMA) – final price minus exponentially decreasing weighted average of past prices with decay factor 0.8, for an effect between MOM and P-SMA.
4. Double-Crossover Method (DCM) – long-interval EMA minus short-interval EMA with decay factors 0.8 and the short interval fixed at two months.

For all four rules, a positive (negative or zero) signal means hold stocks (the risk-free asset) the following month. For optimization of moving average lookback intervals, he considers both rolling 10-year windows and inception-to-date (expanding window) data and tests intervals up to 24 months. His total sample spans 1860 through 2014, with the first 10 years reserved for lookback interval optimization. He also considers two equal subsamples (1860-1942 and 1932-2014), with the first 10 years of each reserved for initial optimization. He assumes one-way switching friction 0.25%. He uses several risk-adjusted performance measures, emphasizing Sharpe ratio. Using monthly capital gains and total returns of the S&P Composite stock price index and the contemporaneous U.S. Treasury bill yield as the risk-free rate during January 1860 through December 2014, he finds that: Keep Reading

Do small capitalization stocks exploitably lag broad market trends? In their October 2015 paper entitled “Slow Trading and Stock Return Predictability”, Matthijs Lof and Matti Suominen investigate whether overall stock market trends predict variation in the size effect and therefore the performance of small capitalization exchange-traded funds (ETF). For size effect testing, they each year at the end of June rank stocks into tenths (deciles) by market capitalization and calculate the size effect as the difference in value-weighted average returns between the smallest and largest deciles. Using daily returns, trading volumes and institutional buying and selling data for a broad sample of U.S. common stocks during 1964 through 2014 and for a selection of small capitalization ETFs as available through 2014, they find that: Keep Reading

Is there a way to suppress the volatility and drawdowns of a mixed value and momentum stock strategy while retaining most of its benefit? In his September 2015 paper entitled “Learning to Play Offense and Defense: Combining Value and Momentum from the Bottom up, and the Top Down”, Mebane Faber examines the feasibility of a strategy that combines market valuation and market trend timing (defense) with a mixed value and momentum stock selection strategy (offense). Specifically:

For offense, he each month: (1) ranks stocks by each of price-to-earnings, price-to-book and earnings before interest and taxes-to-total enterprise value ratios and then re-ranks them by the average of the three separate value rankings; (2) ranks stocks by each of 3-month, 6-month and 12-month past returns and then re-ranks them by the average of the three separate momentum rankings; and, (3) forms an equally weighted portfolio of the top 100 value and top 100 momentum stocks and holds for three months (three overlapping portfolios).

For defense, he each month: (1) hedges half of the portfolio by shorting the S&P 500 Index if the long-term real earnings yield for the S&P 500 (inverse of the Cyclically Adjusted Price-Earnings ratio, CAPE or P/E10 as calculated by Robert Shiller, minus the most recently available actual 12-month U.S. inflation rate) is in the 20% of its lowest inception-to-date monthly values; and, (2) hedges half of the portfolio by shorting the S&P 500 Index if the index is below its 12-month simple moving average. 

The overall portfolio can therefore be 100% long “offense” stocks, 50% hedged or market neutral. He does not account for costs of portfolio reformations or hedging. Using monthly total returns for all NYSE stocks in the top 60% of market capitalizations, monthly levels of the S&P 500 Total Return Index and monthly values of CAPE during 1964 through 2014, he finds that: Keep Reading

Do stock limit order books tip the direction of stock price? In their October 2015 paper entitled “Enhancing Trading Strategies with Order Book Signals”, Alvaro Cartea, Ryan Donnelly and Sebastian Jaimungal test the use of buying and selling pressures based on limit order book data to predict the direction, depth and magnitude of near-term stock price movements. They define pressure simply as the difference between the volume of limit orders at the highest bid minus volume of limit orders at the lowest ask, divided by the sum of the two volumes. When the ratio approaches 1 (-1), there is strong buying (selling) pressure. They test the degree to which traders can enhance performance of round-trip trading strategies by exploiting buying and selling pressure. Using stock limit order book data for ten Nasdaq stocks with relatively large tick sizes during January 2014 through June 2014 to calibrate trading rules and during July 2014 through December 2014 to test application of the rules to trading, they find that: Keep Reading

Are annual stock market winning and losing streaks informative about future market performance? To investigate, we consider up and down annual streaks for the Dow Jones Industrial Average (DJIA). We look at streaks in two ways:

1. Retrospective (non-overlapping). We know the total duration of each streak.
2. Experienced (real-time and partially overlapping). We know each year how long a streak has lasted, but we don’t know when it will end.

Using DJIA annual returns for 1929 through 2014 (86 years), we find that: Keep Reading

What drives the profitability of algorithmic long-short statistical arbitrage trading (such as pairs trading) of liquid U.S. stocks? In their September 2015 paper entitled “Performance v. Turnover: A Story by 4,000 Alphas”, Zura Kakushadze and Igor Tulchinsky examine portfolio turnover and portfolio volatility as potential net return drivers for such trading. Their data source is 4,002 randomly selected portfolios (essentially synonymous with “alphas” in their lexicon) from a substantially larger survivorship bias-free pool of real trading accounts. Position holding periods for sampled portfolios range from 0.7 to 19 trading days. The authors exclude 366 portfolios with negative performance and then remove 347 portfolios as outliers for a residual sample of 3,289 portfolios. Using daily closing prices for holdings in these portfolios over an unspecified sample period, they find that: Keep Reading