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Technical Trading

Does technical trading work, or not? Rationalists dismiss it; behavioralists investigate it. Is there any verdict? These blog entries relate to technical trading.

Testing the Guard Score

A subscriber suggested testing of the Guardian Indicator, “a proprietary new market-strength indicator designed to enhance risk-adjusted investment returns by identifying long-term directional changes in the stock market.” This indicator tabulates Guard Score (GS) “votes” by U.S. equity sectors to predict the trend of the overall U.S. stock market. Per the paper “Introducing Guardian Indicator: Market Timing Based on Momentum and Volatility”, flagged by the subscriber, GS is the greater of two contributing indicators:

  • Price momentum indicator (PI), the ratio of the 50-day simple moving average (SMA) to the 200-day SMA of asset/index level.
  • Volatility regime indicator (VI), the ratio of the 1250-day SMA to the 250-day SMA of downside deviation, calculated as the square root of the sum of squared negative daily returns over the past 90 trading days divided by 90.

If GS is greater than one, the trend is bullish. The paper applies GS to time the S&P 500 Index during 1957-2014 (apparently without dividends). Here we replicate the GS series for the S&P 500 Index (excluding dividends) and use it to time the index. In calculating returns, however, we account for S&P 500 dividends by each month allocating the annual dividend yield from Robert Shiller’s data to the days of that month (dividing by 252). We assume cash earns the 3-month U.S. Treasury bill (T-bill) yield. We invest in the S&P 500 Index (T-bills) when prior-day GS for the index is greater than (less than or equal to) one. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as performance measures. We use total return from buying and holding the S&P 500 Index (B&H) as a benchmark. Using daily S&P 500 Index level and monthly S&P 500 dividend yield and T-bill yield during January 1950 through December 2015 (allowing first calculation of VI in May 1955), we find that: Keep Reading

Combining Seasonality and Trend Following by Asset Class

Does seasonality usefully combine with trend following for timing asset markets? In his January 2016 paper entitled “Multi-Asset Seasonality and Trend-Following Strategies”, Nick Baltas examines seasonal patterns (based on same calendar month over the past ten years) for four asset classes: commodities, government bonds, currency exchange rates and country equity markets. He then tests whether identified seasonal patterns enhance a simple trend-following strategy that is long (short) the inverse volatility-weighted assets within a class that have positive (negative) excess returns over the past 12 months. Specifically, he closes any long (short) trend positions in the bottom (top) fifth of seasonality rankings. To assess net performance, he considers trading frictions ranging from 0.05% to 0.25%. Using spot and front futures return data for 19 commodity price indexes and spot return data for 16 10-year government bonds, 10 currency exchange rates and 18 country equity total return indexes as available through December 2014, he finds that: Keep Reading

Performance of Actual Quant Strategies

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

Combining SMA Crash Protection and Momentum in Asset Allocation

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

Trend Factor and Future Stock Returns

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

Simple Volatility Harvesting?

Findings in “Add Stop-gain to Asset Class Momentum Strategy?” suggest that systematic capture of upside volatility may enhance the base strategy. Does this conclusion hold for a simpler application to a single liquid asset over a longer sample period? To investigate, we apply a stop-gain rule to SPDR S&P 500 (SPY) that: (1) exits SPY if its intra-month return exceeds a specified threshold; and, (2) re-enters SPY at the end of the month. We also look at a corresponding stop-loss rule. Using monthly unadjusted highs, lows and closes (for stop-gain and stop-loss calculations) and dividend-adjusted closes (for return calculations) for SPY during February 1993 through November 2015 (275 months), we find that:

Keep Reading

Stop-losses on Stock Positions in Depth

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

Long-run Moving Average Horse Race for Timing the U.S. Stock Market

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

Exploiting the Trend Lag of Small Stocks?

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

Valuation/Trend Hedging of a Value and Momentum Stock Portfolio

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

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