Technical Trading

Can investors exploit monthly persistence in the value premium for U.S. stocks? In his February 2014 paper entitled “Exploiting Factor Autocorrelation to Improve Risk Adjusted Returns”, Kevin Oversby investigates whether investors can exploit the fact that the Fama-French model high-minus-low (HML) value factor exhibits positive monthly autocorrelation (persistence). The HML factor derives from the difference in performance between portfolios of stocks with high and low book-to-market ratios. Prior published research indicates that the value premium concentrates in small firms, so he focuses on stocks with market capitalizations below the NYSE median. His test strategies each month invest in capitalization-weighted small value (small growth or small momentum) Fama-French portfolios when the prior-month sign of the HML factor is positive (negative). The strategies additionally retreat to a risk-free asset (such as U.S. Treasury bills) if the prior-month return for the test strategy is negative. Using HML factor values and monthly portfolio returns for small value, small growth and small momentum Fama-French portfolios, he finds that: Keep Reading

Do hedge fund managers who use technical analysis beat those who do not? In their May 2014 paper entitled “Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry”, David Smith, Na Wang, Ying Wang and Edward Zychowicz examine the relative performance of users and non-users of technical analysis among hedge fund managers in different sentiment environments. They hypothesize that short-selling constraints prevent market correction of mispricings when sentiment is high (overly optimistic), but not when sentiment is low (overly pessimistic). Discovery of mispricings via technical analysis may therefore be more effective when sentiment is high. To test their hypothesis, they compare the performance of hedge funds that report using technical analysis to that of hedge funds that do not, with focus on the state of market sentiment. They define the market sentiment state as high or low depending on whether the monthly Baker-Wurgler market sentiment measure is above or below its full-sample median. Using end-of-period status on use/non-use of technical analysis and monthly returns for 3,290 live and 1,845 dead funds from the Lipper TASS hedge fund database and monthly market sentiment data during January 1994 through December 2010, they find that: Keep Reading

Does technical market analysis work? In their June 2014 paper entitled “Technical Market Indicators: An Overview”, Jiali Fang, Yafeng Qin and Ben Jacobsen examine the profitability of 93 market indicators as applied to the S&P 500. Of the 93, 50 are market sentiment indicators that attempt to predict market behavior based on the supposition that stock prices tend to rise (fall) when bullish (bearish) sentiment dominates. The remaining 43 are market strength indicators that attempt to predict market trend continuation based on breadth of movements as indicated by volume, number of advancing/declining issues and number of periodic highs/lows. 65 of the 93 indicators are raw (such as numbers of advancing and declining stocks per day), and 28 involve measures constructed to suppress noise (such as number of advancing issues minus number of declining stocks). The authors use the S&P 500 as a test market because of its long history. They consider entire sample periods, equal subperiods, different economic regimes (expansion or contraction) and different sentiment regimes (bullish or bearish as indication of degree of investor irrationality). They employ a generous 10% significance level for statistical tests, with and without estimated trading frictions of 0.10% for switching between the market and a risk-free asset. Using the longest samples available for each indicator through the end of 2010 or 2011 (averaging 54 years and as long as 200 years), they find that: Keep Reading

In reaction to “10-month Versus 40-week Versus 200-day SMA”, a reader inquired whether using measurement intervals of longer than a month to calculate simple moving averages (SMA) would suppress trading compared to monthly intervals and thereby lower trading frictions and improve performance. To check, we compare the performance of simple moving averages based on 12 months (SMA12M), six bi-months (SMA6B) and four quarters (SMA4Q). SMA6B samples six data points bimonthly, with each measurement spanning 11 months. SMA4Q samples four data points quarterly, with each measurement spanning 10 months. Using monthly dividend-adjusted closes for SPDR S&P 500 (SPY) from inception in January 1993 through April 2014 (about 21 years), along with the contemporaneous monthly 3-month Treasury bill (T-bill) yield, we find that: Keep Reading

Do relative momentum and trend filters operate differently on value and growth stocks? In their May 2014 paper entitled “When Growth Beats Value: Removing Tail Risk from Global Equity Momentum Strategies”, Andrew Clare, James Seaton, Peter Smith and Stephen Thomas investigate the effects of relative momentum and trend filters on portfolios of developed and emerging market broad, value and growth stock indexes. Their relative momentum filter each months picks either the top five indexes (Mom5) or top quarter of indexes (Mom25%) based on volatility-adjusted past 12-month return (return divided by standard deviation of monthly returns) at the end of the prior month. Their trend filter each month invests in an index or U.S. Treasury bills (T-bills) according to whether the index is above or below its 10-month simple moving average (SMA10) at the end of the prior month. Using monthly levels of broad, value and growth stock indexes for 23 developed country markets (since 1976) and 21 emerging country markets (since 1998) through 2012, they find that: Keep Reading

Does technical analysis boost or depress performance for individual investors? In their February 2014 paper entitled “Technical Analysis and Individual Investors”, Arvid Hoffmann and Hersh Shefrin combine actual trading histories and results of a survey to investigate the use of technical analysis by individual investors. The 2006 survey solicits objectives, strategies and traits from a large group of individual clients of an online Dutch discount broker. The survey explicitly asks about use technical analysis and/or fundamental analysis. The authors use actual trading records to measure individual investment performance. Using 5500 survey responses matched to detailed trading histories spanning January 2000 through March 2006, they find that: Keep Reading

Is is feasible to exploit stock price deviation from a purely statistical estimate of equilibrium? In his February 2014 paper entitled “Back to Black” (the National Association of Active Investment Managers’ 2014 Wagner Award second place winner), Arthur Grabovsky investigates exploitation of a model based on assumptions that: (1) unpredictable investor behavior sometimes makes stock price deviate from equilibrium; and, (2) price then tends to revert back to equilibrium. He defines equilibrium based on the conventional Capital Asset Pricing Model (CAPM), which holds that an asset’s returns depend on its alpha, market beta and an unexplained (random) noise factor. He employs daily double regressions over rolling windows of 60 trading days to measure how far and in what direction noise makes price trend away from its equilibrium alpha-beta relationship. He normalizes this drift as a number of standard deviations of the average noise factor. He then tests the tendency of stocks that drift too high (low) to revert to alpha-beta equilibrium and devises a long-only strategy to exploit prices that drift too low. He performs sensitivity tests on: (1) the threshold for exiting stocks that are reverting from “too low”; (2) the number of stocks an investor must hold for reliable portfolio performance; and, (3) different levels of trading frictions.  Finally, he considers how different market conditions affect strategy performance. He selects the total return Russell 3000 Index as a market proxy and benchmark. Using daily prices for the market and a broad sample of U.S. stocks with market capitalizations over $100 million during January 2005 through December 2013, he finds that: Keep Reading

How sensitive in a recent sample are outcomes from simple trend following rules to the length of the measurement interval used to detect a trend. To investigate, we consider two simple types of trend following rules as applied to the U.S. market:

1. Hold a risky asset when its price is above its x-month simple moving average (SMAx) and cash when below, with x ranging from two to 12.
2. Hold a risky asset when its x-month return, absolute or intrinsic momentum (IMx), is positive and cash when negative, with x ranging from one to 12.

Specifically, we apply these 23 rule variations to time the S&P 500 Index since the inception of SPDR S&P 500 (SPY) as an easy and flexible way to trade the index over the available sample period and two subperiods, the decade of the 2000s and the last five years. We use the yield on 3-month U.S. Treasury bills (T-bills) to approximate return on cash. We use buying and holding SPY as a benchmark for the active rules. Using monthly closing levels of the S&P 500 Index since April 1992 and dividend-adjusted prices for SPY and T-bill yields since January 1993, all through March 2014, we find that: Keep Reading

Do prices exhibit persistently exploitable trends across asset classes all the time? In their April 2014 paper entitled “Two Centuries of Trend Following”, Y. Lemperiere, C. Deremble, P. Seager, M. Potters and J. P. Bouchaud examine risk-adjusted performance of a trend following strategy across four asset classes (commodities, currencies, stock indexes, bonds) over very long sample periods. They generate trend signals for an asset based on the difference between current monthly closing price and the exponential moving average (EMA) of past monthly closing prices (excluding current price) with a decay rate n months, divided (normalized) by volatility as measured by the EMA of absolute monthly price changes also with decay rate n months. They use a baseline EMA decay rate of five months, but test of findings to other values. They define the trend strength as the statistical significance of gross profit from a hypothetical strategy that buys (sells) a quantity of the asset scaled by the inverse of the volatility when the signal is positive (negative). Their measure of statistical significance is annualized return divided by annualized volatility multiplied by the square root of the number of years the strategy is active. They ignore trading frictions. Using monthly closing futures contract prices as available since 1960 (seven stock indexes, seven 10-year bonds and six currency exchange rates for developed economies and seven commodity series) and spot prices for these assets as available since 1800, they find that: Keep Reading

Are simple moving averages (SMA) effective in generating signals for short-term currency trading? In the April 2014 draft of his paper entitled “ANANTA: A Systematic Quantitative FX Trading Strategy”, Nicolas Georges investigates the effectiveness of fast (2-day) and slow (15-day) SMAs as indicators of currency exchange rate evolutions when applied to ten G10 currency pairs and aggregated. His objective is to buy (sell) currencies expected to appreciate (depreciate) based on aggregation of binary signals (see the first chart below). He rebalances the portfolio twice daily when liquidity is high at the London and New York closes. He uses market orders and includes actual trading costs unique to each currency pair, based on bid-ask spreads ranging from 0.0036% to 0.035%. He does not use stop-losses. He compiles results in U.S. dollars. Using twice daily exchange rates for G10 currency pairs during January 2003 through December 2013, he finds that: Keep Reading