Technical Trading

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

Does stock market timing based on simple moving average (SMA) and time-series (intrinsic or absolute) momentum strategies really work? In the November 2013 version of his paper entitled “The Real-Life Performance of Market Timing with Moving Average and Time-Series Momentum Rules”, Valeriy Zakamulin tests realistic long-only implementations of these strategies with estimated trading frictions. The SMA strategy enters (exits) an index when its unadjusted monthly close is above (below) the average over the last 2 to 24 months. The intrinsic momentum strategy enters (exits) an index when its unadjusted return over the last 2 to 24 months is positive (negative). Unadjusted means excluding dividends. He applies the strategies separately to four indexes: the S&P Composite Index, the Dow Jones Industrial Average, long-term U.S. government bonds and intermediate-term U.S. government bonds. When not in an index, both strategies earn the U.S. Treasury bill (T-bill) yield. He considers two test methodologies: (1) straightforward inception-to-date in-sample rule optimization followed by out-of-sample performance measurement, with various break points between in-sample and out-of-sample subperiods; and, (2) average performance across two sets of bootstrap simulations that preserve relevant statistical features of historical data (including serial return correlation for one set). He focuses on Sharpe ratio (including dividends) as the critical performance metric, but also considers terminal value of an initial investment. He assumes the investor is an institutional paying negligible broker fees and trading in small orders that do not move prices, such that one-way trading friction is the average bid-ask half-spread. He ignores tax impacts of trading. With these assumptions, he estimates a constant one-way trading friction of 0.5% (0.1%) for stock (bond) indexes. Using monthly closes and dividends/coupons for the four specified indexes and contemporaneous T-bill yields during January 1926 through December 2012 (87 years), he finds that: Keep Reading

Does the utilities sector exhibit a useful lead-lag relationship with the broad stock market? In their January 2014 paper entitled “An Intermarket Approach to Beta Rotation: The Strategy, Signal and Power of Utilities”, Charles Bilello and Michael Gayed test a simple strategy that holds either the U.S. utilities sector or the broad U.S. stock market based on their past relative strength. Specifically, when utilities are relatively stronger (weaker) than the market based on total return over the last four weeks, hold utilities (the market) the following week. They call this strategy the Beta Rotation Strategy (BRS) because it seeks to rotate into utilities (the market) when the investing environment favors low-beta (high-beta) stocks. They perform both an ideal (frictionless) long-term test and a short-term net performance test using exchange-traded funds (ETF). Using weekly total returns for the Fama-French utilities sector and broad market since July 1926 and for the Utilities Select Sector SPDR (XLU) and Vanguard Total Stock Market (VTI) since July 2001, all through July 2013, they find that: Keep Reading