Technical Trading

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

What is the best way to generate price trend signals for trading futures/forward contracts? In their December 2013 paper entitled “CTAs – Which Trend is Your Friend?”, Fabian Dori, Manuel Krieger, Urs Schubiger and Daniel Torgler compare risk-adjusted performances of three ways of translating trends into trading signals:

1. Binary signals (up or down) trigger 100% long or 100% short trades. When trends are strong (ambiguous), this approach generates little trading (whipsaws/over-commitment to weak trends). The price impact of trading via this approach may be substantial for large traders.
2. Continuously scaled signals trigger long or short trades with position size scaled according to the strength of up or down trend; the stronger the trend, the larger the position. Changes in trend strength generate incremental position adjustments.
3. Empirical distribution signals trigger long or short trades with position size scaled according to the historical relationship between trend strength and future return. The strongest trend may not indicate the strongest future return, and may actually indicate return (and therefore position) reversal. Changes in trend strength generate position adjustments.

They test these three approaches for comparable trends exhibited by 96 futures/forward contract series, including: 30 currency pairs, 19 equity indexes, 11 government bond indexes, 8 short-term interest rates (STIR) and 28 commodities. They consider two risk-adjusted return metrics: annualized return divided by annualized volatility, and annualized return divided by maximum drawdown. They ignore trading frictions. Using prices for these 96 series from 1993 to 2013, they find that: Keep Reading

Are Google Trends data an independently useful tool in predicting stock returns? In their March 2014 paper entitled “Do Google Trend Data Contain More Predictability than Price Returns?”, Damien Challet and Ahmed Bel Hadj Ayed apply non-linear machine learning methods to measure whether Google Trends data outperform past returns in predicting future stock returns. They focus on avoiding bias derived from choice of keywords (choosing words with obvious retrospective, but dubious prospective, import) and test strategy parameter optimization. Since Google Trends data granularity is weekly, they employ a six-month calibration interval to predict weekly stock returns. They apply a 0.2% trading friction for all backtested trades. Using weekly returns and Google Trends data for stock tickers and firm names plus other simple, non-overfitted words for the S&P 100 stocks as available through late April 2013, they find that: Keep Reading

Does the variation of individual stock returns with liquidity support an investment style? In the January 2014 update of their paper entitled “Liquidity as an Investment Style”, Roger Ibbotson and Daniel Kim examine the viability and distinctiveness of a liquidity investment style and investigate the portfolio-level performance of liquidity in combination with size, value and momentum styles. They define liquidity as annual turnover, number of shares traded divided by number of shares outstanding. They hypothesize that stocks with relatively low (high) turnover tend to be near the bottom (top) of their ranges of expectation. Their liquidity style thus overweights (underweights) stocks with low (high) annual turnover. They define size, value and momentum based on market capitalization, earnings-to-price ratio (E/P) and past 12-month return, respectively. They reform test portfolios via annual sorts into four ranks (quartiles), with initial equal weights and one-year holding intervals. Using monthly data for the 3,500 U.S. stocks with the largest market capitalizations (re-selected each year) over the period 1971 through 2013, they find that: Keep Reading

A reader observed: “One of the problems with simple moving average (SMA) crossing rules is the churning from random price movements across the average. Lars Kestner proposes improvements to SMA crossing rules that signal:

• BUY when: (1) the close crosses over an SMA of the highs (rather than the closes); and, (2) the SMA of the closes is greater today than yesterday.
• SELL when the close crosses below an SMA of the lows (rather than the closes).

These rules create a self-adaptive band around the SMA to identify true trends rather then noise, while retaining most of the responsiveness of daily measurements.” Do these buffered SMA crossing rules outperform pure rules that simply buy (sell) on crossovers (crossunders) based on daily closes? To check, we compare the terminal values from pure and buffered rules for a 200-day SMA (SMA200) applied to both the Dow Jones Industrial Average (DJIA) and its exchange traded fund (ETF) proxy, SPDR Dow Jones Industrial Average (DIA). Using daily highs, lows and closes for DJIA since October 1928 and DIA since January 1998, both through early February 2014, and the contemporaneous 3-month Treasury bill yield as the return on cash, we find that: Keep Reading