Technical Trading

Many studies rely on the first moment (mean) of historical asset return distributions and/or the second moment (variance or standard deviation) to predict future returns. Are the third (skewness, indicating left-right tail asymmetry) and fourth (kurtosis, indicating fat-tailedness) moments of return distributions useful for predicting returns? In the July 2011 update of their paper  entitled “Do Realized Skewness and Kurtosis Predict the Cross-Section of Equity Returns?”, Diego Amaya, Peter Christoffersen, Kris Jacobs and Aurelio Vasquez investigate whether decile sorts of individual stocks based on variance, skewness and kurtosis of intraday stock returns over the past week significantly predict returns the next week. Using past-week averages of daily realized volatility, skewness, and kurtosis computed from prices at five-minute intervals, and associated firm characteristics, for a broad sample of U.S. stocks over the period January 1993 through September 2008 (over two million firm-week observations), they find that: Keep Reading

A reader inquired about the complex strategy for trading stock index proxies and futures described in the March 2010 paper “MR Swing: A quantitative System for Mean‐reversion and Swing Trading in Market Regimes” by David Abrams and Scott Walker. This strategy posits that:

• The stock market switches between bull and bear states, with the bull or bear state in effect when current index level is above or below a channel generated by 200-day simple moving averages (SMA) of daily highs and lows. The channel buffers whipsaws.
• Different (not symmetrically opposite) trading approaches work best during these two states. Specifically, swing trading (short-term mean reversion) works in the bull (bear) state.
• Mean reversion and swing trading signal calculations must incorporate stock market volatility.
• The swing trading and mean reversion components must not produce serious drawdowns when the 200-day SMA indicator whipsaws between bull and bear states.

Using fairly recent daily data for the S&P 500 Index, SPDR S&P 500 (SPY), exchange-traded fund (ETF) proxies for several other stock market indexes and index futures, they find that: Keep Reading

The typical long-term moving average used for technical analysis is 200 trading days. Do moving averages measured over even longer intervals have value? In the December 2010 version of their paper entitled “Technical Analysis with a Long Term Perspective: Trading Strategies and Market Timing Ability”, Dusan Isakov and Didier Marti investigate the performance of stock market trading rules based on simple moving averages (SMA) with measurement intervals up to four years. Their general assumption is that a short-term SMA crossing over (under) a long-term SMA predicts an up (down) trend, with a buffer useful for filtering noise when the values of the two SMAs are close. They consider a basic investment strategy of going long (short) the stock market at the next close after a filtered buy (sell) signal and otherwise staying in cash at the risk free rate. They test this strategy on a universe of 1,886 simple rules involving combinations of: 23 short-term SMAs with measurement intervals of one to 100 days; 48 long-term SMAs with measurement intervals of five to 1000 days; and, a buffer of 1% to filter noise. They then evaluate four complex rules for out-of-sample testing based on combining inception-to-date or rolling historical outputs of the 1,886 simple rules. They also investigate the effects of leverage implemented through debt or index options. Using daily closing levels of the S&P 500 Index to compute moving averages and daily returns and contemporaneous lending and borrowing rates and index option prices over the period 1990 through 2008 (with 1990 through 1993 reserved for initial estimation), they find that: Keep Reading

Can investors systematically benefit from the perspective that trading is the exchange of one asset for another, not the buying and selling of a single asset? In his paper entitled “Optimal Rotational Strategies Using Combined Technical and Fundamental Analysis”, third-place winner for the 2011 Wagner Award presented by the National Association of Active Investment Managers, Tony Cooper presents methods and tools designed to exploit the precept that valuations are relative. An organizing concept for these methods and tools is the Binary Decision Chart (BDC), which in one form addresses simultaneous analysis of two competing investments for the purpose of switching or weighting and in an extended form addresses combining technical analysis (based on observed price action) and fundamental analysis (indicator-based prediction). BDCs are cumulative return charts, but the horizontal axis may be a technical or fundamental indicator rather than time. More specifically, using various asset price series and indicators, he illustrates the following methods/tools: Keep Reading

“The Industry 52-week High Effect” summarizes findings that the 52-week high effect, the future outperformance (underperformance) of stocks currently near their respective 52-week highs (lows), is stronger and more consistent for 20 industries than for individual stocks. Do findings apply to equity sectors that are somewhat broader than the 20 industries? Specifically, might such a strategy outperform past six-month return when applied to the following nine sector exchange-traded funds (ETF) defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR), all of which have trading data back to December 1998:

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

To check, we consider three strategies based on closeness of each sector ETF to its 12-month high, defined as ratio of monthly close to highest monthly close over the prior 12 months. The three strategies are to: (1) allocate all funds each month to the sector ETF closest to its 12-month high at the end of the preceding month (12MH-1); (2) allocate all funds each month to the sector ETF closest to its 12-month high at the end of the month before the preceding month (12MH-1;1); and, (3) allocate all funds each quarter to the sector ETF closest to its 12-month high at the end of the month before the end of the quarter (12MH-3;1). Strategy (2) addresses the concern that a sector ETF surging toward a 12-month might experience some reversion the next month, and strategy (3) addresses the concern (based on the methodology in “The Industry 52-week High Effect”) that the effect materializes over several months. For comparison, we include the strategy of monthly allocation to the sector ETF with the highest total return over the past six months (6-1). Using monthly dividend-adjusted closing prices for the nine sector ETFs and S&P Depository Receipts (SPY) over the period December 1998 through March 2011 (148 months), we find that: Keep Reading

Are 52-week highs and lows useful equity price momentum indicators at the industry level? In their March 2011 paper entitled “Industry Information and the 52-Week High Effect”, Xin Hong, Bradford Jordan and Mark Liu compare the 52-week high effect for industries to that for individual stocks. This effect consists of the future outperformance (underperformance) of stocks currently near their respective 52-week highs (lows). Using monthly closes and rolling 52-week (intraday) highs for all stocks listed on NYSE, AMEX and NASDAQ and 20 value-weighted industry indexes constructed from SIC codes for these firms over the period July 1963 through 2009, they find that: Keep Reading

Do technical indicators add value to fundamental indicators in assessing broad stock market valuation? In their March 2011 paper entitled “Forecasting the Equity Risk Premium: The Role of Technical Indicators”, Christopher Neely, David Rapach, Jun Tu and Guofu Zhou examine the powers of technical and fundamental indicators to predict stock market returns. They consider 12 variations of three stock market index technical indicators: (1) relative values of two moving averages (1 month versus 3, 6, 9 and 12 months); (2) return momentum (past 3, 6, 9 and 12 months); and, (3) relative values of two on-balance volume moving averages (1 month versus 3, 6, 9 and 12 months). They consider 14 fundamental indicators ranging from stock market valuation ratios to Treasury yields, yield spreads and the default spread. They compare mean squared equity risk premium forecast errors for technical and fundamental indicators to that for the historical average premium. They also compare the average utility gain for a mean-variance investor who allocates monthly between stocks and Treasury bills based on either technical or fundamental market forecasts to that for an investor who uses the historical average premium. Finally, they generate equity risk premium forecasts based on a rolling principal component analysis that encapsulates the predictive powers of the 26 technical and fundamental indicators into three or four variables. Using monthly price and volume data for the dividend-adjusted S&P 500 Index and monthly readings of the 14 U.S. fundamental indicators as available over the period 1927 through 2008 (1926-1959 for in-sample optimization and 1960–2008 for out-of-sample testing), along with NBER business expansion and contraction dates, they find that: Keep Reading

Are there technical trading rules that persist in profitability, or does the market adapt to extinguish them? In their January 2011 paper entitled “Technical Analysis in the Foreign Exchange Market”, Christopher Neely and Paul Weller review research on technical trading returns in the foreign exchange market during the era of floating exchange rates. They focus on trends in profitability of technical trading rules and examine whether data snooping/mining biases may have been the sources of past findings of profitability. Based on a survey of academic research on technical trading in the foreign exchange market since the early 1970s, they conclude that: Keep Reading

Information technology has lowered barriers for creating/operating financial asset exchanges (venues for matching supply and demand). Proliferation of low-cost venues elevates competition for investor dollars and tends to depress transaction fees. Automated, broadened supply/demand matching tends to depress bid-ask spreads. This evolving market ecology attracts high-frequency traders (HFT), enabled by new technology to exploit short-term market predictability. Do such traders succeed, and how do they impact the markets in which they trade? In his December 2010 preliminary paper entitled “High Frequency Trading and The New-Market Makers”, Albert Menkveld examines the behavior of one large high-frequency trader (HFT) and the effects of associated trading on the exchanges in which this HFT participates. Using high-frequency (one-second) quote and trade data from two European exchanges during January 1, 2007 through June 17, 2008, he finds that: Keep Reading

What is the best way to measure and interpret market reaction to new information? In their October 2010 paper entitled “Measuring Flow Toxicity in a High Frequency World”, David Easley, Marcos López de Prado and Maureen O’Hara introduce a new method to estimate the degree to which trading in financial markets is informed. They name this metric Volume-Synchronized Probability of Informed Trading (VPIN), approximated by the fraction of trading volume that is imbalanced (absolute difference between seller-initiated and buyer-initiated volumes, divided by total volume).  Their approach builds on three beliefs: (1) new orders indicate arrival of new information potentially predictive of subsequent price moves; (2) a specific volume of trades therefore represents a more consistent metric for information arrival than an interval of time; and, (3) a trade imbalance is the hallmark of arrival of important information. In a related November 2010 paper entitled “The Microstructure of the ‘Flash Crash’: Flow Toxicity, Liquidity Crashes and the Probability of Informed Trading”, these same authors focus this method on the May 6, 2010 market crash. Using high-frequency (one-minute intervals) price and volume data for a variety of futures contracts during January 2008 through August 2010 to construct rolling sets of equal-volume increments, they find that: Keep Reading