Technical Trading

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

Pairs trading seeks to exploit temporary divergences in price between two assets that historically track very closely, taking long/short positions in the relatively undervalued/overvalued pair member to generate small steady profits at low risk. How profitable is this concept in implementation? In his November 2010 paper entitled “Pairs Trading in the Land Down Under”, Timofei Bogomolov measures the profitability of the three most cited methods of pairs trading as applied to Australian stocks. All three methods: (1) employ a 12-month rolling window of historical data to select the best five and 20 pairs to trade based on statistical measures of similarity; (2) open trades on selected pairs during the next six months whenever pair prices diverge by a statistically large spread; and, (3) close trades when the spread disappears/diminishes, confidence in pair tracking degrades or the six-month trading interval ends. Using daily price data for 55 to 300 liquid Australian stocks for trading over the period January 1998 through May 2010, he finds that: Keep Reading

Evidence indicates that 52-week highs may be effective momentum signals for individual stocks, but probably not for major U.S. indexes. What do 52-week highs indicate for emerging markets? In their paper entitled “Predictability of Future Index Returns Based on the 52-Week High Strategy”, Mirela Malin and Graham Bornholt investigate the predictive power of 52-week highs for future returns of emerging markets indexes. To test the power of the 52-week high, they form monthly portfolios that are long (short) the fourth of emerging markets indexes that rank fractionally nearest to (farthest from) their respective 52-week highs and measure returns over the next 1, 3, 6, 9 and 12 months. They also test for comparison similar momentum portfolios with ranking intervals of 3, 6, 9 and 12 months and the same holding intervals. Both strategies insert a skip-month between ranking and portfolio formation. Using monthly dividend-adjusted levels and 52-week highs for 26 emerging markets indexes as available during January 1988 through March 2009 (171 to 255 months per index), they find that: Keep Reading

Is the aggregate effect of technical trading visible and exploitable at the equity index level? In his March 2007 paper entitled “The Interaction between the Aggregate Behavior of Technical Trading Systems and Stock Price Dynamics”, Stephan Schulmeister investigates how S&P 500 Index futures prices relate to the aggregate trading signals of 2,580 widely used trend-following and contrarian technical trading rules (moving average, momentum and relative strength) implemented with 30-minute data. Using 30-minute data for S&P 500 Index futures over the period 1983-2000, he finds that: Keep Reading

Is the aggregate effect of technical trading visible and exploitable in currency exchange rate trading? In his 2008 paper entitled “Aggregate Trading Behaviour of Technical Models and the Yen/Dollar Exchange Rate 1976-2007”, Stephan Schulmeister investigates the interaction between the aggregate signaling of 1,024 moving average and momentum rules and the behavior of the yen/dollar exchange rate. Using daily yen/dollar exchange rate data over the period 1976-2007, he finds that: Keep Reading

Is technology driving the profitability of technical rules in financial markets from low-frequency trading to high-frequency trading? In his March 2007 paper entitled “The Profitability of Technical Stock Trading Has Moved from Daily to Intraday Data”, Stephan Schulmeister investigates how well 2,580 widely used trend-following and contrarian technical trading rules (moving average, momentum and relative strength) exploit trend and reversal behavior of the S&P 500 Index futures markets. In estimating trading returns, he assumes: (1) that futures positions roll on the tenth day of the month from the expiring contract to the contact expiring three months later, and (2) that trading friction is 0.01% per trade. Using daily open (30-minute) data for the S&P 500 Index spot (futures) prices during 1960-2000 (1983-2006), he finds that: Keep Reading

In his 2010 book Trade the Trader: Know Your Competition and Find Your Edge for Profitable Trading, author Quint Tatro observes that “…what most investors don’t understand as they start to learn their basic technical patterns…is they are the ones actually in play. Seasoned traders are no longer just cuing off of charts or indicators, they are also analyzing those same charts to determine what the amateurs are doing, and are seeking to profit from the ignorance of the newcomers. It’s a chess game where the successful traders are thinking two and three moves ahead, playing off the basic strategy of the newcomers. Those simply pursuing a basic path of understanding technical analysis will find it is a road that ultimately leads to frustration, whereas those looking to trade the traders will be met with an endless world of opportunity. …If you don’t know on which side you fall, odds are you are someone’s next meal.” Some notable points from the book are: Keep Reading

Does pairs trading work for exchange-traded funds (ETF)? In the November 2010 version of their paper entitled “ETF Arbitrage”, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti examine arbitrage opportunities among three dollar-traded ETFs designed to track the S&P 500 Index: SPDR S&P 500 (SPY); iShares S&P 500 Index (IVV); and, the Swiss Exchange iShares S&P 500 (IUSA). The baseline strategy requires a minimum 0.2% ETF price divergence as a conservative threshold for long-short trade initiations to ensure that a large number of very small divergences, arguably subsumed by transaction fees, do not dominate results. This strategy terminates trades upon ETF price convergence. The authors conservatively assume a 15-second execution delay for opening and closing (fill or kill) orders. Using “cleaned” high-frequency, post-decimalization price data from normal trading hours minus the first and last 5 minutes for SPY and IVV (IUSA) from February 2001 (June 2004) through August 2010, they find that: Keep Reading

Is pairs trading (buying the loser and selling the winner of close-substitute stocks that have diverged unusually in price) profitable after accounting for reasonable trading frictions? In the November 2010 version of their paper entitled “Are Pairs Trading Profits Robust to Trading Costs?”, Binh Do and Robert Faff examine the impact of trading friction (commissions, market impact and short selling fees) on pairs trading profitability. Their baseline pairs trading strategy consists of: (1) finding a partner for each stock that minimizes normalized price spread during a 12-month formation period; (2) screening the best pairs based on lowest tracking error; (3) within six months after pairs identification, opening long/short positions in the underpriced/overpriced members of the best pairs with normalized price divergences of at least two standard deviations; and, (4) closing the trade at the first price convergence or, otherwise, at the end of the six-month trading interval. They also consider 29 strategy refinements that address industry affinity, frequency of past price divergence-convergence and/or magnitude of past price divergence. Using prices and industry designations for relatively liquid U.S. stocks over the period July 1962 through December 2009, they find that: Keep Reading