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Technical Trading

Does technical trading work, or not? Rationalists dismiss it; behavioralists investigate it. Is there any verdict? These blog entries relate to technical trading.

Combining Annual Fundamental and Monthly Trend Screens

Stock return anomaly studies based on firm accounting variables generally employ annually reformed portfolios that are long (short) the tenth of stocks expected to perform well (poorly). Does adding monthly portfolio updates based on technical stock price trend measurements boost anomaly portfolio performance? In the June 2015 version of their paper entitled “Anomalies Enhanced: The Use of Higher Frequency Information”, Yufeng Han, Dayong Huang and Guofu Zhou test eight equal-weighted long-short portfolios that combine annual screening based on a predictive accounting variable with monthly screening based on a simple moving average (SMA)-based stock price trend rule. The eight accounting variables (screened in June based on prior December data) are: (1) book-to-market ratio; (2) gross profitability; (3) operating profitability; (4) asset growth; (5) investment growth; (6) net stock issuance; (7) accruals; and, (8) net operating assets. The price trend screen excludes from the long (short) side of the portfolio any stock for which 50-day SMA is less than (greater than) 200-day SMA at the end of the prior month. Using accounting and daily price data for a broad sample of U.S. stocks during July 1965 through December 2013, they find that: Keep Reading

Best Stock Pairs Trading Method?

What is the best stock pairs trading method? In their June 2015 paper entitled “The Profitability of Pairs Trading Strategies: Distance, Cointegration, and Copula Methods”, Hossein Rad, Rand Kwong Yew Low and Robert Faff compare performances of three pairs trading methods as applied to U.S. stocks.

  1. Distance – Select the 20 stock pairs with the smallest sum of squared differences in initially normalized dividend-adjusted prices during a 12-month formation period. Then re-normalize prices of selected pairs and initiate equal long-short trades when prices diverge by at least two formation-period standard deviations during a subsequent six-month trading period. Close trades when prices converge or, if not, at the end of the trading period. Re-open trades if prices diverge again withing the trading period.
  2. Cointegration – Sort stock pairs based on sum of squared differences in initially normalized dividend-adjusted prices during a 12-month formation period. Then determine which pairs are cointegrated (exhibit a reliable mean-reverting relationship) during the formation period, and select the 20 cointegrated pairs with the smallest sum of squared differences. Over the subsequent six-month trading period, trade pair divergences and convergences based on cointegration statistics, with long and short position sizes also determined by these statistics.
  3. Copula – Select the 20 stock pairs with the smallest sum of squared differences in initially normalized dividend-adjusted prices during a 12-month formation period. Then construct best-fit copulas for each pair and use copula statistics to determine when pair prices diverge and converge during a subsequent six-month trading period, opening and closing equal long-short trades accordingly.

They iterate each method monthly, so each always involves six overlapping portfolios. They assume round trip broker fees start at 0.7% in 1962 and gradually decline to 0.09% in recent years. They estimate impact of trading on price as 0.3% during 1962-1988 and 0.2% since. They assume zero cost of shorting. They calculate returns based on both employed capital (funding only actual trades) and committed capital (funding 20 concurrent positions per portfolio, with no return on cash). Monthly return for each method is the equally weighted average for the six overlapping portfolios. Using daily dividend-adjusted prices for a broad sample of relatively liquid U.S. common stocks during 1962 through 2014, they find that: Keep Reading

Best Moving Average Weighting Scheme for Market Timing?

What is the best scheme over the long run for identifying U.S. stock market trends? In the May 2015 version of his paper entitled “Market Timing With a Robust Moving Average”, Valeriy Zakamulin isolates the most robust moving average weighting scheme for a U.S. stock market index based on monthly data. He tests 300 weighting schemes. For all schemes, test portfolios are in stocks (a risk-free asset) when the last index price is above (below) the moving average. His principal performance metric is the Sharpe ratio. He defines robust as: (1) being insensitive to outliers; and, (2) generating consistent performance across all observed market environments. He specifies the range of observed market environments as 30 subperiods, each 10 years in length (with 5-year overlaps). He assumes that there is no optimal trend measurement look-back interval and therefore considers 15 intervals (4 to 18 months). He therefore generates 450 ranks by Sharpe ratio for each of the 300 weighting schemes and defines the most robust as the one with the highest median rank. Using monthly estimates of the Standard and Poor’s Composite Total Return Index and the risk-free rate during January 1860 through December 2014, he finds that: Keep Reading

Trend Indicator Similarities

What is the best way to do asset price trend analysis? Two recent papers address this question. In the May 2015 version of their paper entitled “Which Trend is Your Friend?”, Ari Levine and Lasse Pedersen compare time series (intrinsic or absolute) momentum, moving average (fast and slow) crossovers and other trend indicators to determine the best way to identify a price trend. In the May 2015 version of their paper entitled “Uncovering Trend Rules”, Paul Beekhuizen and Winfried Hallerbach describe how to determine the underlying historical weighting schemes (a combination of continuation and reversion) of price moving averages and combinations of price moving averages. Using both theoretical analyses and examples, these papers conclude that: Keep Reading

Long-term Tests of Simple X% Rules

A subscriber requested long-term tests of simple versions of the strategy described by Jason Kelly in The 3% Signal: The Investing Technique that Will Change Your Life. We start with a general strategy targeting an X% quarterly increase in a stock fund, as follows:

  1. Initiate X% rules with either 80%-20% or 60%-40% allocations to a stock fund and a bond fund.
  2. If over the next quarter the stock fund increases by more than X%, transfer the excess from the stock fund to the bond fund.
  3. If over the next quarter the stock fund increases by less than X%, make up the shortfall by transferring money from the bond fund to the stock fund.
  4. If at the end of any quarter the bond fund does not have enough money to make up a shortfall in the stock fund: either draw the bond fund down to 0 and add cash to make up the rest of the shortfall; or, draw the bond fund down to 0 and bear the rest of the shortfall in the stock fund.
  5. Consider two benchmarks: a 100% allocation to the stock fund (B&H); and, 60%-40% allocations to the stock and bond funds, rebalanced quarterly (60-40). Whenever adding cash to the bond fund per Step 4, add equal amounts to the benchmarks.

We consider for X% a range of 2% to 4% in increments of 0.5%. We employ stock and bond mutual funds with long histories: Fidelity Magellan (FMAGX) and Fidelity Investment Grade Bond (FBNDX). We assume there are no trading frictions when adding or withdrawing money from these funds. Using quarterly returns for these funds from the first quarter of 1972 (limited by FBNDX) through the first quarter of 2015 (43.25 years), we find that:

Keep Reading

Market Timing with Moving Averages Over the Very Long Run

Which moving average rules and measurement (lookback) intervals work best? In the March 2015 version of his paper entitled “Market Timing with Moving Averages: Anatomy and Performance of Trading Rules” Valeriy Zakamulin compares market timing rules based on different kinds of moving averages, including simple momentum. He first compares the mathematics of these rules to identify similarities and differences. He then conducts very long run out-of-sample tests of a few trading rules with distinct weighting schemes to measure their market timing effectiveness. He tries both an expanding window (inception-to-date) and rolling windows to discover optimal lookback intervals. He uses Sharpe ratio as his principal performance metric. He estimates one-way trading friction as a constant 0.25%. Using monthly returns for the S&P Composite Index and for the risk-free asset during January 1860 through December 2009, he finds that: Keep Reading

When, Where and Why Stock Pairs Trading Works

Is stock pairs trading particularly successful under predictable conditions? In their December 2014 paper entitled “On the Determinants of Pairs Trading Profitability”, Heiko Jacobs and Martin Weber present a large-scale analysis of pairs trading, evaluating the effects on profitability of the type of news driving pair divergence, the level of available investor attention and obstacle to exploitation (limits of arbitrage). Their pairs trading approach (see the first chart below as an example) employs daily stock price data to:

  1. Calculate each month normalized total return trajectories of stocks over the past 12 months.
  2. Measure differences in trajectories for all possible stock pairs.
  3. Select the 100 pairs with minimum differences and re-normalize their prices.
  4. Whenever over the next six months a pair diverges by more than two standard deviations (per the above 12-month interval), buy the underpriced stock and sell the overpriced stock after a one-day delay.
  5. Close the positions upon price convergence within the next month with a one-day delay. If prices do not converge, close the positions after one month. A pair may trade several times during the six-month trading period.

Using stock return data from 34 countries during 2000 through 2013 (excluding small and illiquid firms) and a sample of U.S. stocks with greater than median capitalizations during 1962 through 2008 with contemporaneous news, investor attention and cost of trading proxies, they find that: Keep Reading

Upside-Downside Participation Ratio Difference as an Alpha Proxy

Is the difference between upside and downside asset participation ratios relative to a benchmark a useful metric for evaluating asset investment performance? In his June 2014 paper entitled “On the Holy Grail of ‘Upside Participation and Downside Protection'”, Edward Qian defines and investigates the performance implications of the Participation Ratio Difference (PRD) as a measure of combined upside participation and downside protection. He defines the upside (downside) participation ratio of an asset as the ratio of expected excess return for the asset to the expected excess return of its benchmark when benchmark returns are positive (negative). “Excess” means in excess of the return on cash (such that cash has zero participation rates). He defines PRD as the simple difference between positive participation ratio (P+) and negative participation ratio (P-). He then investigates the relationship between asset PRDs and one-factor (market) alphas. He then checks PRDs for the S&P 500 sectors (with the S&P 500 Index as the benchmark) and PRDs for Russell style indexes (with the Russell 3000 Index as the benchmark). Using monthly returns of the S&P 500 index and its ten sectors during October 1989 through April 2014 and monthly returns of Russell broad and value-growth style indexes during January 1979 through April 2014, he finds that: Keep Reading

Comprehensive, Long-term Test of Technical Currency Trading

Does quantitative technical analysis work reliably in currency trading? If so, where does it work best? In their May 2013 paper entitled “Forty Years, Thirty Currencies and 21,000 Trading Rules: A Large-Scale, Data-Snooping Robust Analysis of Technical Trading in the Foreign Exchange Market”, Po-Hsuan Hsu and Mark Taylor test the effectiveness of a broad set of quantitative technical trading rules as applied to exchange rates of 30 currencies with the U.S. dollar over extended periods. They consider 21,195 distinct technical trading rules: 2,835 filter rules; 12,870 moving average rules; 1,890 support-resistance signals; 3,000 channel breakout rules; and, 600 oscillator rules. They employ a test methodology designed to account for data snooping in identifying reliably profitable trading rules. They also test whether technical trading effectiveness weakens over time. In testing robustness to trading frictions, they assume a fixed one-way trading cost of 0.025%. Using daily U.S. dollar exchange rates for nine developed market currencies and 21 emerging market currencies during January 1971 through July 2011, they find that:

Keep Reading

Where Technical Trading Works

In which country stock markets is technical analysis likely to work best? In the October 2014 version of her paper entitled “Technical Analysis: A Cross-Country Analysis”, Jiali Fang investigates three potential cross-country determinants of technical trading profitability:

  1. An individualism index, measuring the degree to which individuals integrate via cultural groups.
  2. Market development and integrity metrics, including stock market size, stock market age, transaction costs and measures of investor protection, anti-director rights, ownership concentration and insider trading.
  3. Information uncertainty metrics, including aggregate market turnover, volatility of cash flow growth rate and book-to-market ratio.

She considers 26 previously studied trading rules employing only past prices, classified as: variable moving average (VMA) rules, fixed-length moving average (FMA) rules and trading range break-out (TRB) rules. VMA rules are long (short) an index when a short-term moving average is above (below) a long-term moving average. FMA rules are similar to VMA rules, but hold a newly signaled position a fixed interval of 10 days. TRB rules generate buy (sell) signals when price rises above (falls below) the resistance (support) defined by prices over a specified past interval. Tests include both regressions and model strategies that are long (short) the market index as signaled and invest in the risk-free asset when there is no signal. Using cultural metrics, daily stock market index data and economic/financial variables for 50 countries during March 1994 through March 2014, she finds that: Keep Reading

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