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Sources of Trend-following Profitability

| | Posted in: Technical Trading

What makes trend-following tick? In the April 2017 version of his paper entitled “What Drives Trend-Following Profits?”, Adrian Zoicas-Ienciu investigates sources of trend-following profits in equity indexes and stocks. He focuses on daily trading signals for Dow Jones Industrial Average (DJIA) closing levels, as follows:

  • Each day after the close, he compares the DJIA close to its simple moving average (SMA) plus or minus a buffer to suppress signal noise. If the close is above (below) the SMA plus (minus) the buffer, the signal is buy (sell). Otherwise the signal is neutral. He considers SMAs ranging from 2 to 250 trading days and signal buffers ranging from 1% to 5% for total of 1,245 rules.
  • He implements signal changes at the next daily close by taking a 100% position in DJIA after a neutral signal, a (100%+x) position after a buy signal and a (100%-y) position after a sell signal. This approach allows separation of trend-following versus allocation effects. He assumes rebalancing friction 0.5% of traded value, cost of leverage (x) as the risk-free rate and return on cash (y) as the risk-free rate.
  • He assesses rule performance principally as excess daily return versus buy-and-hold (B&H). He considers as alternative benchmarks the risk-free rate or a combination benchmark that is each day: B&H for a neutral signal; B&H for a buy signal; and, (100%-y) times B&H plus y times the risk-free rate for a sell signal.
  • He assesses overall trend-following performance as the average performance of the 1,245 rules. He also considers the performance of an equally weighted portfolio of the top tenth (decile) of rules in each of 64 sequential 370-day subperiods.

He also evaluates the role of signal volatility (volume-weighted trading frequency) as a determinant of profitability. Using daily DJIA closing prices and 1-month U.S. Treasury bill (T-bill) yields as the risk-free rate during March 1926 through early October 2016, he finds that:

  • Average annual gross excess returns relative to B&H are:
    • 3.72% (0.72%) for overall trend following buy (sell) signals.
    • Positive (negative) for both buy and sell signals during 11 (10) of 64 subperiods.
    • Less profitable during the last two decades. Gross performance of buy signals is significantly lower since 2000, and of sell signals is mostly negative since 1987.
    • 7.96% (3.94%) on average for the top-performing decile of rules within each subperiod.
  • Outperformance of overall trend-following appears due to sample characteristics rather than forecasting ability. Based on gross performance:
    • On average, 63% of all trend-following rules beat B&H.
    • The percentage of rules beating B&H is polarized, above 75% during 33 of 64 subperiods and below 25% during 15 of 64 subperiods. Trend-following success depends on DJIA declines and is very sensitive to the joint choice of benchmark and length of evaluation interval.
    • With a favorable sample and benchmark, the benefit of trend-following parameter optimization and the impact of trading frictions are marginal.
    • The best-performing rules within subperiods generally fail to outperform in subsequent subperiods.
  • Three variables substantially explain variations in trend-following buy and sell excess returns relative to B&H.
    • Traded index return relates positively to buy signal excess return and negatively to sell signal excess return.
    • Traded index volatility relates positively to both buy and sell excess returns.
    • Signal volatility relates negatively to both buy and sell excess returns. In other words, performance declines as trading frequency increases.
    • Explanatory powers of these three variables are robust to inclusion of trading frictions, cost of leverage and return on cash.
  • Results for other major equity indexes and for individual U.S. blue chip stocks, with and without dividends, are similar.

In summary, evidence indicates that the success of trend-following in equity markets depends on sample characteristics and choosing rules with low trading frequencies rather than on any inherent predictive power of trend-following rules.

Cautions regarding findings include:

  • Using an index as an asset ignores any costs of tracking the index. These costs would reduce reported returns.
  • Trading frequency increases as SMA measurement interval and signal buffer decrease, such that gross rankings of trend-following rules may differ from net rankings.
  • As noted in the paper, 0.5% trading frictions may be optimistic for the early part of the sample period and pessimistic for the later part.
  • Using 1-month T-bill yield as cost of leverage and return on cash may be optimistic for most investors.

For a simple illustrations of trading frequency effects, see “Optimal SMA Calculation Interval for Long-term Crossing Signals?” and “10-month Versus 40-week Versus 200-day SMA”.

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