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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.

Sector Breadth as Market Return Indicator

Does breadth of equity sector performance predict overall stock market return? To investigate, we relate next-month stock market return to sector breadth (number of sectors with positive past returns) over lookback intervals ranging from 1 to 12 months. We consider the following nine sector exchange-traded funds (ETF) offered as Standard & Poor’s Depository Receipts (SPDR):

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)

We use SPDR S&P 500 (SPY) to represent the overall stock market. Using monthly dividend-adjusted returns for SPY and the sector ETFs during December 1998 through August 2019, we find that: Keep Reading

European Stock Return Predictors

Can investors effectively use firm characteristics to screen European stocks? In their August 2019 paper entitled “Predictability and the Cross-Section of Expected Returns: Evidence from the European Stock Market”, Wolfgang Drobetz, Rebekka Haller, Christian Jasperneite and Tizian Otto examine the power of 22 firm characteristics to predict stock returns individually and jointly. They assume market-based characteristics are available immediately and accounting-based characteristics are available four months after firm fiscal year end. For multi-characteristic predictions, they consider 5-characteristic, 8-characteristic and 22-characteristic models. For regression-based forecasts, they use either 10-year rolling or inception-to-date monthly inputs. For economic tests, they form equal-weighted or value-weighted portfolios that are each month long (short) the tenth, or decile, of stocks with the the highest (lowest) expected next-month returns based on 22-characteristic regression outputs. To estimate net performance, they apply one-way trading frictions of 0.57%. Using groomed monthly data for all firms in the STOXX Europe 600 index during January 2003 through December 2018, they find that:

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SMA10 vs. OFR FSI for Stock Market Timing

In response to “OFR FSI as Stock Market Return Predictor”, a subscriber suggested overlaying a 10-month simple moving average (SMA10) technical indicator on the Office of Financial Research Financial Stress Index (OFR FSI) fundamental indicator for timing SPDR S&P 500 (SPY). The intent of the suggested overlay is to expand risk-on opportunities safely. To test the overlay, we add four strategies (4 through 7) to the prior three, each evaluated since January 2000 and since January 2009:

  1. SPY – buy and hold SPY.
  2. OFR FSI-Cash – hold SPY (cash as proxied by 3-month U.S. Treasury bills) when OFR FSI at the end of the prior month is negative or zero (positive).
  3. OFR-FSI-VFITX – hold SPY (Vanguard Intermediate-Term Treasury Fund Investor Shares, VFITX, as a more aggressive risk-off asset than cash) when OFR FSI at the end of the prior month is negative or zero (positive).
  4. SMA10-Cash – hold SPY (cash) when the S&P 500 Index is above (at or below) its SMA10 at the end of the prior month.
  5. SMA10-VFITX – hold SPY (VFITX) when the S&P 500 Index is above (at or below) its SMA10 at the end of the prior month.
  6. OFR-FSI-SMA10-Cash – hold SPY (cash) when either signal 2 or signal 4 specifies SPY. Otherwise, hold cash.
  7. OFR-FSI-SMA10-VFITX – hold SPY (cash) when either signal 3 or signal 5 specifies SPY. Otherwise, hold VFITX.

Using end-of-month values of OFR FSI, SPY total return and level of the S&P 500 Index during January 2000 (OFR FSI inception) through June 2019, we find that:

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Combining RSI Range and RSI Momentum for Stocks

Some traders use a Relative Strength Index (RSI) range to identify trend and RSI extremes to signal turning points. How long should they require that RSI remain in range, and how often should they require that RSI recapture a momentum threshold? In his December 2018 paper entitled “Finding Consistent Trends with Strong Momentum – RSI for Trend-Following and Momentum Strategies”, Arthur Hill systematically tests the predictive power of 14-day RSI range and momentum signals on S&P 500 stocks. Specifically, he tests each of the following five signals over lookback intervals of 25, 50, 75, 100 and 125 trading days:

  1. RSI Bull Range: RSI between 40 and 100.
  2. RSI Bear Range: RSI between 0 and 60.
  3. RSI Bull Momentum: highest high value of RSI greater than 70.
  4. RSI Bear Momentum: lowest low value of RSI less than 30.
  5. RSI Bull Range-Momentum: combination of 1 and 3.

For example, 25-day RSI Bull Range signals buy at the close when 14-day RSI has been between 40 and 100 over the last 25 trading days and sell at the open when it next crosses below 40. His performance metrics are gross Success Rate (frequency of positive/negative returns after buy/sell signals) and gross Profit/Loss Ratio (average gain of successful trades divided by average loss of failed trades). Using daily prices for historical S&P 500 stocks during July 1998 through June 2018, he finds that:

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Optimal SMA Calculation Interval for Long-term Crossing Signals?

Is a 10-month simple moving average (SMA10) the best SMA for long-term crossing signals? If not, is there some other optimal SMA calculation interval? To check, we compare performance statistics for SMA crossing signals generated by calculation intervals ranging from 2 trailing months (SMA2) to 48 trailing months (SMA48), as applied to the S&P 500 Index. Using monthly S&P 500 Index closes, monthly S&P 500 Composite Index dividend data from Robert Shiller and monthly average yields for 3-month Treasury bills (T-bills) during January 1950 through June 2019, we find that: Keep Reading

Optimal Cycle for Monthly SMA Signals?

A subscriber commented and asked:

“Some have suggested that the end-of-the-month effect benefits monthly simple moving average strategies that trade on the last day of the month. Is there an optimal day of the month for long-term SMA calculation and does the end-of-the-month effect explain the optimal day?”

To investigate, we compare 21 variations of a 10-month simple moving average (SMA10) timing strategy generated by shifting the monthly return calculation cycle relative to trading days from the end of the month (EOM). Specifically, the 21 variations represent calculation cycles ranging from 10 trading days before EOM (EOM-10) to 10 trading days after EOM (EOM+10). We apply the strategy to the S&P 500 Index as a proxy for the U.S. stock market. The strategy holds the S&P 500 Index (cash) whenever the index is above (below) its SMA10 as of the most recent monthly calculation. Using daily S&P 500 Index closes and 3-month Treasury bill (T-bill) yields as the return on cash during January 1990 through mid-June 2019, we find that: Keep Reading

Simple Tests of Sy Harding’s Seasonal Timing Strategy

Does the technically adjusted Seasonal Timing Strategy popularized some years ago in Sy Harding’s Street Smart Report Online (now unavailable due to Mr. Harding’s death) generate attractive performance? This strategy combines “the market’s best average calendar entry [October 16] and exit [April 20] days with a technical indicator, the Moving Average Convergence Divergence (MACD).” According to Street Smart Report Online, applying this strategy to a Dow Jones Industrial Average (DJIA) index fund generated a cumulative return of 213% during 1999 through 2012, compared to 93% for the DJIA itself. To check over a longer sample period with an alternative market proxy, we apply the strategy to SPDR S&P 500 (SPY) since its inception and consider several alternatives, as follows:

  1. SPY – buy and hold SPY.
  2. Seasonal-MACD – seasonal timing per specified dates with MACD refinement, holding cash when not in SPY.
  3. Seasonal Only – seasonal timing per the same dates without MACD refinement, again holding cash when not in SPY.
  4. SMA200 – hold SPY (cash) when the S&P 500 Index is above (below) its 200-day simple moving average at the prior daily close. 

For all strategies, we use the yield on short-term U.S. Treasury bills (T-bills) as the return on cash. Using daily closes for the S&P 500 Index, dividend-adjusted closes for SPY and T-bill yield during 1/29/93 (SPY inception) through 5/13/19, we find that: Keep Reading

Optimal Intrinsic Momentum and SMA Intervals Across Asset Classes

What are the optimal intrinsic/absolute/time series momentum (IM) and simple moving average (SMA) measurement intervals for different asset class proxies? To investigate, we use data from the Simple Asset Class ETF Momentum Strategy for the following eight asset class exchange-traded funds (ETF), plus Cash:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

For IM tests, we invest in each ETF (Cash) when its return over the past one to 12 months is positive (negative). For SMA tests, we invest in each ETF (Cash) when its price is above (below) its average monthly price over the past two to 12 months. Since SMA rules use price levels and IM rules use returns, IM measurement interval N corresponds to SMA measurement interval N+1. For example, a 6-month IM measurement uses the same start and stop points as a 7-month SMA measurement. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key metrics for comparing different IM and SMA measurement intervals since earliest ETF data availabilities based on the longest IM measurement interval. Using monthly dividend-adjusted closing prices for the asset class proxies and the yield for Cash over the period July 2002 (or inception if not available by then) through April 2019, we find that:

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Long-term SMA and TOTM Combination Strategy

“Turn-of-the-Month Effect Persistence and Robustness” indicates that average absolute returns during the turn-of-the-month (TOTM) are strong for both bull and bear markets. Does a strategy of capturing all bull market returns and TOTM returns only during bear markets perform well? To investigate, we apply four strategies to S&P Depository Receipts (SPY) as a tradable proxy for the stock market:

  1. Buy and hold SPY.
  2. Hold SPY (cash) when SPY closes above (below) its 200-day simple moving average (SMA200).
  3. Hold SPY from the close five trading days before through the close four trading days after the last trading day of each month and cash at all other times (TOTM).
  4. Hold SPY when SPY closes above its 200-day SMA and otherwise use the TOTM strategy (SMA200 or TOTM).

We explore sensitivities of these strategies to a range of one-way SPY-cash switching frictions, with baseline 0.1%. Using daily dividend-adjusted closing levels of SPY from inception (January 1993) through early April 2019 and contemporaneous 3-month Treasury bill (T-bill) yields, we find that: Keep Reading

Trailing Stop-loss Effectiveness for Stocks

How well do trailing stop-loss rules work for U.S. stocks? In their March 2019 paper entitled “Risk Reduction Using Trailing Stop-Loss Rules”, Bochuan Dai, Ben Marshall, Nick Nguyen and Nuttawat Visaltanachoti evaluate effectiveness of trailing stop-loss rules. Traditional stop-loss rules are price-based or time-based. Trailing stop rules sell (buy back) a stock when it declines X% from a high price (rises X% above a low price). The initial trailing stop is X% below the purchase price, remaining at this level unless the stock price rises and escalates to X% below each new high. Stock sales occur at the close on the day after respective stop-loss triggers, with proceeds moved to U.S. Treasury bills (T-bills). Stock re-entries occur at the close on the day after respective buy triggers (see the figure below). They consider trailing stop thresholds of 1%, 5%, 10% and 20%. They use buy-and-hold as a benchmark. Using daily returns for 25,997 common stocks, including delisted stocks, during July 1926 through December 2016, they find that:

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