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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.

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

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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Optimal Retirement Glidepath with Trend Following

What are optimal allocations during retirement years for a portfolio of stocks and bonds, without and with a trend following overlay? In their March 2019 paper entitled “Absolute Momentum, Sustainable Withdrawal Rates and Glidepath Investing in US Retirement Portfolios from 1925”, Andrew Clare, James Seaton, Peter Smith and Steve Thomas compare outcomes across two sets of U.S. retirement portfolios since 1925:

  1. Standard – allocations to the S&P 500 Index and a bond index ranging from all stocks to all bonds in increments of 10%, rebalanced at the end of each month.
  2. Trend following – the same portfolios with a trend following overlay that shifts stock index and bond index allocations to U.S. Treasury bills (T-bills) when below respective 10-month simple moving averages at the end of the preceding month.

They consider investment horizons of 2 to 30 years to assess glidepath effects. They consider both U.S. Treasury bonds and U.S. corporate bonds to assess credit effects. For comparison of portfolio outcomes, they use real (inflation-adjusted) returns and focus on Perfect Withdrawal Rate (PWR), the maximum annual withdrawal rate that results in zero terminal value (requiring perfect foresight). Using monthly data for the S&P 500 Index, U.S. government and corporate bond indexes and U.S. inflation during 1926 through 2016, they find that: Keep Reading

Joint Fundamental and Technical Analysis

What kinds of fundamental and technical indicators play well together? In their August 2018 paper entitled “When Buffett Meets Bollinger: An Integrated Approach to Fundamental and Technical Analysis”, Zhaobo Zhu and Licheng Sun test performance of six stock portfolios that jointly exploit one of three popular fundamental indicators and one of two popular technical indicators, as follows:

  1. Piotroski’s FSCORE – each quarter long (short) stocks having high (low) scores summarizing a composite of accounting variables.
  2. Standardized unexpected earnings (SUE) – each quarter long (short) the fifth of stocks with the highest (lowest) earnings surprises.
  3. Return on equity (ROE) – each quarter long (short) the fifth of stocks with the highest (lowest) ROEs.
  4. Moving averages (MA) – each month long (short) stocks with 20-day MAs above (below) 125-day MAs at the end of the prior month.
  5. Bollinger bands (BOLL) – long (short) stocks below (above) one standard deviation of daily prices below (above) the average prices over the past 20 trading days.

Specifically, for each of six fundamental-technical pairs, they each month reform a portfolio that is long (short) stocks with both fundamental and technical buy (sell) signals. For risk adjustment, they employ widely used 5-factor (market, size, book-to-market, profitability, investment) alpha. Using accounting data and stock returns for a broad sample of U.S. common stocks priced at least $5, plus monthly factor returns, during January 1985 through December 2015, they find that:

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Country Stock Market Anomaly Momentum

Do country stock market anomalies have trends? In his March 2018 paper entitled “The Momentum Effect in Country-Level Stock Market Anomalies”, Adam Zaremba investigates whether country-level stock market return anomalies exhibit trends (momentum) based on their past returns. Specifically, he:

  • Screens potential anomalies via monthly reformed hedge portfolios that long (short) the equal-weighted or capitalization-weighted fifth of country stock market indexes with the highest (lowest) expected gross returns based on one of 40 market-level characteristics/combinations of characteristics. Characteristics span aggregate market value, momentum, reversal, skewness, quality, volatility, liquidity, net stock issuance and seasonality metrics.
  • Tests whether the most reliable anomalies exhibit trends (momentum) based on their respective returns over the past 3, 6, 9 or 12 months.
  • Compares performance of a portfolio that is long the third of reliable anomalies with the highest past returns to that of a portfolio that is long the equal-weighted combination of all reliable anomalies.

He performs all calculations twice, accounting in a second iteration for effects of taxes on dividends across countries. Using returns for capitalization-weighted country stock market indexes and data required for the 40 anomaly hedge portfolios as available across 78 country markets during January 1995 through May 2015, he finds that: Keep Reading

SACEMS with Momentum Breadth Crash Protection

In response to “SACEMS with SMA Filter”, a subscriber suggested instead crash protection via momentum breadth (proportion of assets with positive momentum) by:

  1. Switching to 100% cash when fewer than four of eight Simple Asset Class ETF Momentum Strategy (SACEMS) non-cash assets have positive past returns.
  2. Scaling from cash into winners when four to eight risk assets have positive past returns (no cash for eight).
  3. Replacing U.S. Treasury bills (T-bills), a proxy for broker money market rates, with iShares Barclays 7-10 Year Treasury Bond (IEF) as “Cash.”

To investigate, we each month rank assets from the following SACEMS universe based on total returns over a specified lookback interval. We also each month measure momentum breadth for the eight non-cash assets using the same lookback interval.

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)

While emphasizing the suggested momentum breadth crash protection threshold, we look at all possible thresholds. While emphasizing a baseline lookback interval, we consider lookback intervals ranging from one to 12 months for the suggested momentum breadth threshold. We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for the equal-weighted (EW) Top 3 SACEMS portfolio, but also look at Top 1 and EW Top 2. We also look at EW Top 3 portfolio turnover. Using monthly dividend-adjusted closing prices for SACEMS assets and IEF and the T-bill yield during February 2006 (the earliest all ETFs are available) through December 2018, we find that: Keep Reading

Trend Following: Momentum or Moving Average?

Are moving averages or intrinsic (time series) momentum theoretically better for following trends in asset prices? In their November 2018 paper entitled “Trend Following with Momentum Versus Moving Average: A Tale of Differences”, Valeriy Zakamulin and Javier Giner compare from a theoretical perspective effectiveness of four popular trend following rules:

  1. Intrinsic Momentum – buy (sell) when the closing price at the end of a specified lookback interval is greater (less) than the closing price at the beginning of the lookback interval.
  2. Simple Moving Average – buy (sell) when the closing price at the end of a specified lookback interval is greater (less) than the equally weighted average closing price during the lookback interval.
  3. Linear Moving Average – buy (sell) when the closing price at the end of a specified lookback interval is greater (less) than the linearly weighted (weights linearly increasing to the most recent) average closing price during the lookback interval.
  4. Exponential Moving Average – buy (sell) when the closing price at the end of a specified lookback interval is greater (less) than the exponentially weighted (weights exponentially increasing to the most recent) average closing price during the lookback interval.

They transform these price rules into return-based versions and create a trend model as an autoregressive return process. They then explore interactions of the trading rules with the trend model. Based on this theoretical approach, they conclude that: Keep Reading

Most Effective U.S. Stock Market Return Predictors

Which economic and market variables are most effective in predicting U.S. stock market returns? In his October 2018 paper entitled “Forecasting US Stock Returns”, David McMillan tests 10-year rolling and recursive (inception-to-date) one-quarter-ahead forecasts of S&P 500 Index capital gains and total returns using 18 economic and market variables, as follows: dividend-price ratio; price-earnings ratio; cyclically adjusted price-earnings ratio; payout ratio; Fed model; size premium; value premium; momentum premium; quarterly change in GDP, consumption, investment and CPI; 10-year Treasury note yield minus 3-month Treasury bill yield (term structure); Tobin’s q-ratio; purchasing managers index (PMI); equity allocation; federal government consumption and investment; and, a short moving average. He tests individual variables, four multivariate combinations and and six equal-weighted combinations of individual variable forecasts. He employs both conventional linear statistics and non-linear economic measures of accuracy based on sign and magnitude of forecast errors. He uses the historical mean return as a forecast benchmark. Using quarterly S&P 500 Index returns and data for the above-listed variables during January 1960 through February 2017, he finds that: Keep Reading

Moving Average Timing of Stock Fundamental Ratios

Can investors time premiums associated with widely used stock/firm fundamental ratios? In their September 2018 paper entitled “It Takes Two to Tango: Fundamental Timing in Stock Market”, Fuwei Jiang, Xinlin Qi, Guohao Tang and Nan Huang use a simple moving average (SMA) trend indicator to time premiums associated with four fundamental stock/firm ratios: book-to-market (BM), earnings-to-price (EP), gross profitability (GP), and return-on-assets (ROA). In calculating these ratios, they lag accounting variables by six months to avoid look-ahead bias. For each ratio, they:

  • At the end of each June, rank stocks into tenths (deciles).
  • Each day, calculate value-weighted average returns for the deciles with the highest (highest BM, EP, GP, ROA) and lowest (lowest BM, EP, GP, ROA) expected returns and maintain price indexes for these two deciles.
  • Each day, hold a long (short) position in the decile with highest (lowest) expected returns only when the decile price index is above (below) its 20-day SMA, indicating an upward (downward) trend. When not holding a decile, hold Treasury bills.

As benchmarks, they each year buy and hold four portfolios that are each long (short) the value-weighted deciles with the highest (lowest) expected returns for one of the fundamental ratios. While focusing on a 20-day SMA, for robustness they also test SMAs of 10, 50, 100 and 200 trading days. While focusing on value weighting, they also look at equal weighting. They run tests on both non-financial Chinese A-share stocks and non-financial U.S. common stocks. Using annual groomed fundamentals data and daily returns for Chinese stocks during January 2001-December 2017 and for U.S. stocks during July 1970-December 2017, and contemporaneous Treasury bill yields, they find that:

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