Technical Trading

When the stock market close is strong (weak) relative to its daily range, does it indicate pent-up buying (selling) demand? Should one trade with or against this relative close? To investigate, we relate position of the daily close for the S&P 500 Index relative to its same-day range to future return for the index. We calculate:

• Daily range as High minus Low, divided by Open.
• Daily relative close as Close minus Low, divided by High minus Low.

Using daily open, high, low and close levels of the S&P 500 Index during 1/2/62 (the earliest with a daily range) through 3/17/20, we find that:

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Are there any seasonal, technical or fundamental strategies that reliably time the U.S. stock market as proxied by the S&P 500 Total Return Index? In the February 2018 version of his paper entitled “Investing In The S&P 500 Index: Can Anything Beat the Buy-And-Hold Strategy?”, Hubert Dichtl compares excess returns (relative to the U.S. Treasury bill [T-bill] yield) and Sharpe ratios for investment strategies that time the S&P 500 Index monthly based on each of:

• 4,096 seasonality strategies.
• 24 technical strategies (10 slow-fast moving average crossover rules; 8 intrinsic [time series or absolute] momentum rules; and, 6 on-balance volume rules).
• 18 fundamental variable strategies based on a rolling 180-month regression, with 1950-1965 used to generate initial predictions.

In all cases, when not in stocks, the strategies hold T-bills as a proxy for cash. His main out-of-sample test period is 1966-2014, with emphasis on a “crisis” subsample of 2000-2014. He includes extended tests on seasonality and some technical strategies using 1931-2014. He assumes constant stock index-cash switching frictions of 0.25%. He addresses data snooping bias from testing multiple strategies on the same sample by applying Hansen’s test for superior predictive ability. Using monthly S&P 500 Index levels/total returns and U.S. Treasury bill yields since 1931 and values of fundamental variables since January 1950, all through December 2014, he finds that:

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How can investors suppress the downside of trend following strategies? In their July 2019 paper entitled “Protecting the Downside of Trend When It Is Not Your Friend”, flagged by a subscriber, Kun Yan, Edward Qian and Bryan Belton test ways to reduce downside risk of simple trend following strategies without upside sacrifice. To do so, they: (1) add an entry/exit breakout rule to a past return signal to filter out assets that are not clearly trending; and, (2) apply risk parity weights to assets, accounting for both their volatilities and correlations of their different trends. Specifically, they each month:

• Enter a long (short) position in an asset only if the sign of its past 12-month return is positive (negative), and the latest price is above (below) its recent n-day minimum (maximum). Baseline value for n is 200.
• Exit a long (short) position in an asset only if the latest price trades below (above) its recent n/2-day minimum (maximum), or the 12-month past return goes negative (positive).
• Assign weights to assets that equalize respective risk contributions to the portfolio based on both asset volatility and correlation structure, wherein covariances among assets adapt to whether an asset is trending up or down. They calculate covariances based on monthly returns from an expanding (inception-to-date) window with baseline 2-year half-life exponential decay.
• Impose a 10% annual portfolio volatility target.

Their benchmark is a simpler strategy that uses only past 12-month return for trend signals and inverse volatility weighting with annual volatility target 40% for each asset. Their asset universe consists of 66 futures/forwards. They roll futures to next nearest contracts on the first day of the expiration month. They calculate returns to currency forwards using spot exchange rates adjusted for carry. Using daily prices for 23 commodity futures, 13 equity index futures, 11 government bond futures and 19 developed and emerging markets currency forwards as available during August 1959 through December 2017, they find that: Keep Reading

Are Bollinger Bands (BB) useful for deciding when to buy low and when to sell high the overall U.S. stock market? In other words, can an investor beat a buy-and-hold strategy by systematically buying (selling) when the market crosses below (above) the lower (upper) BB? To check, we examine the historical behavior of BBs around the 21-trading day (one month) simple moving average (SMA) of S&P 500 SPDR (SPY) as a tradable proxy for the U.S. stock market, with 3-month Treasury bill (T-bill) yield as the return on cash when not in SPY. We consider BB settings ranging from 0.5 to 2.5 standard deviations of daily returns, calculated over the same trailing 21 trading days. We focus on net compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio (with average daily T-bill yield during a year as the risk-free rate for that year) as key performance metrics. Baseline SPY-cash switching frictions are 0.2%. Using daily unadjusted closes of of SPY (to calculate BBs), dividend-adjusted closes of SPY (to calculate total returns) and contemporaneous T-bill yield from the end of January 1993 (SPY inception) through late November 2019, we find that: Keep Reading

A subscriber asked for verification of the finding in “Is Buying Stocks at an All-Time High a Good Idea?” that it is not only a good idea, but a great one, including comparison to a moving average crossover rule. To investigate, we use the S&P 500 Index as a proxy for the U.S. stock market and test a strategy that holds SPDR S&P 500 (SPY) when the S&P 500 Index stands at an all-time high at the end of last month and otherwise holds Vanguard Long-Term Treasury Fund Investor Shares (VUSTX). We compare results to buying and holding SPY, buying and holding VUSTX, and holding SPY (VUSTX) when the S&P 500 Index is above (below) its 10-month simple moving average (SMA10) at the end of last month. We assume 0.1% switching frictions. We compute average net monthly return, standard deviation of monthly returns, net monthly Sharpe ratio (with monthly T-bill yield as the risk-free rate), net compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key strategy performance metrics. We calculate the number of switches for each scenario to indicate sensitivities to switching frictions and taxes. Using monthly closes for the S&P 500 Index, SPY and VUSTX during January 1993 (inception of SPY) through October 2019, we find that:

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A subscriber asked for an update on the effectiveness of applying a two-period Relative Strength Index, RSI(2), to leveraged exchange-traded funds (ETF), with two pairs of trade entry (oversold) and exit (overbought) settings:

1. Buy when RSI(2) falls below 10 and sell when it subsequently rises over 90 (10-90).
2. More conservatively, buy when RSI(2) falls below 5 and exit when it subsequently rises over 70 (5-70).

To investigate, we run simple tests on ProShares Ultra S&P 500 (SSO) with RSI(2) calculations based on the RSI template from StockCharts. Using daily adjusted SSO opens and closes during July 2006 (the first full month SSO is available) through October 2019, we find that: Keep Reading

A subscriber inquired whether “The Only Indicator You Will Ever Need” really works. This technical indicator, a form of the Coppock Guide (or curve or indicator), applied to the Dow Jones Industrial Average by Jay Kaeppel, is a multi-parameter composite based on monthly closes as follows:

1. Calculate the asset’s return over the past 11 months.
2. Calculate the asset’s return over the past 14 months.
3. Average these two past returns.
4. Each month, calculate the 10-month front-weighted moving average (WMA) of this average (multiply the most recent value by 10, the next most recent by 9, the value for the month before that by 8, etc). Then sum the products and divide by 55.
5. Hold the asset (cash) if this WMA is above (below) its value three months ago.

We designate this indicator 11-14WMA3. To test 11-14WMA3 in realistic scenarios, we apply it to the entire available histories for three exchange-traded funds (ETF): SPDR S&P 500 (SPY), SPDR Dow Jones Industrial Average (DIA) and iShares Russell 2000 (IWM). We consider buy-and-hold and a conventional 10-month simple moving average timing strategy (SMA10) as benchmarks. SMA10 holds the ETF (cash) when the ETF’s most recent monthly close is above (below) its 10-month SMA. Using monthly dividend-adjusted and unadjusted closes for the ETFs from their respective inceptions through September 2019 and contemporaneous 3-month U.S. Treasury bill (T-bill) yield, we find that: Keep Reading

A subscriber requested review of an analysis concluding that combining economic trend and market trend signals enhances market timing performance. Specifically, per the example in the referenced analysis, we look at combining:

• The 10-month simple moving average (SMA10) for the broad U.S. stock market. The trend is positive (negative) when the market is above (below) its SMA10.
• The 12-month simple moving average (SMA12) for the U.S. unemployment rate (UR). The trend is positive (negative) when UR is below (above) its SMA12.

We consider scenarios when the stock market trend is positive, the UR trend is positive, either trend is positive or both trends are positive. We consider two samples: (1) dividend-adjusted SPDR S&P 500 (SPY) since inception at the end of January 1993 (nearly 26 years); and, (2) the S&P 500 Index (SP500) since January 1948 (limited by UR availability), adjusted monthly by estimated dividends from the Shiller dataset, for longer-term robustness tests (nearly 71 years). Per the referenced analysis, we use the seasonally adjusted civilian UR, which comes ultimately from the Bureau of Labor Statistics (BLS). BLS generally releases UR monthly within a few days after the end of the measured month. We make the simplifying assumptions that UR for a given month is available for SMA12 calculation and signal execution at the market close for that same month. When not in the stock market, we assume return on cash from the broker is the yield on 3-month U.S. Treasury bills (T-bill). We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance metrics. We use the average monthly T-bill yield during a year as the risk-free rate for that year in Sharpe ratio calculations. While we do not apply any stocks-cash switching frictions or tax considerations, we do calculate the number of switches for each scenario. Using specified monthly data through September 2019, we find that: Keep Reading

A reader asked about the usefulness of the S&P Short-range Oscillator as sometimes used by Jim Cramer to forecast U.S. stock market returns. The self-reported “Performance” of the oscillator, relying on in-sample visual inspection with snooped thresholds, is of small use. Since continuous historical values of the indicator are not publicly available, we conduct an out-of-sample test by:

1. Searching CNBC.com for “Oscillator” “Mad Money” and just “Oscillator” on October 3, 2019 and identifying articles with U.S. stock market forecasts from Jim Cramer based on the S&P Short-range Oscillator.
2. Extracting the date for each forecast and determining whether it is call to be “In” or “Out” of the market.
3. Calculating for each call a cumulative S&P 500 Index return starting at the next open after the article date (generally timestamped after the market close) for 21 trading days.
4. Computing average cumulative performances of “In” and “Out” calls.
5. Comparing these averages to that for all days spanning the search results.

Using the 15 qualifying articles and daily opening levels of the S&P 500 Index during June 16, 2008 through October 31, 2019, we find that: Keep Reading

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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