Technical Trading

A subscriber asked about the performance of a variation of the monthly reformed 50-50  Simple Asset Class ETF Value Strategy (SACEVS) Best Value-Simple Asset Class ETF Momentum Strategy (SACEMS) Equal-Weighted (EW) Top 2 combination that substitutes 100% SACEMS EW Top 2 whenever both:

1. SPDR S&P 500 ETF Trust (SPY) is the selection for SACEVS Best Value at the end of the prior month.
2. SPY is below its 10-month simple moving average at the end of the prior month.

The objective of the variation is to suppress SACEVS Best Value drawdowns. To investigate, we compare performance results for this variation (“Filtered”) with those for baseline 50-50 SACEVS Best Value-SACEMS EW Top 2. Using monthly returns for SACEVS Best Value and SACEMS EW Top 2 since July 2006 (limited by SACEMS) and monthly dividend-adjusted prices for SPY since September 2005, all through March 2023, we find that: Keep Reading

A subscriber asked about practical exploitation of the hypothesis that the high and low of an exchange-traded fund (ETF) during 9:30AM-10:30AM are informative about its high and low during 10:31AM-4PM. To investigate, we obtain minute-by-minute open, high, low and close prices for SPDR S&P 500 ETF Trust (SPY) for 2019. From that data, we extract the high and low during 9:30AM-10:30AM and the high, low and close for 10:31AM-4:00PM for each trading day. To test explicitly whether the 9:30AM-10:30AM high (low) is indicative of the 10:31AM-4:00PM high (low), we:

• Sell SPY during 10:31AM-4:00PM if it rises to the 9:30AM-10:30AM high and close the short position at 4:00PM.
• Buy SPY during 10:31AM-4:00PM if it falls to the 9:30AM-10:30AM low and close the long position at 4:00PM.

Using the specified SPY price data for 2019, we find that:

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A subscriber asked about boosting the performance of the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS), and thereby the Combined Value-Momentum Strategy (SACEVS-SACEMS), by substituting ProShares Ultra S&P500 (SSO) for SPDR S&P 500 ETF Trust (SPY) in these strategies whenever:

1. SPY is above its 200-day simple moving average (SMA200); and,
2. The CBOE Volatility Index (VIX) SMA200 is below 18.

Substitution of SSO for SPY applies to portfolio holdings, but not SACEMS asset ranking calculations. To investigate, we test all versions of SACEVS, SACEMS and monthly rebalanced 50% SACEVS-50% SACEMS (50-50) combinations. We limit SPY SMA200 and VIX SMA200 conditions to month ends as signals for next-month actions (no intra-month changes). We consider baseline SACEVS and SACEMS (holding SPY as indicated) and versions of SACEVS and SACEMS that always hold SSO instead of SPY as benchmarks. We look at average gross monthly return, standard deviation of monthly returns, monthly gross reward/risk (average monthly return divided by standard deviation), gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio as key performance metrics. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using daily unadjusted SPY and VIX values for SMA200 calculations since early September 2005 and monthly total returns for SSO since inception in June 2006 to modify SACEVS and SACEMS inputs, all through February 2023, we find that: Keep Reading

A subscriber proposed using Minervini Trend Template criteria to time broad U.S. stock market proxies such as SPDR S&P 500 ETF Trust (SPY). Specifically, use leveraged versions of SPY when SPY meets the following seven criteria:

1. SPY is above both its 150-day and 200-day simple moving averages (SMA150 and SMA200).
2. SMA150 is above SMA200.
3. SMA200 trends up for at least one month.
4. SMA50 is above both SMA150 and SMA200.
5. SPY is above its SMA50.
6. SPY is at least 30% above its 52-week (252-day) low.
7. SPY is within 25% percent of its 52-week (252-day) high.

To investigate, we apply the above criteria to daily raw (not dividend-adjusted) SPY daily closes, with criteria 6 and 7 pushing the start of performance testing to January 1994. We use dividend-adjusted daily closes to calculate returns, assuming zero returns when not in SPY. We assume SPY-cash switches occur at the same close as signals, requiring slight anticipation of signals. We consider each of the seven criteria alone and in aggregate (the template). We focus on percentage of time in SPY, number of SPY-cash switches, average daily return, standard deviation of daily returns and daily reward/risk (average return divided by standard deviation) as key performance statistics. Using daily dividend-adjusted and unadjusted prices for SPY during late January 1993 through early March 2023, we find that: Keep Reading

Can investors use volatility signals to identify short-term stock market trend changes? In his February 2023 paper entitled “Using Volatility to Add Alpha and Control Portfolio Risk”, John Rothe uses Welles Wilder’s Average True Range (ATR) volatility metric to generate buy and sell signals for broad U.S. stock market indexes. Specifically, he each trading day:

1. Computes the true range of a broad equity exchange-traded fund (ETF) as the greatest of: (a) daily high minus low; (b) absolute value of daily high minus previous close; and, (c) absolute value of daily low minus previous close.
2. Calculates ATR as the simple average of the last five true ranges (including the current one).
3. Generates a Wilder Volatility Stop (WVS) by multiplying ATR by a factor of 2.5 as representative of investor volatility risk tolerance.
4. When out of the asset, he buys when the asset closes above a dynamic trendline apparently defined by a trend minimum plus current WVS (breakout). When in the asset, he sells when the asset closes below a dynamic trendline apparently defined by a trend maximum minus current WVS (breakdown).

He focuses on SPDR S&P 500 ETF Trust (SPY) during 2000-2010 (beginning of 2000 through 2009) but also looks at both Invesco QQQ Trust (QQQ) and iShares Russell 2000 ETF (IWM). In an appendix, he provides similar results for 2010-2020. He assume trades occur at the same closes as breakout and breakdown signals. He ignores effects of dividends and trading frictions. He uses buy-and-hold SPY as the benchmark for the strategy applied to SPY. Using daily raw (not dividend-adjusted) data for SPY, QQQ and IWM during January 2000 through December 2019, he finds that: Keep Reading

Are there different patterns of short-term stock return reversal based on stock liquidity (measured by size, volatility or turnover)? In their January 2023 paper entitled “Reversals and the Returns to Liquidity Provision”, Wei Dai, Mamdouh Medhat, Robert Novy-Marx and Savina Rizova examine interactions between short-term reversal returns and stock liquidity metrics. They select reversal candidates from the fifth (quintile) of stocks with the highest (winners) and lowest (losers) industry-relative returns over the last 1, 5 or 21 trading days, excluding 3-day returns around earnings announcements. They separately sort stocks into quintiles by size (market capitalization), volatility (standard deviation of daily returns over the last 63 days) or turnover (average percentage of shares outstanding traded daily over the last 63 days). While the sample includes all NYSE, AMEX and NASDAQ common stocks, quintile breakpoints come from NYSE stocks only. Finally, they look at returns to value-weighted intersections of reversal candidate quintiles and size, volatility or turnover quintiles. Using the specified inputs for all listed U.S. common stocks, measured monthly, during January 1973 through December 2021, they find that:

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What is the best short-term reversal indicator for equity exchange-traded funds (ETF)? In his January 2023 paper entitled “A Comparison of Short-Term Mean-Reversion Indicators for Global Equities”, Raymond Micaletti tests several short-term mean reversion indicators on equity ETFs. Specifically, he tests 306 trade setups, encompassing:

• Four broad U.S. equity ETFs, 10 U.S. equity sector ETFs and three non-U.S. equity ETFs.
• 17 indicators, including 14 widely known price oscillators and three oscillator modifications.
• Three short-term holding intervals (1, 3 and 5 days).
• Long and short positions.
• Three levels of signal intensity: top (bottom) 10%, 20% or 30% for long (short) trades.

He ranks indicators by aggregate performance across all ETFs and across all signal intensities for all strategies, for all long strategies, for all short strategies and for each holding period (all, long or short). Using 1-minute open-high-low-close-volume data, adjusted for splits and dividends, to calculate indicators and daily returns for all ETFs during 2003 through 2022, he finds that: Keep Reading

Is there an optimal net (incorporating trading frictions) trend-following strategy for broad stock portfolios? In their November 2022 paper entitled “Optimal Trend-Following With Transaction Costs”, Valeriy Zakamulin and Javier Giner develop and test a simple model that incorporates short-term return persistence (trend) and trading frictions (half bid-ask spread, fees and impact of trading). Their trend-following strategy switches between a stock portfolio and the risk-free asset (Treasury bills). They model trend as typically positive, linearly decreasing daily return autocorrelations for lags up to 25 trading days. For empirical tests, they focus on small-capitalization U.S. stocks (bottom fifth of market capitalizations). Based on past studies, they test 1-way proportional trading frictions in the range 0% to 1%. Using theoretical analyses and daily returns for small stocks/Treasury bill yields during January 1952 through December 2021, they find that: Keep Reading

In response to a prior analysis (updated here), a subscriber asked whether adding a simple moving average (SMA) filter to “Simple Asset Class ETF Momentum Strategy” (SACEMS) assets, either before or after ranking them based on past returns, improves strategy performance. SACEMS each month picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. Since many technical traders use a 10-month SMA (SMA10), we test effectiveness of requiring that each asset pass an SMA10 filter as follows:

1. Baseline – SACEMS as presented at “Momentum Strategy” (no SMA10 filter).
2. Apply an SMA10 filter after asset ranking (SACEMS R-F) – Run Baseline SACEMS and then apply SMA10 filters to dividend-adjusted prices of winners. If a winner is above (below) its SMA10, hold the winner (Cash).
3. Apply an SMA10 filter before asset ranking (SACEMS F-R) – If a SACEMS asset is above (below) its SMA10, apply SACEMS ranking rules to it (exclude it from ranking). If there are not enough ranked assets to populate multi-position SACEMS portfolios, put the positions in Cash.

We focus on compound annual growth rates (CAGR), annual Sharpe ratios and maximum drawdowns (MaxDD) of SACEMS Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios. To calculate Sharpe ratios, we use average monthly 3-month U.S. Treasury bill (T-bill) yield during a year as the risk-free rate for that year. Using monthly dividend-adjusted closing prices for the asset class proxies and the (T-bill) yield for Cash over the period February 2006 through November 2022, we find that: Keep Reading

A subscriber requested an update of April 2015 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 (buy and hold); 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 total (dividend-reinvested) returns for these funds from the first quarter of 1972 (with some early data no longer available from the source retained from the prior test) through the third quarter of 2022 (50.75 years), we find that:

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