Technical Trading

“Distance Between Fast and Slow Price SMAs and Stock Returns” finds that extreme distance between a 21-trading day simple moving average (SMA21) and 200-trading day simple moving average (SMA200), as applied to individual U.S. stock price series, may be a useful stock return predictor. “Distance Between Fast and Slow Price SMAs and Country Stock Index Returns” finds that extreme distance between a 30-calendar day simple moving average and 300-calendar day simple moving average, as applied to country stock market indexes, may be a useful index return predictor. Do these findings apply the time series for the S&P 500 Index (SP500)? To investigate, we test relationships between the SMA21-SMA200 ratio for SP500, measured at month-ends, to SP500 future monthly returns. Using daily SP500 closing levels from the end of December 1927 through November 2024, we find that: Keep Reading

Can expected trading frictions, as derived from trading volume forecasts, materially improve active stock portfolio net performance? In the May 2024 version of their paper entitled “Trading Volume Alpha”, flagged by a subscriber, Ruslan Goyenko, Bryan Kelly, Tobias Moskowitz, Yinan Su and Chao Zhang explore optimization of net stock portfolio performance by accounting for expected trading frictions as implied by stock trading volume forecasts. They apply neural networks to forecast stock trading volumes based on past returns/volumes, firm characteristics and various events associated with volume fluctuations (such as earnings releases). They then run experiments that use volume forecasts to quantify expected portfolio-level costs and benefits of trading. For example, they test the net benefit (trading volume alpha) of accounting for expected trading volumes/frictions within each of 153 factor portfolios. Using the specified data for an average 3,500 stocks per day during 2018 through 2022 (a 3-year neural network training subsample and a 2-year testing subsample), they find that: Keep Reading

A reader requested a test of the following hypothesis from the article “Gold’s Bluff – Is a 30 Percent Drop Next?” [no longer available]: “Ironically, gold is more than just a hedge against market turmoil. Gold is actually one of the most accurate indicators of the stock market’s long-term direction. The Dow Jones measured in gold is a forward looking indicator.” To test this assertion, we examine relationships between the spot price of gold and the level of the Dow Jones Industrial Average (DJIA). Using monthly data for the spot price of gold in dollars per ounce and DJIA over the period January 1971 through October 2024, we find that: Keep Reading

Can investors reliably exploit the somewhat opaquely presented strategy summarized in “Using Wilder Volatility Stops to Time the U.S. Stock Market”, which employs Welles Wilder’s Average True Range (ATR) volatility metric to generate buy and sell signals for broad U.S. stock market indexes? To investigate, we each trading day for the SPDR S&P 500 ETF Trust (SPY):

1. Compute true range 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. Calculate ATR as the simple average of the last five true ranges (including the current one).
3. Generate a Wilder Volatility Stop (WVS) by multiplying ATR by a risk factor of 2.5.
4. When out of SPY, buy when it closes above a dynamic trendline defined by a trend minimum plus current WVS (breakout). When in SPY, sell when it closes below a dynamic trendline defined by a trend maximum minus current WVS (breakdown).

We perform the above calculations using raw (not adjusted for dividends) daily SPY prices, but use dividend-adjusted prices to calculate returns. We assume any breakout/breakdown signal and associated SPY-cash switch occurs at the same close. We initially ignore SPY-cash switching frictions, but then test outcome sensitivity to different levels of frictions. We ignore return on cash due to frequency of switching. We further test outcome sensitivity to parameter choices and to an alternative definition of ATR. We use buy-and-hold SPY as a benchmark. Using daily raw and dividend-adjusted prices for SPY during January 1993 (inception) through most of October 2024, we find that: Keep Reading

Short-term reversal is a widely accepted stock return anomaly, with the long-only version glibly termed “buy the dip.” Is short-term reversal readily exploitable? As a live test, we look at the performance of Vesper U.S. Large Cap Short-Term Reversal Strategy ETF (UTRN). This fund seeks to capture bounces of stocks with recent sharp declines by each week:

• Calculating for the 500 largest U.S. stocks a metric similar to the Sharpe ratio but using an asymmetric volatility to find overreaction dips in downtrending stocks (the Chow ratio).
• Initially buying the 25 stocks with the lowest Chow ratios.
• Selling any holdings for which the Chow ratio has risen out of the bottom 50 and replacing them with bottom 25 stocks.

The restriction to large stocks and the differing buy and sell rules suppress trading frictions/portfolio turnover. The benchmark is SPDR S&P 500 ETF Trust (SPY). Using monthly dividend-adjusted returns for UTRN and SPY from the inception of the former in September 2018 through August 2024, we find that: Keep Reading

Can pattern recognition software, combined with news sentiment, identify profitable short-term stock trades? In their July 2024 paper entitled “Technical Patterns and News Sentiment in Stock Markets”, Markus Leippold, Qian Wang and Min Yang test the ability of pattern recognition software (convolutional neural network) to find profitable technical reversal patterns within U.S. and Chinese stock candlestick charts. They consider four pairs of technical patterns: double tops and bottoms; head and shoulders and inverted head and shoulders; broadening tops and bottoms; and, triangle tops and bottoms. They use Bollinger bands to find local maximums and minimums, with the standard deviation multiplier set at 1.1 based on parameter tuning. They augment pattern recognition with news sentiment from Refinitiv for U.S. stocks since 2003 and from Tonglian for Chinese stocks since 2014 during the 10 trading days around each pattern. They identify combined tops as double tops, head and shoulders, broadening tops or triangle tops coupled with negative news and combined bottoms as double bottoms, inverted head and shoulders, broadening bottoms or triangle bottoms coupled with positive news. They first consider each technical pattern as an independent event and measure abnormal returns for holding intervals of 1, 5, 10, 21 and 42 days after a signal. They then examine the performance of a portfolio of events for a 1-day holding interval. They use U.S. stock data from 1992 to 1999, enhanced via two data augmentation strategies, for pattern recognition software training and validation. They then apply the trained software to U.S. stock data from 2000 through 2021 and Chinese stock data from 2005 through 2021. Combined pattern-sentiment test periods are shorter based on availability of sentiment data. Using price series for all U.S. common stocks and Chinese A-shares and news sentiment data as described through 2021, they find that: Keep Reading

Can investors manage cryptocurrency volatility risk with simple trend following rules? In their August 2024 paper entitled “Trend-following Strategies for Crypto Investors”, Trinh Hue Le and Ummul Ruthbah test simple trend following rules on Bitcoin (BTC), Ether (ETH) and the S&P Cryptocurrency LargeCap-Ex. MegaCap Index. Specifically, they hold the cryptocurrency (cash) when its price is above (at of below) its prior-day simple moving average (SMA) of 20, 65, 150 or 200 days. To assess net profitability, they consider trading frictions of 0.1%, 0.25% or 0.5% of amount traded. They further measure correlations between the movements of cryptocurrencies and those of the Nasdaq 100 Index. Using daily prices for the S&P BTC, ETH and Cryptocurrency LargeCap-Ex. MegaCap indexes starting January 2016, January 2017 and January 2019, respectively, all through January 2023, and contemporaneous daily levels of the NASDAQ 100 Index, they find that:

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Does subjective technical analysis truly add value? In their June 2024 paper entitled “The Power Of Price Action Reading”, Carlo Zarattini and Marios Stamatoudis investigate the value added to simple trading rules by the discretionary judgments of an experienced technician for a sample of stocks with: (1) overnight gaps up over 6%; (2) minimum opening price $2.00; and, (3) minimum pre-market volume at least 200,000 shares. One of the paper’s authors (Marios Stamatoudis) is the expert technician. They assess his abilities both to select the best gaps to trade and to micromanage precise entry points, stop-losses and partial exits at predetermined profit points. To screen out confounding information, they remove dates, ticker symbols, sectors/industries, news, and specific prices/volumes from the his inputs, leaving only an anonymized visual 2-year daily price history for each stock. They present gaps to him randomly, not in chronological order. Using daily pre-gap prices and 1-minute post-gap prices for NYSE and NASDAQ stocks satisfying the above three criteria during January 2016 through December 2023 (9,794 events), they find that:

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A subscriber suggested review of the February 2017 paper “Bubbles for Fama”, in which Robin Greenwood, Andrei Shleifer and Yang You assess Eugene Fama’s claim that stock prices do not exhibit bubbles. They define a bubble candidate as a value-weighted U.S. industry or international sector that rises over 100% in both raw and net of market returns over the prior two years, as well as 50% or more raw return over the prior five years. They define a crash as a 40% drawdown within a two-year interval. They also look at characteristics of industry/sector portfolios identified bubble candidates, including level and change in volatility, level and change in turnover, firm age, return on new versus old companies, stock issuance, book-to-market ratio, sales growth, price-earnings ratio and price acceleration (abruptness of price run-up). They evaluate timing strategies that switch from an industry portfolio to either the market portfolio or cash (with risk-free yield) based on a price run-up signal, or a signal that combines price run-up and other characteristics. Their benchmark is buying and holding the industry portfolio. Using value-weighted returns for 48 U.S. industries (based on SIC code) during January 1926 through March 2014 and for 11 international sectors (based on GICS codes) during October 1985 through December 2014, they find that:

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A subscriber asked for confirmation that the strategy described at “Meb Faber’s 12-Month High Switch” (the strategy) is attractive. This strategy at each monthly close:

• Calculates the 12-month high for: SPDR S&P 500 ETF Trust (SPY), iShares MSCI EAFE ETF (EFA), Vanguard Real Estate Index Fund ETF Shares (VNQ), SPDR Gold Shares (GLD) and Invesco Optimum Yield Diversified Commodity Strategy No K-1 ETF (PDBC) based on dividend-adjusted prices.
• Allocates 20% of the portfolio to each asset currently within 5% of its 12-month high.
• Allocates any remaining funds to iShares 7-10 Year Treasury Bond ETF (IEF) if IEF is within 5% of its 12-month high, or otherwise to cash with return estimated by the yield on 3-month U.S. Treasury bills (T-bill).
• Rebalances to equal weights each month and applies trading frictions of 0.1% per 1-way trade for any switches in holdings.

To investigate, we replicate the strategy, substituting Invesco DB Commodity Index Tracking Fund (DBC) for PDBC to get a longer sample period that includes the 2008-2009 financial crisis. We use an equal-weighted (EW), monthly rebalanced combination of SPY, EFA, VNQ, GLD and DBC as a benchmark. We apply 0.1% frictions to ETF switches but not to simple rebalances, which are generally small. Using monthly dividend-adjusted prices for SPY, EFA, VNQ, GLD and DBC during February 2006 (DBC inception) through June 2024, we find that: Keep Reading