Technical Trading

“Distance Between Fast and Slow Price SMAs and Stock Returns” finds that extreme distance between a 21-trading day simple moving average (SMA) and 200-trading day SMA, as applied to individual U.S. stock price series, may be a useful return predictor. Does this finding apply to non-U.S. stock market indexes? In their December 2023 paper entitled “Market Timing with Moving Average Distance: International Evidence”, Menachem Abudy, Guy Kaplanski and Yevgeny Mugerman test ability of the distance between fast and slow SMAs to predict future returns across 92 international stock market indexes. Specifically, they each month:

• Measure moving average distance (MAD) for each index as the ratio of its 30-calendar day SMA to its 300-calendar day SMA in local currencies.
• Sort the indexes according to MAD into fifths (quintiles) or tenths (deciles).
• Reform an equal-weighted hedge portfolio that is long indexes in the top quintile or decile with MAD values above one and short indexes in the bottom quintile or decile with MAD values below one.
• Adjust portfolio returns to U.S. dollars via local currency exchange rates.

They consider the full sample of 92 indexes and three subsamples: (1) 46 countries with the highest United Nations development ratings; (2) the MSCI 25 developed markets; and, (3) the MSCI 30 emerging markets. Their benchmarks are buy-and-hold the MSCI World Index (large and mid-size firms in 23 developed countries) and the S&P Global 1200 (30 markets representing about 70% of global market capitalization). Using daily levels of 92 international stock market indexes as available since June 1980, associated U.S. dollar exchange rates and international stock factor model returns, all through November 2020, they find that: Keep Reading

Does degree of difference between fast and slow simple moving averages (SMA) for a stock price series predict future stock return? In the December 2023 revision of their paper entitled “Moving Average Distance as a Predictor of Equity Returns”, Doron Avramov, Guy Kaplanski and Avanidhar Subrahmanyam test distance between a 21-day SMA (SMA21) and 200-day SMA (SMA200) for the stock price series as a return predictor. Specifically, they each month:

• Calculate for each stock the SMA21-to-SMA200 ratio.
• Sort stocks into tenths (deciles) by this ratio.
• Calculate the standard deviation of the ratio across all stocks.
• Select stocks from the top decile with ratios greater than one plus one standard deviation for a long portfolio. Select stocks from the bottom decile with ratios less than one minus one standard deviation for a short portfolio. 
• Specify the Moving Average Distance (MAD) for a stock as 1 if it is in the long portfolio, -1 if it is in the short portfolio, and 0 otherwise. Stocks in the two portfolios are market capitalization-weighted.

They then assess the magnitude and reliability of MAD portfolio performance. They estimate breakeven trading frictions for MAD portfolios based on zero alpha relative to different factor models of stock returns. To assess uniqueness of MAD indications, they control for 18 firm characteristics and several technical indicators. Using daily prices adjusted for splits and dividends for publicly traded U.S. common stocks priced at least $5 during July 1977 through December 2018, they find that:

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Are return reversals especially strong for lottery stocks? In their October 2023 paper entitled “Maxing Out Short-term Reversals in Weekly Stock Returns”, Chen Chen, Andrew Cohen, Qiqi Liang and Licheng Sun investigate return reversals for lottery stocks, those with high recent maximum daily returns (MAX). Specifically, for their main calculations, they each week:

• For each stock, calculate MAX during the week before last.
• Sort stocks into fifths (quintiles) based on MAX values.
• Within each MAX quintile, further sort stocks based on their last-week returns.

They then use these sorts to explore interactions between effects of past MAX and effects of past returns on next-week returns. Using weekly returns for U.S. common stocks during July 1963 to December 2022, they find that: Keep Reading

Does market volatility predictably affect returns to simple moving average (SMA) trend-following strategies? In their November 2023 paper entitled “Market Volatility and the Trend Factor”, Ming Gu, Minxing Sun, Zhitao Xiong and Weike Xu investigate how stock market volatility affects multi-SMA trend factor profitability. They first assess significance of the trend factor premium, as follows:

• For each stock at the close on the last trading day of each month:
  • Compute SMAs of prices for lookback intervals of 3, 5, 10, 20, 50, 100, 200, 400, 600, 800 and 1000 trading days, and divide each SMA by the end price.
  • Starting five years into the sample period (1931), regress next-month stock returns on corresponding monthly SMA ratios over the past 60 months.
  • Average the SMA ratio regression coefficients separately over the past 12 months to estimate next-month coefficients and apply these coefficients to estimate next-month return.
• At the end of each month, sort all stocks into tenths, or deciles, based on estimated next-month returns and form a trend factor hedge portfolio that is long (short) the equal-weighted top (bottom) decile. The trend factor premium is the monthly gross return for this portfolio.

They then assess how trend factor hedge portfolio returns interact with monthly stock market return volatility (standard deviation of monthly value-weighted market returns over the past 12 months) by specifying volatility has high or low when its prior-month value is above or below the full-sample median. Using data for all listed U.S. common stocks, excluding those priced below $5 or in the lowest tenth of NYSE market capitalizations, during January 1926 through December 2022, they find that:

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Should qualified investors count on global macro (GM) and managed futures (MF, or alternatively CTA for commodity trading advisors) hedge funds to beat the market? In their November 2023 paper entitled “Global Macro and Managed Futures Hedge Fund Strategies: Portfolio Differentiators?”, Rodney Sullivan and Matthew Wey assess the performances of GM and MF hedge fund categories, defined as:

• GM – try to anticipate how political trends and global economic activity will affect valuations of global equities, bonds, currencies and commodities.
• MF – rely systematic trading programs based on historical prices/market trends across stocks, bonds, currencies and commodities.

For comparison, they also look at the long-short equity (LSE) hedge fund category. They decompose category returns into components driven by exposures to U.S. stock and bond market return factors, other factor premiums and unexplained alpha. They focus on how fund categories have changed since the 2008 financial crisis, emphasizing performances during market downtowns. Using index returns from Hedge Fund Research (equal-weighted) and Credit Suisse (asset-weighted) during January 1994 through December 2022, they find that:

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Is there an exploitable way to find which stocks lead and which stocks lag in returns? In their October 2023 paper entitled “Detecting Lead-Lag Relationships in Stock Returns and Portfolio Strategies”, Álvaro Cartea, Mihai Cucuringu and Qi Jin test three ways to measure and exploit linear and non-linear lead-lag relationships in individual stock returns via three steps:

1. Use daily returns for stocks over the last 60 trading days to compute all pairwise lead-lag relationships using one of two cross-correlation methods (linear relationships) or a third method based on the Levy-area of pairwise asset returns (linear and non-linear relationships).
2. Rank all stocks from strongest leaders to weakest followers for each of the three sets of pairwise relationships.
3. Each day for each set of ranks, if the sign of the average prior-day return of the strongest 20% of leader stocks is positive (negative):
   • Buy (sell) an equal-weighted portfolio of the weakest 20% of follower stocks.
   • Sell (buy) an offsetting position in SPDR S&P 500 (SPY) to approximate market neutrality.

Their universe each trading day is the top 25% of market capitalizations cross NYSE, NASDAQ and AMEX, excluding stocks with missing returns during the last 60 trading days. To test leader-follower relationship persistence, they consider also measurement frequencies of every two days, weekly, every two weeks, every three weeks and monthly. Using daily prices, market capitalizations, trading volumes and sectors of listed U.S. stocks during July 1963 through December 2022, they find that:

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Are there ways to revive the fading performance of the short-term reversal (STR) strategy, which is long stocks with the lowest returns last month and short stocks with the highest? In their September 2023 paper entitled “Reversing the Trend of Short-Term Reversal”, David Blitz, Bart van der Grient and Iman Honarvar investigate revival of the strategy by suppressing its conflicts with either industry momentum or general momentum. Specifically, at the end of each month, they sort stocks into fifths (quintiles) in three ways:

1. Generic STR – sorting on simple last-month returns.
2. Industry-adjusted STR – sorting on last-month returns minus respective last-month industry returns.
3. Residual STR – sorting on 3-factor alphas (adjusting for market, size and book-to-market factors over rolling 36-month intervals), scaled for volatility over the past 36 months.

For each approach each month, they form a hedge portfolio that is long (short) the quintile with the lowest (highest) past performances. For all three approaches, they impose regional neutrality by sorting stocks separately within North America, Europe and the Pacific region. They also consider developed and emerging markets segmentation. Using end-of-month data for all stocks in the MSCI World index during December 1985 through December 2022 (an average of 1,745 stocks per year), they find that: Keep Reading

Referring to commonly used Relative Strength Index (RSI) oversold parameter settings, but seeking to avoid exiting rebounds too soon, a subscriber asked about performances of the following four rules as applied to SPDR S&P 500 ETF Trust (SPY):

1. Buy when daily RSI 14 falls under 30 and hold for six months.
2. Buy when daily RSI 14 rises above 30 and hold for six months.
3. Buy when weekly RSI 14 falls under 30 and hold for six months.
4. Buy when weekly RSI 14 rises above 30 and hold for six months.

To investigate, we use a 126-day (26-week) holding interval for daily (weekly) calculations. We assume that overlapping signals reset the clock. In other words, if there are buy signals while already in SPY, we extend the holding interval to six months after the last overlapping buy signal. We ignore frictions for switching between SPY and cash and assume no return on cash. We ignore tax implications of trading. We use buy-and-hold SPY as a benchmark. Key metrics are compound annual growth rate (CAGR) and maximum drawdown (MaxDD), but we also look at average 6-month returns and return volatilities while in SPY. Using daily and weekly raw (for RSI calculations) and dividend-adjusted (for return calculations) SPY closing prices from the end of January 1993 through mid-August 2023, we find that:

A subscriber requested comparison of four variations of an “Ivy 5” asset class allocation strategy, as follows:

1. Ivy 5 EW: Assign equal weight (EW), meaning 20%, to each of the five positions and rebalance annually.
2. Ivy 5 EW + SMA10: Same as Ivy 5 EW, but take to cash any position for which the asset is below its 10-month simple moving average (SMA10).
3. Ivy 5 Volatility Cap: Allocate to each position a percentage up to 20% such that the position has an expected annualized volatility of no more than 10% based on daily volatility over the past month, recalculated monthly. If under 20%, allocate the balance of the position to cash.
4. Ivy 5 Volatility Cap + SMA10: Same as Ivy 5 Volatility Cap, but take completely to cash any position for which the asset is below its SMA10.

To perform the tests, we employ the following five asset class proxies:

iShares 7-10 Year Treasury Bond ETF (IEF)
SPDR S&P 500 ETF Trust (SPY)
Vanguard Real Estate Index Fund (VNQ)
iShares MSCI EAFE ETF (EFA)
Invesco DB Commodity Index Tracking Fund (DBC)

We consider monthly performance statistics, annual performance statistics, and full-sample compound annual growth rate (CAGR) and maximum drawdown (MaxDD). Annual Sharpe ratio uses average monthly yield on 3-month U.S. Treasury bills (T-bills) as the risk-free rate. The DBC series in combination with the SMA10 rule are limiting with respect to sample start date and the first return calculations. Using daily and monthly dividend-adjusted closing prices for the five asset class proxies and T-bill yield as return on cash during February 2006 through July 2023, we find that:

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Why is trend following so persistently popular among investors? In their March 2022 paper entitled “A Guide to Trend Following Strategies”, Stuart Broadfoot and Daniel Leveau describe popular trend identification methods and provide an example of how to build/test a multi-asset class trend following strategy in four steps. Using trend following index data during January 2000 through May 2022 and prices for 52 futures contract series during January 2000 through January 2022, they find that: Keep Reading