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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.

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 10/1/21, we find that: Keep Reading

DJIA-Gold Ratio as a Stock Market Indicator

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 August 2021, we find that: Keep Reading

Comparing the Sahm Indicator and the Yield Curve

In response to “Combining SMA10 and Sahm Indicator”, a subscriber asked for a comparison of signals generated by the Sahm Recession Indicator (Sahm) and by yield curve inversion. The former signals a recession when the 3-month simple moving average (SMA) of the U.S. unemployment rate is at least 0.5% higher than its low during the last 12 months. The latter signals a recession when the yield on the 3-month U.S. Treasury bill (T-bill) rises above the yield on the 10-year U.S. Treasury note (T-note). To investigate, we calculate average monthly returns and standard deviations of monthly returns for the S&P 500 Index (SP500):

  • When Sahm does not indicate a recession and, separately, when it does.
  • When the yield curve does not indicate a recession and, separately, when it does.
  • When SP500 is below its 10-month SMA (SMA10) and, separately, when it is above (for additional perspective).

Using end-of-month levels of SP500 since March 1959, Sahm levels since inception in December 1959 (history vintage 8/6/2021) and T-bill and T-note yields since December 1959, all through July 2021, we find that:

Keep Reading

Optimal Intrinsic Momentum and SMA Intervals Across Asset Classes

What are the optimal intrinsic/absolute/time series momentum (IM) and simple moving average (SMA) lookback intervals for different asset class proxies? To investigate, we use data for the following eight asset class exchange-traded funds (ETF), plus Cash:

  • PowerShares DB Commodity Index Tracking (DBC)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
  • 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)

For IM tests, we invest in each ETF (Cash) when its return over the past one to 12 months is positive (negative). For SMA tests, we invest in each ETF (Cash) when its price is above (below) its average monthly price at the ends of the last two to 12 months. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key metrics for comparing different IM and SMA lookback intervals since earliest ETF data availabilities based on the longest IM lookback interval. Using monthly dividend-adjusted closing prices for the asset class proxies and the yield for Cash over the period July 2002 (or inception if not available by then) through July 2021, we find that:

Keep Reading

Combining SMA10 and Sahm Indicator

A subscriber asked about a stock market timing strategy that combines the market 10-month simple moving average (SMA10) and the Sahm Recession Indicator (Sahm), which signals the start of a recession when the 3-month SMA of the U.S. unemployment rate is at least 0.5% higher than its low during the last 12 months. Specifically, the strategy:

  • Holds the S&P 500 Index (SP500) unless it is below its SMA10 and Sahm first signals a recession.
  • Subsequently holds cash until SP500 crosses above its SMA10.

To investigate, we compare three alternative strategies:

  1. SP500 – buy and hold the index.
  2. SMA10 – hold the index only while it is above its SMA10 and otherwise hold cash.
  3. SMA10+Sahm – combined signals as specified above.

We focus on average monthly return, standard deviation of monthly returns, monthly reward/risk (average return divided by standard deviation), compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. Using end-of-month levels of SP500 since March 1959, Shiller’s monthly SP500 dividends (to estimate SP500 total returns) since January 1960, Sahm since inception in December 1959 (history vintage 8/6/2021) and T-bill yield since December 1959, all through July 2021, we find that:

Keep Reading

Testing a QQQ Swing Trade Strategy

A subscriber requested review of a swing trade strategy that buys and sells Invesco QQQ Trust (QQQ) according to the following rules:

  • Buy at the close when it is either Monday or Tuesday and QQQ (Close-Low)/(High-Low) is 0.15 or less.
  • Subsequently sell at the close when it is higher than the prior-day high.

To investigate, to simplify portfolio cash management, we assume that there are no overlapping trades (if a position opens on Monday, another position does not open on Tuesday). We further assume that cash earns the 3-month U.S. Treasury bills (T-bill) yield when not in QQQ and that frictions for switching between T-bills and QQQ are 0.10% of trade value. Using daily high, low, close and dividend-adjusted close (to calculate returns) for QQQ and daily T-bill close during March 10, 1999 (QQQ inception) through August 5, 2021, we find that:

Predicting Stock Market Crashes with Interpretable Machine Learning

Can machine learning-generated stock market crash predictions be amenable to human interpretation? In their June 2021 paper entitled “Explainable AI (XAI) Models Applied to Planning in Financial Markets”, Eric Benhamou, Jean-Jacques Ohana, David Saltiel and Beatrice Guez apply a gradient boosting decision tree (GBDT) to 150 technical, fundamental and macroeconomic inputs to generate daily predictions of short-term S&P 500 Index crashes. They define a crash as a 15-day S&P 500 Index return below its historical fifth percentile within the training dataset. The 150 model inputs encompass:

  1. Risk aversion metrics such as asset class implied volatilities and credit spreads.
  2. Price indicators such as returns, major stock index Sharpe ratios, distance from a long-term moving average and and equity-bond correlations.
  3. Financial metrics such as 12-month sales growth and price-to-earnings ratio forecasts.
  4. Macroeconomic indicators such Citigroup regional and global economic surprise indexes.
  5. Technical indicators such as market breath and index put-call ratio.
  6. Interest rates such as 10-year and 2-year U.S. Treasury yields and break-even inflation level.

They first rank and filter the 150 inputs based on GBDT to discard about two thirds of the variables. They then apply the Shapley value solution concept to identify the most important of the remaining variables and thereby support interpretation of methodology outputs. Using daily values of the 150 model inputs and daily S&P 500 Index roll-adjusted futures prices from the beginning of January 2003 through mid-January 2021 (with data up to January 2019 used for training, the next year for validation and the rest for testing), they find that:

Keep Reading

Return Recency as Stock Return Predictor Worldwide

Does the recency effect evident for U.S. stock returns carry over to stocks globally? In their May 2021 paper entitled “Chronological Return Ordering and the Cross-Section of International Stock Returns”, Nusret Cakici and Adam Zaremba examine whether the recency effects holds among stocks worldwide. Their measure of recency (Chronological Return Ordering, CRO) for each stock each month is the correlation between daily returns and number of days until the end of the month. Low (high) CRO values indicate relatively high (low) recent returns and relatively low (high) older returns. Low (high) CRO values imply low (high) future returns. To measure the recency effect, they each month sort stocks into tenths, or deciles, and reform an equal-weighted or value-weighted hedge portfolio that is long (short) the decile with highest (lowest) recency correlations. Using daily and monthly returns and other data for stocks from 49 countries (23 developed markets and 26 emerging ones) as available starting January 1990 through December 2020 (a total of 92,680 stocks, 62,495  from developed markets and 30,185 from emerging markets), they find that: Keep Reading

Return Recency as U.S. Stock Return Predictor

Do naive investors overvalue (undervalue) stocks with relatively high (low) recent returns, thereby causing exploitable overpricing (underpricing)? In the April 2019 version of his paper entitled “The Impact of Recency Effects on Stock Market Prices”, Hannes Mohrschladt devises and tests a measure of this recency effect based on correlation between daily returns during a month and the number of days until the end of the month. For stocks with low (high) values of this correlation:

  • Recent returns are relatively high (low) and older returns are relatively low (high).
  • Naive investors overvalue (undervalue) such stocks, which therefore become overpriced (underpriced).
  • There is an opportunity to exploit this effect by buying (selling) stocks with high (low) values of this variable.

His principal test is to each month sort stocks into tenths, or deciles, by prior-month recency correlation and reform an equal-weighted or value-weighted hedge portfolio that is long (short) the decile with high (low) recency correlations. He also considers a 1-year lookback interval using monthly returns rather than a 1-month interval using daily returns for calculation of recency correlations. Using daily and monthly returns and other data for a broad sample of U.S. common stocks during January 1926 through December 2016, he finds that: Keep Reading

Effectiveness of Buying the Dip

Is buy-the-dip (BTD) a reliably attractive stock market timing approach? In their April 2021 paper entitled “Buy the Dip”, Thomas Shohfi and Majeed Simaan devise and test various BTD strategies as applied to SPDR S&P 500 ETF Trust (SPY), as follows:

  1. BTD with Lump Sum – 54 variations in which the investor progressively allocates a fixed percentage of an initial lump sum to SPY whenever the last real monthly return on SPY is below a specified threshold. Variations derive from different fixed allocation percentages and different return thresholds.
  2. BTD with Monthly Inflows – five variations in which the investor receives cash flows at the beginning of each month and moves one monthly increment from cash to SPY whenever its prior-day return is below a specified threshold. Variations derive from different return thresholds.
  3. BTD with MaxDD – nine variations in which the investor receives cash flows at the beginning of each month and initiates the strategy with 12 months of savings, allocating all cash savings to SPY whenever SPY drops below a maximum drawdown (MaxDD) threshold over a past rolling window. Variations derive from different MaxDD thresholds and different rolling window lengths.

They consider four strategy implementation dates, two associated beginnings of bull markets (January 1994 and January 2010) and two associated with beginnings of bear markets (January 2000 and January 2008). They use real (inflation-adjusted) returns on SPY and also deflate value of cash holdings accordingly. They focus on two strategy performance criteria: (1) terminal wealth at the end of 2020; and, (2) Sortino ratio. Respective benchmarks are passive strategies that allocate all cash to SPY as soon as the cash is available. They assume zero trading frictions and zero return on cash. Using daily dividend-adjusted SPY returns and monthly U.S. Consumer Price Index levels for inflation adjustments during January 1994 through December 2020, they find that: Keep Reading

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