Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for February 2023 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for February 2023 (Final)
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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.

Best Trend-following Strategy with Frictions?

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

SACEMS with SMA Filter

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

Optimal Intrinsic Momentum and SMA Intervals Across Asset Classes

What are 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 November 2022, we find that:

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Long-term Tests of Simple X% Rules

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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Combining SMA10 and P/E10 Signals

In response to the U.S. stock market timing backtest in “Usefulness of P/E10 as Stock Market Return Predictor”, a subscriber suggested combining a 10-month simple moving average (SMA10) technical signal with a P/E10 (or Cyclically Adjusted Price-Earnings ratio, CAPE) fundamental signal. Specifically, we test:

  • SMA10 – bullish/in stocks (bearish/in cash) when prior-month stock index level is above (below) its SMA10.
  • SMA10 AND Binary 20-year – in stocks only when both SMA10 and P/E10 Binary 20-year signals are bullish, and otherwise in cash. The latter rule is bullish when last-month P/E10 is below its rolling 20-year monthly average.
  • SMA10 OR Binary 20-year – in stocks when one or both of the two signals are bullish, and otherwise in cash.
  • NEITHER SMA10 NOR Binary 20-year – in stocks only when neither signal is bullish, and otherwise in cash.

We use Robert Shiller’s S&P Composite Index to represent stocks. We consider buying and holding the S&P Composite Index and the standalone P/E10 Binary 20-year strategy as benchmarks. Using monthly data from Robert Shiller, including S&P Composite Index level, associated dividends, 10-year government bond yields and values of P/E10 as available during January 1871 through September 2022, we find that:

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Sector Breadth as Market Return Indicator

Does breadth of equity sector performance predict overall stock market return? To investigate, we relate next-month stock market return to sector breadth (number of sectors with positive past returns) over lookback intervals ranging from 1 to 12 months. We consider the following nine sector exchange-traded funds (ETF) offered as Standard & Poor’s Depository Receipts (SPDR):

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

We use SPDR S&P 500 (SPY) to represent the overall stock market and also relate next-month SPY return to the sign of past SPY return. Using monthly dividend-adjusted returns for SPY and the sector ETFs during December 1998 through October 2022, we find that: Keep Reading

Strong Stock Market Return Reversals and Future Trend

Are days with strong stock market return reversals predictive of the future trend? To check, we we use daily opens, lows, highs and closes of the S&P 500 Index to define the following:

  • Reversal – days with both (1) lows more than 1% below the open and (2) highs more than 1% above the open.
  • Reversal Down – Reversal days for which the close is less than 0.25% above the low.
  • Reversal Up – Reversal days for which the close is less than 0.25% below the high.

These thresholds are arbitrary but generate enough signals for some testing. For each group we plot cumulative index returns over the next 21 trading days. Using S&P 500 Index daily levels as specified during January 1962 (limited by availability of daily lows and highs) through September 2022 (15,293 trading days), we find that: Keep Reading

Require a Subsequent Confirming Signal?

A subscriber asked about a tactic that requires a subsequent confirming signal before triggering a strategy action. To investigate we use the 10-month simple moving average (SMA10) as applied to the S&P 500 Index and exploited via SPDR S&P 500 (SPY) over the available history of SPY. Specifically, we compare performances of the following three strategies:

  1. SPY:SMA10 Baseline – buy (sell) SPY when the S&P 500 Index crosses above (below) its SMA10.
  2. SPY:SMA10 Confirmed – buy (sell) SPY when the S&P 500 Index crosses above (below) its SMA10 and holds the crossing action the next month (suppressing whipsaws).
  3. SPY:SMA10 Tranched – each month allocated half of funds to SPY:SMA10 Baseline and the other half to SPY:SMA10 Confirmed.

We assume that trades execute immediately at monthly closes coincident with signals (requiring slight anticipation of signals). We assume that cash earns the yield on 3-month U.S. Treasury bills and trading frictions for switching between SPY and cash are 0.1%. We focus on average return, standard deviation of returns, return/risk (average return divided by standard deviation of returns), compound annual growth rate (CAGR) and maximum drawdown (MaxDD), all based on monthly data. Using monthly S&P 500 Index closes since March 1992 and monthly dividend-adjusted closes for SPY since January 1993, both through August 2022, we find that: Keep Reading

SPY Breakout/Breakdown Signal Usefulness

A subscriber asked about a tactic employed in some strategies wherein a new X-day high (breakout) triggers a buy and a new Y-day low (breakdown) triggers a sell. Specific X:Y values requested are 55:20, 100:20, 200:20, 100:50 and 200:100. To investigate, we apply this tactic to SPDR S&P 500 (SPY) over its entire history, using unadjusted daily closes to identify breakouts/breakdowns and dividend-adjusted prices to calculate returns. We focus on percentage of days in and out of SPY and average daily return, standard deviation of daily returns and daily return/risk (average daily return divided by standard deviation of daily returns) when in SPY and out of SPY. We also look at number of switches into and out of SPY. We consider execution of trades at the same daily close as the signals and at the next open. Using daily unadjusted and dividend-adjusted SPY opening and closing prices from the end of January 1993 through August 2022, we find that: Keep Reading

Proximity to 52-week High and Short-term Momentum/Reversal

What determines whether a stock will exhibit short-term momentum or short-term reversal? In their May 2022 paper entitled “Short-term Relative-Strength Strategies, Turnover, and the Connection between Winner Returns and the 52-week High”, building upon prior research, Chen Chen, Chris Stivers and Licheng Sun investigate interactions among proximity to 52-week high, share turnover and 1-month return momentum/reversal behaviors for U.S. stocks.  Specifically, at the end of each month t, they form 125 portfolios by:

  1. Sorting stocks into fifths (quintiles) based on return during month t.
  2. Further sorting these quintiles stocks into sub-quintiles based on ratio of price at the end of month t-1 to highest price over the preceding 52 weeks.
  3. Further sorting the sub-quintiles into sub-sub-quintiles based on share turnover during month t.

They then use month t+1 value-weighted returns of the resulting 125 portfolios to evaluate short-term momentum/reversal strategies in multiple ways: buying winners and shorting losers (momentum); buying losers and shorting winners (reversal); and, winners-only or losers-only strategies based on 52-week high proximity. Using the specified trading data for a broad sample of U.S. common stocks priced at least $1 during July 1963 to December 2020, they find that:

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