Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for October 2020 (Final)
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Momentum Investing Strategy (Strategy Overview)

Allocations for October 2020 (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.

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. Using monthly dividend-adjusted returns for SPY and the sector ETFs during December 1998 through September 2020, we find that: Keep Reading

Distinct and Predictable U.S. and ROW Equity Market Cycles?

A subscriber asked: “Some pundits have noted that U.S. stocks have greatly outperformed foreign stocks in recent years. What does the performance of U.S. stocks vs. foreign stocks over the last N years say about future performance?” To investigate, we use the S&P 500 Index (SP500) as a proxy for the U.S. stock market and the ACWI ex USA Index as a proxy for the rest-of-world (ROW) equity market. We consider three ways to relate U.S. and ROW equity returns:

  1. Lead-lag analysis between U.S. and ROW annual returns to see whether there is some cycle in the relationship.
  2. Multi-year correlations between U.S. and next-period ROW returns, with periods ranging from one to five years.
  3. Sequences of end-of-year high water marks for U.S. and ROW equity markets.

For the first two analyses, we relate the U.S. stock market to itself as a control (to assess whether ROW market behavior is distinct). Using end-of-year levels of the S&P 500 Index and the ACWI ex USA Index during 1987 (limited by the latter) through 2019, we find that: Keep Reading

Optimal SMA Lookback Interval?

Is a 10-month simple moving average (SMA10) the best SMA for long-term crossing signals? If not, is there some other optimal SMA lookback interval? To check, we compare performance statistics for SMA crossing signals generated by lookback intervals ranging from 2 (SMA2) to 48 months (SMA48), as applied to the S&P 500 Index. Using monthly S&P 500 Index closes, monthly S&P 500 Composite Index dividend data from Robert Shiller and monthly average yields for 3-month Treasury bills (T-bills) during January 1928 (except January 1934 for T-bills) through June 2020, we find that: Keep Reading

Mitigating Impact of Price Turning Points on Trend Following

Is there a way to mitigate adverse impact of price trajectory turning points (trend changes) on performance of intrinsic (absolute or time series) momentum strategies? In their May 2020 paper entitled “Breaking Bad Trends”, Ashish Garg, Christian Goulding, Campbell Harvey and Michele Mazzoleni measure impact of turning points on time series momentum strategy performance across asset classes. They define a turning point as a month for which slow (12-month or longer lookback) and fast (3-month or shorter lookback) momentum signals disagree on whether to buy or sell. They test a dynamic strategy to mitigate trend change impact based on turning points defined by disagreement between 12-month (slow) and 2-month (fast) momentum signals. Specifically, their dynamic strategy each month:

  1. For each asset, measures slow and fast momentum as averages of monthly excess returns over respective lookback intervals.
  2. Specifies the trend condition for each asset as: (1) Bull (slow and fast signals both non-negative); (2) Correction (slow non-negative and fast negative); Bear (slow and fast both negative); and, Rebound (slow negative and fast non-negative). For Bull and Bear (Correction and Rebound) conditions, next-month return is the same (opposite in sign) for slow and fast signals.
  3. After trend changes (Corrections and Rebounds separately), empirically determines with at least 48 months of historical data optimal weights for combinations of positions based on slow and fast signals.

They compare performance of this dynamic strategy with several conventional (static) time series momentum strategies, with each competing strategy retrospectively normalized to 10% test-period volatility. They test strategies on 55 futures, forwards and swaps series spanning four asset classes, with returns based on holding the nearest contract and rolling to the next at expiration. Using monthly returns for futures, forwards and swaps for 12 equity indexes, 10 bond indexes, 24 commodities and 9 currency pairs as available during January 1971 through December 2019, they find that:

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Combining Market Trend and Chicago Fed NFCI Signals

In response to “Exploiting Chicago Fed NFCI Predictive Power”, which tests practical use of the Federal Reserve Bank of Chicago’s National Financial Conditions Index (NFCI) for U.S. stock market timing, a subscriber suggested combining this strategy with stock market trend as in “Combine Market Trend and Economic Trend Signals?”. To investigate, we use the 40-week simple moving average (SMA40) for the S&P 500 Index to measure stock market trend. We then test two strategies that are each week in SPDR S&P 500 (SPY) or cash (U.S. Treasury bills, T-bills), as follows:

  1. Combined (< Mean): hold SPY (cash) when either: (a) prior-week S&P 500 Index is above (below) its SMA40; or, (b) prior-week change in NFCI is below (above) its mean since since the beginning of 1973.
  2. Combined (< Mean+SD): hold SPY (cash) when either: (a) prior-week S&P 500 Index is above (below) its SMA40; or, (b) prior-week change in NFCI is below (above) its mean plus one standard deviation of weekly changes in NFCI since the beginning of 1973.

The return week is Wednesday open to Wednesday open (Thursday open when the market is not open on Wednesday) per the NFCI release schedule. SMA40 calculations are Tuesday close to Tuesday close to ensure timely availability of signals before any Wednesday open trades. We assume SPY-cash switching frictions are a constant 0.1% over the sample period. Using weekly NFCI data since January 1973, weekly S&P 500 Index levels since April 1992, weekly dividend-adjusted opens of SPY and weekly T-bill yield since February 1993 (limited by SPY), all as specified through April 2020, we find that:

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Long-term SMA and TOTM Combination Strategy

“Turn-of-the-Month Effect Persistence and Robustness” indicates that average absolute returns during the turn-of-the-month (TOTM) are strong for both bull and bear markets. Does a strategy of capturing all bull market returns and TOTM returns only during bear markets perform well? To investigate, we apply four strategies to S&P Depository Receipts (SPY) as a tradable proxy for the stock market:

  1. Buy and hold SPY.
  2. Hold SPY (cash) when SPY closes above (below) its 200-day simple moving average (SMA200).
  3. Hold SPY from the close five trading days before through the close four trading days after the last trading day of each month and cash at all other times (TOTM).
  4. Hold SPY when SPY closes above its 200-day SMA and otherwise use the TOTM strategy (SMA200 or TOTM).

We explore sensitivities of these strategies to a range of one-way SPY-cash switching frictions, with baseline 0.1%. Using daily dividend-adjusted SPY from the end of January 1993 through late May 2020 and contemporaneous 3-month Treasury bill (T-bill) yields, 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 over the past two to 12 months. Since SMA rules use price levels and IM rules use returns, IM lookback interval N corresponds to SMA lookback interval N+1. For example, a 6-month IM lookback uses the same start and stop points as a 7-month SMA lookback. 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 April 2020, we find that:

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10-month vs. 40-week vs. 200-day SMA

A reader proposed: “I would love to see a backtest pitting a 10-month simple moving average (SMA) against a 200-day SMA for SPDR S&P 500 (SPY). I assume trading costs would go through the roof on the latter, but do performance gains offset additional costs?” Others asked about a 40-week SMA. To investigate, we use the three SMAs to time SPY since its inception and compare results. Specifically, we buy (sell) SPY at the close as it crosses above (below) the SMA, anticipating crossing signals such that trades occur at the close on the signal day (assuming calculations can occur just before the close). The baseline SMA calculation series is dividend-adjusted, but we also check use of unadjusted prices and underlying S&P 500 Index levels. We assume return on cash is the 3-month U.S. Treasury bill (T-bill) yield (ignoring settlement delays). We use a baseline 0.1% one-way SPY-cash switching frictions and test sensitivity to frictions ranging from 0.0% to 0.5% (but assume dividend reinvestment is frictionless). Using daily dividend-adjusted and unadjusted closes for SPY, daily closes of the S&P 500 Index and daily T-bill yield from the end of January 1993 through mid-April 2020, we find that:

Keep Reading

SMA Signal Effectiveness Across Stock ETFs

Simple moving averages (SMA) are perhaps the most widely used and simplest market regime indicators. For example, many investors estimate that a stock index, exchange-traded fund (ETF) or individual stock priced above (below) its 200-day SMA is in a good (bad) regime. Do SMA signals/signal combinations usefully and consistently distinguish good and bad regimes across different kinds of U.S. stock ETFs? To investigate, we test regime signals of 50-day, 100-day and 200-day SMAs and combinations of them across broad equity market (DIASPYIWBIWM and QQQ), equity style (IWDIWFIWN and IWO) and equity sector (XLBXLEXLFXLIXLKXLPXLUXLV and XLY) ETFs. We consider also three individual stocks: Apple (AAPL), Berkshire Hathaway (BRK-B) and Wal-Mart (WMT). We focus on compound annual growth rate (CAGR) for comparisons, but also look at a few other performance metrics. Using daily dividend-adjusted closes of these 18 ETFs and three stocks during late July 2000 (limited by IWN and IWO) through late April 2020, we find that: Keep Reading

Add Position Stop-gain to SACEMS?

Does adding a position take-profit (stop-gain) rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by harvesting some upside volatility? SACEMS each months 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. To investigate the value of stop-gains, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return rises above a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month maximum returns for the specified assets during February 2006 through March 2020, we find that: Keep Reading

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