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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.

Reducing Downside Risk of Trend Following Strategies

How can investors suppress the downside of trend following strategies? In their July 2019 paper entitled “Protecting the Downside of Trend When It Is Not Your Friend”, flagged by a subscriber, Kun Yan, Edward Qian and Bryan Belton test ways to reduce downside risk of simple trend following strategies without upside sacrifice. To do so, they: (1) add an entry/exit breakout rule to a past return signal to filter out assets that are not clearly trending; and, (2) apply risk parity weights to assets, accounting for both their volatilities and correlations of their different trends. Specifically, they each month:

  • Enter a long (short) position in an asset only if the sign of its past 12-month return is positive (negative), and the latest price is above (below) its recent n-day minimum (maximum). Baseline value for n is 200.
  • Exit a long (short) position in an asset only if the latest price trades below (above) its recent n/2-day minimum (maximum), or the 12-month past return goes negative (positive).
  • Assign weights to assets that equalize respective risk contributions to the portfolio based on both asset volatility and correlation structure, wherein covariances among assets adapt to whether an asset is trending up or down. They calculate covariances based on monthly returns from an expanding (inception-to-date) window with baseline 2-year half-life exponential decay.
  • Impose a 10% annual portfolio volatility target.

Their benchmark is a simpler strategy that uses only past 12-month return for trend signals and inverse volatility weighting with annual volatility target 40% for each asset. Their asset universe consists of 66 futures/forwards. They roll futures to next nearest contracts on the first day of the expiration month. They calculate returns to currency forwards using spot exchange rates adjusted for carry. Using daily prices for 23 commodity futures, 13 equity index futures, 11 government bond futures and 19 developed and emerging markets currency forwards as available during August 1959 through December 2017, they find that: Keep Reading

Intrinsic Momentum or SMA for Avoiding Crashes?

A subscriber suggested comparing intrinsic momentum (IM), also called absolute momentum and time series momentum, to simple moving average (SMA) as alternative signals for equity market entry and exit. To investigate across a wide variety of economic and market conditions, we measure the long run performances of entry and exit signals from IMs over past intervals of one to 12 months (IM1 through IM12) and SMAs ranging from 2 to 12 months (SMA2 through SMA12). We consider two cases for IM signals and one case for SMA signals, as applied to the S&P 500 Index as a proxy for the stock market and the 3-month U.S. Treasury bill (T-bill) as a proxy for cash (the risk-free rate). The three rule types are therefore:

  1. IMs Case 1 – in stocks (cash) when past index return is positive (negative).
  2. IMs Case 2 – in stocks (cash) when average monthly past index return is above (below) average monthly T-bill yield over the same interval.
  3. SMAs – in stocks (cash) when the index is above (below) the SMA.

We estimate S&P 500 Index monthly total returns using monthly dividend yield calculated from Shiller data. This estimation does not affect index timing signals. We focus on net compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance metrics, with baseline stocks-cash switching frictions 0.2%. We use buying and holding the S&P 500 Index (B&H) as a benchmark. Using monthly closes of the S&P 500 Index during December 1927 through November 2019 (92 years), and contemporaneous monthly index dividend and T-bill yields, we find that:

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Bollinger Bands: Buy Low and Sell High?

Are Bollinger Bands (BB) useful for deciding when to buy low and when to sell high the overall U.S. stock market? In other words, can an investor beat a buy-and-hold strategy by systematically buying (selling) when the market crosses below (above) the lower (upper) BB? To check, we examine the historical behavior of BBs around the 21-trading day (one month) simple moving average (SMA) of S&P 500 SPDR (SPY) as a tradable proxy for the U.S. stock market, with 3-month Treasury bill (T-bill) yield as the return on cash when not in SPY. We consider BB settings ranging from 0.5 to 2.5 standard deviations of daily returns, calculated over the same trailing 21 trading days. We focus on net compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio (with average daily T-bill yield during a year as the risk-free rate for that year) as key performance metrics. Baseline SPY-cash switching frictions are 0.2%. Using daily unadjusted closes of of SPY (to calculate BBs), dividend-adjusted closes of SPY (to calculate total returns) and contemporaneous T-bill yield from the end of January 1993 (SPY inception) through late November 2019, we find that: Keep Reading

Hold Stocks Only After All-time Market Highs?

A subscriber asked for verification of the finding in “Is Buying Stocks at an All-Time High a Good Idea?” that it is not only a good idea, but a great one, including comparison to a moving average crossover rule. To investigate, we use the S&P 500 Index as a proxy for the U.S. stock market and test a strategy that holds SPDR S&P 500 (SPY) when the S&P 500 Index stands at an all-time high at the end of last month and otherwise holds Vanguard Long-Term Treasury Fund Investor Shares (VUSTX). We compare results to buying and holding SPY, buying and holding VUSTX, and holding SPY (VUSTX) when the S&P 500 Index is above (below) its 10-month simple moving average (SMA10) at the end of last month. We assume 0.1% switching frictions. We compute average net monthly return, standard deviation of monthly returns, net monthly Sharpe ratio (with monthly T-bill yield as the risk-free rate), net compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key strategy performance metrics. We calculate the number of switches for each scenario to indicate sensitivities to switching frictions and taxes. Using monthly closes for the S&P 500 Index, SPY and VUSTX during January 1993 (inception of SPY) through October 2019, we find that:

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Using RSI(2) to Trade Leveraged ETFs

A subscriber asked for an update on the effectiveness of applying a two-period Relative Strength Index, RSI(2), to leveraged exchange-traded funds (ETF), with two pairs of trade entry (oversold) and exit (overbought) settings:

  1. Buy when RSI(2) falls below 10 and sell when it subsequently rises over 90 (10-90).
  2. More conservatively, buy when RSI(2) falls below 5 and exit when it subsequently rises over 70 (5-70).

To investigate, we run simple tests on ProShares Ultra S&P 500 (SSO) with RSI(2) calculations based on the RSI template from StockCharts. Using daily adjusted SSO opens and closes during July 2006 (the first full month SSO is available) through October 2019, we find that: Keep Reading

“Best” Indicator Consistency Across Samples

A subscriber inquired whether “The Only Indicator You Will Ever Need” really works. This technical indicator, a form of the Coppock Guide (or curve or indicator), applied to the Dow Jones Industrial Average by Jay Kaeppel, is a multi-parameter composite based on monthly closes as follows:

  1. Calculate the asset’s return over the past 11 months.
  2. Calculate the asset’s return over the past 14 months.
  3. Average these two past returns.
  4. Each month, calculate the 10-month front-weighted moving average (WMA) of this average (multiply the most recent value by 10, the next most recent by 9, the value for the month before that by 8, etc). Then sum the products and divide by 55.
  5. Hold the asset (cash) if this WMA is above (below) its value three months ago.

We designate this indicator 11-14WMA3. To test 11-14WMA3 in realistic scenarios, we apply it to the entire available histories for three exchange-traded funds (ETF): SPDR S&P 500 (SPY), SPDR Dow Jones Industrial Average (DIA) and iShares Russell 2000 (IWM). We consider buy-and-hold and a conventional 10-month simple moving average timing strategy (SMA10) as benchmarks. SMA10 holds the ETF (cash) when the ETF’s most recent monthly close is above (below) its 10-month SMA. Using monthly dividend-adjusted and unadjusted closes for the ETFs from their respective inceptions through September 2019 and contemporaneous 3-month U.S. Treasury bill (T-bill) yield, we find that: Keep Reading

Combine Market Trend and Economic Trend Signals?

A subscriber requested review of an analysis concluding that combining economic trend and market trend signals enhances market timing performance. Specifically, per the example in the referenced analysis, we look at combining:

  • The 10-month simple moving average (SMA10) for the broad U.S. stock market. The trend is positive (negative) when the market is above (below) its SMA10.
  • The 12-month simple moving average (SMA12) for the U.S. unemployment rate (UR). The trend is positive (negative) when UR is below (above) its SMA12.

We consider scenarios when the stock market trend is positive, the UR trend is positive, either trend is positive or both trends are positive. We consider two samples: (1) dividend-adjusted SPDR S&P 500 (SPY) since inception at the end of January 1993 (nearly 26 years); and, (2) the S&P 500 Index (SP500) since January 1948 (limited by UR availability), adjusted monthly by estimated dividends from the Shiller dataset, for longer-term robustness tests (nearly 71 years). Per the referenced analysis, we use the seasonally adjusted civilian UR, which comes ultimately from the Bureau of Labor Statistics (BLS). BLS generally releases UR monthly within a few days after the end of the measured month. We make the simplifying assumptions that UR for a given month is available for SMA12 calculation and signal execution at the market close for that same month. When not in the stock market, we assume return on cash from the broker is the yield on 3-month U.S. Treasury bills (T-bill). We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance metrics. We use the average monthly T-bill yield during a year as the risk-free rate for that year in Sharpe ratio calculations. While we do not apply any stocks-cash switching frictions or tax considerations, we do calculate the number of switches for each scenario. Using specified monthly data through September 2019, we find that: Keep Reading

Jim Cramer Using the S&P Oscillator

A reader asked about the usefulness of the S&P Short-range Oscillator as sometimes used by Jim Cramer to forecast U.S. stock market returns. The self-reported “Performance” of the oscillator, relying on in-sample visual inspection with snooped thresholds, is of small use. Since continuous historical values of the indicator are not publicly available, we conduct an out-of-sample test by:

  1. Searching CNBC.com for “Oscillator” “Mad Money” and just “Oscillator” on October 3, 2019 and identifying articles with U.S. stock market forecasts from Jim Cramer based on the S&P Short-range Oscillator.
  2. Extracting the date for each forecast and determining whether it is call to be “In” or “Out” of the market.
  3. Calculating for each call a cumulative S&P 500 Index return starting at the next open after the article date (generally timestamped after the market close) for 21 trading days.
  4. Computing average cumulative performances of “In” and “Out” calls.
  5. Comparing these averages to that for all days spanning the search results.

Using the 15 qualifying articles and daily opening levels of the S&P 500 Index during June 16, 2008 through October 31, 2019, we find that: Keep Reading

European Stock Return Predictors

Can investors effectively use firm characteristics to screen European stocks? In their August 2019 paper entitled “Predictability and the Cross-Section of Expected Returns: Evidence from the European Stock Market”, Wolfgang Drobetz, Rebekka Haller, Christian Jasperneite and Tizian Otto examine the power of 22 firm characteristics to predict stock returns individually and jointly. They assume market-based characteristics are available immediately and accounting-based characteristics are available four months after firm fiscal year end. For multi-characteristic predictions, they consider 5-characteristic, 8-characteristic and 22-characteristic models. For regression-based forecasts, they use either 10-year rolling or inception-to-date monthly inputs. For economic tests, they form equal-weighted or value-weighted portfolios that are each month long (short) the tenth, or decile, of stocks with the the highest (lowest) expected next-month returns based on 22-characteristic regression outputs. To estimate net performance, they apply one-way trading frictions of 0.57%. Using groomed monthly data for all firms in the STOXX Europe 600 index during January 2003 through December 2018, they find that:

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SMA10 vs. OFR FSI for Stock Market Timing

In response to “OFR FSI as Stock Market Return Predictor”, a subscriber suggested overlaying a 10-month simple moving average (SMA10) technical indicator on the Office of Financial Research Financial Stress Index (OFR FSI) fundamental indicator for timing SPDR S&P 500 (SPY). The intent of the suggested overlay is to expand risk-on opportunities safely. To test the overlay, we add four strategies (4 through 7) to the prior three, each evaluated since January 2000 and since January 2009:

  1. SPY – buy and hold SPY.
  2. OFR FSI-Cash – hold SPY (cash as proxied by 3-month U.S. Treasury bills) when OFR FSI at the end of the prior month is negative or zero (positive).
  3. OFR-FSI-VFITX – hold SPY (Vanguard Intermediate-Term Treasury Fund Investor Shares, VFITX, as a more aggressive risk-off asset than cash) when OFR FSI at the end of the prior month is negative or zero (positive).
  4. SMA10-Cash – hold SPY (cash) when the S&P 500 Index is above (at or below) its SMA10 at the end of the prior month.
  5. SMA10-VFITX – hold SPY (VFITX) when the S&P 500 Index is above (at or below) its SMA10 at the end of the prior month.
  6. OFR-FSI-SMA10-Cash – hold SPY (cash) when either signal 2 or signal 4 specifies SPY. Otherwise, hold cash.
  7. OFR-FSI-SMA10-VFITX – hold SPY (cash) when either signal 3 or signal 5 specifies SPY. Otherwise, hold VFITX.

Using end-of-month values of OFR FSI, SPY total return and level of the S&P 500 Index during January 2000 (OFR FSI inception) through June 2019, we find that:

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