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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.

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 month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
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)

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 (limited by DBC) through January 2019, we find that: Keep Reading

Add Position Stop-loss to SACEMS?

Does adding a position stop-loss rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by avoiding some downside volatility? SACEMS each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified lookback interval:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
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)

To investigate the value of stop-losses, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return falls below 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 drawdowns for the specified assets during February 2006 (limited by DBC) through January 2019, 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 mid-January 2019, we find that: Keep Reading

SACEMS with Momentum Breadth Crash Protection

In response to “SACEMS with SMA Filter”, a subscriber suggested instead crash protection via momentum breadth (proportion of assets with positive momentum) by:

  1. Switching to 100% cash when fewer than four of eight Simple Asset Class ETF Momentum Strategy (SACEMS) non-cash assets have positive past returns.
  2. Scaling from cash into winners when four to eight risk assets have positive past returns (no cash for eight).
  3. Replacing U.S. Treasury bills (T-bills), a proxy for broker money market rates, with iShares Barclays 7-10 Year Treasury Bond (IEF) as “Cash.”

To investigate, we each month rank assets from the following SACEMS universe based on total returns over a specified lookback interval. We also each month measure momentum breadth for the eight non-cash assets using the same lookback interval.

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
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)

While emphasizing the suggested momentum breadth crash protection threshold, we look at all possible thresholds. While emphasizing a baseline lookback interval, we consider lookback intervals ranging from one to 12 months for the suggested momentum breadth threshold. We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for the equal-weighted (EW) Top 3 SACEMS portfolio, but also look at Top 1 and EW Top 2. We also look at EW Top 3 portfolio turnover. Using monthly dividend-adjusted closing prices for SACEMS assets and IEF and the T-bill yield during February 2006 (the earliest all ETFs are available) through December 2018, we find that: Keep Reading

Trend Following: Momentum or Moving Average?

Are moving averages or intrinsic (time series) momentum theoretically better for following trends in asset prices? In their November 2018 paper entitled “Trend Following with Momentum Versus Moving Average: A Tale of Differences”, Valeriy Zakamulin and Javier Giner compare from a theoretical perspective effectiveness of four popular trend following rules:

  1. Intrinsic Momentum – buy (sell) when the closing price at the end of a specified lookback interval is greater (less) than the closing price at the beginning of the lookback interval.
  2. Simple Moving Average – buy (sell) when the closing price at the end of a specified lookback interval is greater (less) than the equally weighted average closing price during the lookback interval.
  3. Linear Moving Average – buy (sell) when the closing price at the end of a specified lookback interval is greater (less) than the linearly weighted (weights linearly increasing to the most recent) average closing price during the lookback interval.
  4. Exponential Moving Average – buy (sell) when the closing price at the end of a specified lookback interval is greater (less) than the exponentially weighted (weights exponentially increasing to the most recent) average closing price during the lookback interval.

They transform these price rules into return-based versions and create a trend model as an autoregressive return process. They then explore interactions of the trading rules with the trend model. Based on this theoretical approach, they conclude that: Keep Reading

SACEVS with SMA Filter

Does  applying a simple moving average (SMA) filter improve performance of the “Simple Asset Class ETF Value Strategy” (SACEVS), which seeks diversification across the following three asset class exchange-traded funds (ETF) plus cash according to the relative valuations of term, credit and equity risk premiums?

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

Since many technical traders use a 10-month SMA (SMA10), we test effectiveness of requiring that each of the ETFs pass an SMA10 filter by comparing performances for three scenarios:

  1. BaselineSACEVS as currently tracked.
  2. With SMA10 Filter – Run Baseline SACEVS and then apply SMA10 filters to dividend-adjusted prices of ETF allocations. If an allocated ETF is above (below) its SMA10, hold the allocation as specified (Cash). This rule is inapplicable to any Cash allocation.
  3. With Half SMA10 Filter – Same as scenario 2, but, if an allocated ETF is above (below) its SMA10, hold the allocation as specified (half the specified allocation and half cash at the T-bill yield).

We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) of SACEVS Best Value, SACEVS Weighted and the 60%-40% SPY-TLT benchmark (60-40) portfolios. Using required SACEVS monthly historical data and monthly dividend-adjusted closing prices for the above asset class proxies and the yield for Cash over the period July 2002 (the earliest all ETFs are available) through November 2018, we find that: Keep Reading

SACEMS with SMA Filter

A subscriber asked whether applying a simple moving average (SMA) filter to “Simple Asset Class ETF Momentum Strategy” (SACEMS) winners improves strategy performance. SACEMS each months picks winners from among the following asset class exchange-traded fund (ETF) proxies based on past returns over a specified interval:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
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)

Since many technical traders use a 10-month SMA (SMA10), we test effectiveness of requiring that each winner pass an SMA10 filter by comparing performances for three scenarios:

  1. Baseline – SACEMS as presented at “Momentum Strategy”.
  2. With SMA10 Filter – 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). This rule is inapplicable to Cash as a winner.
  3. With Half SMA10 Filter – Same as scenario 2, but, if a winner is above (below) its SMA10, hold the winner (half the winner and half cash at the T-bill yield).

We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) of SACEMS Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios. Using monthly dividend-adjusted closing prices for the asset class proxies and the yield for Cash over the period February 2006 (the earliest all ETFs are available) through November 2018, we find that: Keep Reading

Most Effective U.S. Stock Market Return Predictors

Which economic and market variables are most effective in predicting U.S. stock market returns? In his October 2018 paper entitled “Forecasting US Stock Returns”, David McMillan tests 10-year rolling and recursive (inception-to-date) one-quarter-ahead forecasts of S&P 500 Index capital gains and total returns using 18 economic and market variables, as follows: dividend-price ratio; price-earnings ratio; cyclically adjusted price-earnings ratio; payout ratio; Fed model; size premium; value premium; momentum premium; quarterly change in GDP, consumption, investment and CPI; 10-year Treasury note yield minus 3-month Treasury bill yield (term structure); Tobin’s q-ratio; purchasing managers index (PMI); equity allocation; federal government consumption and investment; and, a short moving average. He tests individual variables, four multivariate combinations and and six equal-weighted combinations of individual variable forecasts. He employs both conventional linear statistics and non-linear economic measures of accuracy based on sign and magnitude of forecast errors. He uses the historical mean return as a forecast benchmark. Using quarterly S&P 500 Index returns and data for the above-listed variables during January 1960 through February 2017, he finds that: Keep Reading

Moving Average Timing of Stock Fundamental Ratios

Can investors time premiums associated with widely used stock/firm fundamental ratios? In their September 2018 paper entitled “It Takes Two to Tango: Fundamental Timing in Stock Market”, Fuwei Jiang, Xinlin Qi, Guohao Tang and Nan Huang use a simple moving average (SMA) trend indicator to time premiums associated with four fundamental stock/firm ratios: book-to-market (BM), earnings-to-price (EP), gross profitability (GP), and return-on-assets (ROA). In calculating these ratios, they lag accounting variables by six months to avoid look-ahead bias. For each ratio, they:

  • At the end of each June, rank stocks into tenths (deciles).
  • Each day, calculate value-weighted average returns for the deciles with the highest (highest BM, EP, GP, ROA) and lowest (lowest BM, EP, GP, ROA) expected returns and maintain price indexes for these two deciles.
  • Each day, hold a long (short) position in the decile with highest (lowest) expected returns only when the decile price index is above (below) its 20-day SMA, indicating an upward (downward) trend. When not holding a decile, hold Treasury bills.

As benchmarks, they each year buy and hold four portfolios that are each long (short) the value-weighted deciles with the highest (lowest) expected returns for one of the fundamental ratios. While focusing on a 20-day SMA, for robustness they also test SMAs of 10, 50, 100 and 200 trading days. While focusing on value weighting, they also look at equal weighting. They run tests on both non-financial Chinese A-share stocks and non-financial U.S. common stocks. Using annual groomed fundamentals data and daily returns for Chinese stocks during January 2001-December 2017 and for U.S. stocks during July 1970-December 2017, and contemporaneous Treasury bill yields, they 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 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 2017, we find that: Keep Reading

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