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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.

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A Few Notes on Buy the Fear, Sell the Greed

Larry Connors introduces his 2018 book, Buy the Fear, Sell the Greed: 7 Behavioral Quant Strategies for Traders, by stating in Chapter 1 that the book shows when, where and how: “…to trade directly against traders and investors who are having…feelings of going crazy and impending doom. …The goal of this book is to make you aware of when and why short-term market edges exist in stocks and in ETFs, and then give you the quantified strategies to trade them. …Thirty years ago, when a news event would occur, it could take days to assimilate it. …The only thing that’s changed is the timing of their emotion; today it occurs faster and at times is more extreme primarily due to the role the media (and especially social media) plays in disseminating the news that triggers this behavior.” Based on analyses of specific trading setups using data through 2017, he finds that: Keep Reading

RSP/SPY as a Stock Market Breadth Indicator

A reader proposed: “I recently found something interesting while analyzing the ratio of the equal-weighted S&P 500 Index to its market capitalization-weighted counterpart. Whenever this ratio declines (out of an uptrend), the market crashes (July 2007, September-October 2008, July 2011). Also, when this ratio starts rising, the recovery commences (April 2009). The indicator seems to warn of problematic times ahead. …Perhaps this ratio provides insight into whether money is moving into the market (ratio rising) or out of the market (ratio falling). Could you take a look at this to see whether this ratio is a great indicator?” To investigate, we use SPDR S&P 500 (SPY) and Invesco S&P 500 Equal Weight (RSP) as tradable proxies for the capitalization-weighted and equal-weighted S&P 500 Index, respectively. Using weekly dividend-adjusted prices of SPY and RSP from the end of April 2003 (limited by RSP) through July 2018 (796 weeks), we find that: Keep Reading

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 the 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 June 2018, we find that: Keep Reading

Gold Timing Strategies

Are there any gold trading strategies that reliably beat buy-and-hold? In their April 2018 paper entitled “Investing in the Gold Market: Market Timing or Buy-and-Hold?”, Viktoria-Sophie Bartsch, Dirk Baur, Hubert Dichtl and Wolfgang Drobetz test 4,095 seasonal, 18 technical, and 15 fundamental timing strategies for spot gold and gold futures. These strategies switch at the end of each month as signaled between spot gold or gold futures and U.S. Treasury bills (T-bill) as the risk-free asset. They assume trading frictions of 0.2% of value traded. To control for data snooping bias, they apply the superior predictive ability multiple testing framework with step-wise extensions. Using monthly spot gold and gold futures prices and T-bill yield during December 1979 through December 2015, with out-of-sample tests commencing January 1990, they find that:

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Bollinger Bands: Buy Low and Sell High?

Are Bollinger Bands (BB) useful for specifying 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. We consider BB settings ranging from 0 to 2.5 standard deviations of daily returns, calculated over the same trailing 21 trading days. Using daily unadjusted closes of of SPY (to calculate BBs), dividend-adjusted closes of SPY (to calculate total returns) and contemporaneous yields for 3-month Treasury bills (T-bill) from the end of January 1993 (SPY inception) through early May 2018, we 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 IM 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: (1) in stocks (cash) when past return is positive (negative); and, (2) in stocks (cash) when average monthly past return is above (below) the average monthly risk-free rate, proxied by the 3-month U.S. Treasury bill (T-bill) yield, over the same measurement interval. The rule for SMAs is: in stocks (cash) when current level is above (below) the SMA. Using monthly T-bill yield and monthly level of the Dow Jones Industrial Average (DJIA) during January 1934 through April 2018 (over 84 years), we find that: Keep Reading

Commodity, Equity Index and Currency Popular Pairs Trading

Are technical rules applied to pairs trading attractive after correcting for data snooping bias? In their March 2018 paper entitled “Pairs Trading, Technical Analysis and Data Snooping: Mean Reversion vs Momentum”, Ioannis Psaradellis, Jason Laws, Athanasios Pantelous and Georgios Sermpinis test a variety of technical trading rules for long-short trading of 15 commodity futures, equity indexes and currency pairs (all versus the U.S. dollar) frequently used on trading websites or offered by financial market firms. Specifically, they test 18,412 trend-following/momentum and contrarian/mean-reversion rules often applied by traders to past daily pair return spreads. They consider average excess (relative to short-term interest rate) return and Sharpe ratio as key metrics for rule selection and performance measurement. They use False Discovery Rate (FDR) to control for data snooping bias, such that 90% of the equally weighted best rules in FDR-corrected portfolios significantly outperform the benchmark. Most tests are in-sample. To test robustness of findings, they: (1) account for one-way trading frictions ranging from 0.02% to 0.05% across assets; (2) consider five subperiods to test consistency over time; and, (3) perform out-of-sample tests using the first part of each subperiod to select the best rules and roughly the last year to measure performance of these rules out-of-sample. Using daily prices of specified assets and daily short-term interest rates for selected currencies during January 1990 (except ethanol starts late March 2006) through mid-December 2016, they find that:

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Using RSI(2) to Trade Leveraged ETFs

A subscriber asked about the effectiveness of applying a two-period Relative Strength Index, RSI(2), to leveraged exchange-traded funds (ETF), suggesting 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 March 2018, we find that: Keep Reading

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 the non-adjusted series. We assume return on cash is the 13-week 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 monthly, weekly and daily dividend-adjusted and unadjusted closes for SPY and daily T-bill yield from the end of January 1993 through mid-March 2018, we find that:

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Bonds Lead Stocks?

Are bond market investors generally shrewder than their stock market counterparts, such that bond yield tops (bottoms) anticipate stock market bottoms (tops)? To investigate, we employ both a monthly lead-lag analysis and a comparison of bond yield and stock market tops and bottoms. We define “top” and “bottom” as the highest (lowest) value in a rolling window that extends from 30 months in the past to 30 months in the future (a total window of five years). Using monthly levels of Moody’s yield on seasoned Aaa corporate bonds and the Dow Jones Industrial Average (DJIA) during October 1928 through February 2018 (about 90 years) and monthly levels of the 10-year government bond interest rate and the stock market from Robert Shiller during January 1871 through February 2018 (about 148 years), we find that: Keep Reading

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