Investing Research Articles

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Recent Intraday U.S. Stock Market Behavior

“Intraday U.S. Stock Market Behavior” examines behavior of the S&P 500 Index at 15-minute intervals over the trading day during each of 2007 (bullish year) and 2008 (bearish year), finding slight tendencies for market weakness during mid-afternoon and market volatility at the beginning and the end of the trading day. Does recent data confirm these findings? To investigate, we calculate average cumulative returns and standard deviations of returns for both the S&P 500 Index and SPDR S&P 500 (SPY) measured at 5-minute intervals during the trading day over the last six months. Using 5-minute levels/prices for the S&P 500 Index and for SPY during 9:30-16:00 over the period August 2012 through September 2014, we find that: Keep Reading

Mutual Fund Market Timing Worldwide

How successful are active equity mutual fund managers in timing their domestic markets worldwide? In their August 2014 paper entitled “Market Timing Around the World”, Javier Vidal-Garcia, Marta Vidal and Duc Khuong Nguyen employ daily returns to measure the effectiveness of mutual fund market exposure adjustments made more frequently than monthly. They also examine fund timing performance under different economic conditions. Their fund universe consists of 8,680 actively managed, open-end, diversified, domestic live and dead equity mutual funds registered in 35 countries (about 69% are U.S.-registered). Using daily total returns in local currencies and characteristics for these funds, along with contemporaneous country economic data, during January 1990 through December 2013, they find that: Keep Reading

Models, Trading Calendar and Momentum Strategy Updates

We have updated the S&P 500 Market Models summary as follows:

  • Extended Market Models regressions/rolled projections by one month based on data available through September 2014.
  • Updated Market Models backtest charts and the market valuation metrics map based on data available through September 2014.

We have updated the Trading Calendar to incorporate data for September 2014.

We have updated the the monthly asset class momentum winners and associated performance data at Momentum Strategy.

Preliminary Momentum Strategy Update

The home page and “Momentum Strategy” now show preliminary asset class momentum strategy positions for October 2014. The differences in past returns among the top three places are large enough that they are unlikely to change order by the close. However, the gap between the third and fourth places is small enough that third place could change.

At this point, four of nine asset classes have negative cumulative returns over the past five months.

Timing VIX Futures with the Futures-Spot Ratio

Is the return on CBOE S&P 500 Volatility Index (VIX) futures predictable? In his preliminary paper entitled “The Expected Return of Fear”, Ing-Haw Cheng investigates whether the relationship between VIX futures prices and VIX level predicts the return on VIX futures. He focuses on monthly returns to a continuously-invested position in the nearest available VIX futures contract. He considers several different explanations for the behavior of VIX futures prices. Using VIX futures daily settlement prices during March 2004 through July 2014 (125 months), he finds that: Keep Reading

First Trust Sector/Industry ETF Momentum Strategy

A subscriber proposed a simple test of the concept underlying the First Trust Dorsey Wright Focus 5 ETF (FV). This exchange-traded fund (ETF) intends to track the Dorsey Wright Focus Five Index, an equally weighted and weekly reformed portfolio of the five First Trust sector and industry ETFs with the highest price momentum according to the Dorsey, Wright & Associates relative strength ranking system. In the absence of a detailed specification for this ranking system, the subscriber proposed a conceptual test applying the rules for the “Simple Asset Class ETF Momentum Strategy” to the FV universe, which consists of the following 23 ETFs:

First Trust NASDAQ-100-Technology Sector Index Fund (QTEC))
First Trust NYSE Arca Biotechnology Index Fund (FBT)
First Trust Dow Jones Internet Index Fund (FDN)
First Trust ISE-Revere Natural Gas Index Fund (FCG)
First Trust ISE Water Index Fund (FIW)
First Trust S&P REIT Index Fund (FRI)
First Trust Consumer Discretionary AlphaDEX Fund (FXD)
First Trust Consumer Staples AlphaDEX Fund (FXG)
First Trust Health Care AlphaDEX Fund (FXH)
First Trust Technology AlphaDEX Fund (FXL)
First Trust Energy AlphaDEX Fund (FXN)
First Trust Financials AlphaDEX Fund (FXO)
First Trust Industrials/Producer Durables AlphaDEX Fund (FXR)
First Trust Utilities AlphaDEX Fund (FXU)
First Trust Materials AlphaDEX Fund (FXZ)
First Trust FTSE EPRA/NAREIT Developed Markets Real Estate Index Fund (FFR)
First Trust NASDAQ ABA Community Bank Index Fund (QABA)
First Trust NASDAQ Clean Edge Smart Grid Infrastructure Index Fund (GRID)
First Trust ISE Global Copper Index Fund (CU)
First Trust ISE Global Platinum Index Fund (PLTM)
First Trust NASDAQ CEA Smartphone Index Fund (FONE)
First Trust ISE Cloud Computing Index Fund (SKYY)
First Trust NASDAQ Technology Dividend Index Fund (TDIV)

At the end of each month, we allocate all funds to the equally weighted set of the five of these 23 ETFs with the highest total return over the past five months. Using monthly dividend-adjusted closing prices for these ETFs during May 2007 (when 15 of the ETFs are available) through August 2014 (88 months), we find that: Keep Reading

2015 Charles H. Dow Award Competition

The deadline for submission of papers for the 2015 Charles H. Dow Award, presented by the Market Technicians Association (MTA), is January 5, 2015. MTA established this award in 1994 to recognize outstanding research in technical analysis. The Award carries a prize of $5,000 and the opportunity to present the winning paper at the April 2015 MTA Gala Awards Dinner in New York City.

Competition for the award is open to all practitioners and academics. Per the Guidelines for Submissions, the MTA judging panel evaluates each submission based on its enhancement of the understanding of market action and the concepts of technical analysis, and its thoroughness.

CXOadvisory.com has no affiliation with MTA or the Charles H. Dow Award.

Weekly Summary of Research Findings: 9/22/14 – 9/26/14

Below is a weekly summary of our research findings for 9/22/14 through 9/26/14. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Money Supply Growth and Future Stock Market Returns

Are changes in the money supply usefully predictive of stock market behavior? In his September 2014 paper entitled “Does Money Supply Growth Contain Predictive Power for Stock Returns?”, David McMillan investigates whether changes in U.S. money supply reliably affect future U.S. stock market returns. He examines also whether any predictive power of money supply growth is independent of dividend yield, interest rates and other economic variables. He focuses on M2 money stock but also considers M1 money stock and the non-M1 components of M2 (saving deposits, small time deposits, retail money market mutual funds), M4 and the monetary base and its components (currency in circulation and reserves). He considers predictability horizons of one month, one year, five years, 10 years and 15 years. Using monthly data for stock index levels, dividends and earnings from Robert Shiller and seasonally adjusted money supply and other economic data from FRED during January 1959 through December 2012 (54 years), he finds that: Keep Reading

Essential Assumption of Pairs Trading Wrong?

Do stock pairs that track in the past reliably track in the future? In his January 2014 paper entitled “On the Persistence of Cointegration in Pairs Trading”, Matthew Clegg assesses the persistence of cointegration among pairs of liquid U.S. stocks. Specifically, he investigates whether pairs of equities that are cointegrated in an initial interval are likely to be cointegrated in a subsequent interval. He uses calendar years as initial intervals and focuses on next years as subsequent intervals. He also considers shorter subsequent intervals. He employs a variety of methods to measure pair cointegration to ensure robustness of findings. Using daily returns for constituents of the S&P 500 (as of August 13, 2013) during January 2002 through December 2012, allowing ten years of persistence tests, he finds that: Keep Reading

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