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Weekly Summary of Research Findings: 2/8/16 – 2/12/16

Below is a weekly summary of our research findings for 2/8/16 through 2/12/16. 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

Momentum Strategy Performance for German Stocks

Do reversal, momentum and reversion effects hold among German stocks? In his January 2016 paper entitled “Trading Strategies Based on Past Returns – Evidence from Germany”, Martin Schmidt examines the performance of short-term reversal, intermediate-term momentum, long-term reversion and seasonality strategies in the German stock market. The seasonal strategy considers one-month returns from multiples of 12 months ago. His general approach is to each month (1) rank stocks into tenths (deciles) of a specified segment or pattern of past returns and (2) measure the performance next month of a value-weighted or equal-weighted portfolio that is long the top decile and short the bottom decile. For value weighting, he caps weight at 50%. Using monthly prices for a broad sample of German stocks during January 1955 through June 2014, he finds that: Keep Reading

Equity Factor Returns Across the Chinese Zodiac

Do the 12 yearly signs of the Chinese Zodiac cycle (Rabbit, Dragon, Snake, Horse, Goat, Monkey, Rooster, Dog, Pig, Rat, Ox, Tiger) relate individually to stock market behavior? In their January 2016 paper entitled “The Zodiac Calendar and Equity Factor Returns”, Janice Phoeng and Laurens Swinkels calculate four annual equity factor returns for each of the Zodiac signs: (1) market minus the risk-free rate; (2) small capitalization minus big capitalization; (3) value minus growth; and, (4) high momentum versus low momentum. They start each year on the first day of the Zodiac New Year and end at the last day of the same Zodiac year. Using daily U.S. equity factor returns from Kenneth French’s data library during early February 1927 through mid-February 2015, they find that: Keep Reading

Time Series and Dual Momentum for Individual Stocks

Does a time series (absolute or intrinsic) momentum strategy work at the stock level? In their January 2016 paper entitled “The Enduring Effect of Time-Series Momentum on Stock Returns Over Nearly 100-Years” Ian D’Souza, Voraphat Srichanachaitrchok, George Wang  and Yaqiong Yao test the significance of time series momentum among individual stocks. Their baseline time series momentum strategy consists of each month calculating cumulative returns for each stock from 12 months ago to one month ago and taking a long (short) position for one month in stocks with positive (negative) past returns. For comparison, they also test a cross-sectional, or relative, momentum strategy that is each month long (short) the tenth, or decile, of stocks with the highest (lowest) cumulative returns over the same measurement interval. They skip the month between past return measurement and portfolio formation to avoid a reversal effect. They consider both value and equal weighting. They then test a dual momentum strategy that each month: (1) identifies time series momentum winners and losers; (2) ranks these two groups separately into fifths (quintiles); and, (3) buys the top quintile of time series winners and sells the bottom quintile of time series losers. Using monthly data for a broad U.S. stock sample during 1926 through 2014 and for stock samples from 13 other developed markets during mostly 1975 through 2014, they find that: Keep Reading

Blow-ups in Technology-boosted Finance

Has the Moore’s Law-driven advance in financial information technology strengthened the hand of Murphy’s Law in markets? In the January 2016 version of his paper entitled “Moore’s Law vs. Murphy’s Law in the Financial System: Who’s Winning?” Andrew Lo reviews big unintended consequences of technology-leveraged finance including fire sales, flash crashes, botched initial public offerings (IPO), catastrophic algorithmic trading errors and access failures. He then discusses the counterbalancing roles of technology in elevating and suppressing financial system risk. Based on a survey of recent financial system breakdowns and his experience, he finds that: Keep Reading

Gold Futures vs. Gold Miner Stocks

How do gold futures and gold miner stocks interact? In their January 2016 paper entitled “Are Gold Bugs Coherent?”, Brian Lucey and Fergal O’Connor examine the relationship between gold miner stock behavior (NYSE ARCA Gold Bugs Index) and the price of gold (COMEX gold futures). Specifically, they apply wavelet transforms to analyze the degree of co-movement (coherency) and lead-lag tendencies between changes in the Gold Bugs Index and gold futures price measured at both short and long intervals. Using daily closes for the two series during January 1998 through most of November 2015 (see the chart below), they find that: Keep Reading

Weekly Summary of Research Findings: 2/1/16 – 2/5/16

Below is a weekly summary of our research findings for 2/1/16 through 2/5/16. 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

Stock Market and the Super Bowl

Investor mood may affect financial markets. Sports may affect investor mood. The biggest mood-mover among sporting events in the U.S. is likely the National Football League’s Super Bowl. Is the week before the Super Bowl especially distracting and anxiety-producing? Is the week after the Super Bowl focusing and anxiety-relieving? Presumably, post-game elation and depression cancel between respective fan bases. Using past Super Bowl dates since inception and daily/weekly S&P 500 Index data for 1967 through 2015 (49 events), we find that: Keep Reading

Trend Following vs. Return Chasing

How can trend following (intrinsic or absolute or time series momentum) beat the market, while ostensibly similar return chasing transfers wealth from naive to smart investors? In their January 2016 paper entitled “Return Chasing and Trend Following: Superficial Similarities Mask Fundamental Differences”, Victor Haghani and Samantha McBride offer a plausible and testable definition of return chasing and explore its differences from trend following. They characterize trend followers as mechanical and decisive and return chasers as discretionary and slow moving. For quantitative comparison, they consider three long-only, no-leverage strategies:

  1. 50-50 (benchmark): 50% equities and 50% U.S. Treasury bills (T-bills), rebalanced monthly.
  2. Trend following: 100% stocks (T-bills) when real stock market return over the past year is greater than (less than) 2.5%.
  3. Return chasing: increase (decrease) exposure to stocks each month by 20% of however much real stock market return exceeds (falls short of) 2.5% over the past year, holding the balance in T-bills.

They test these strategies with Robert Shiller’s long-run U.S. stock market data spanning 1871 through 2015 and with separately specified Monte Carlo simulation (5,000 runs of 20 years based on weekly simulated prices). Using these two approaches, they find that: Keep Reading

Must ERP Forecasts Be Positive?

Should equity risk premium (ERP) forecasters assume in their models, because stocks always carry risk, that the premium cannot be negative? In their January 2016 paper entitled “Forecasting the Equity Risk Premium: The Ups and the Downs”, Nick Baltas and Dimitris Karyampas examine recent ERP forecasting research, with focus on simple models constrained to positive values. Their baseline model is a linear regression model that forecasts next-period S&P 500 Index excess return from either the index dividend-price ratio or the 3-month US treasury bill yield. They highlight advantages and disadvantages of models that do and do not constrain ERP to non-negative values for three types of market regimes: (1) up markets (positive actual ERP) versus down markets (negative actual ERP); (2) recessions versus expansions; and, (3) low volatility versus high volatility. Using monthly total returns for the S&P 500 Index and monthly levels of the predictive variables during January 1927 through December 2013 (with initial training period 20 years), they find that: Keep Reading

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