Investing Research Articles

Does the Memorial Day holiday signal any unusual return effects? By its definition, this holiday brings with it any effects from three-day weekends and sometimes the turn of the month. Prior to 1971, the U.S. celebrated Memorial Day on May 30. Effective in 1971, Memorial Day became the last Monday in May. To investigate the possibility of short-term effects on stock market returns around Memorial Day, we analyze the historical behavior of the stock market during the three trading days before and the three trading days after the holiday. Using daily closing levels of the S&P 500 Index for 1950 through 2012 (63 observations), we find that: More…

Does adding fundamental firm quality metrics to refine stock sorts based on traditional value ratios, book-to-market ratio (B/M) and earnings-to-price ratio (E/P), improve portfolio performance? In his 2013 paper entitled “The Quality Dimension of Value Investing”, Robert Novy-Marx tests combination strategies to determine which commonly used quality measures most enhance the performance of value ratios. He considers such quality metrics as Piotroski’s FSCORE, earnings accruals, gross profitability (GP) and return on invested capital (ROIC). His general test approach is to reform capitalization-weighted portfolios annually from stocks sorted at the end of each June according to value ratios and quality metrics for the previous calendar year. He uses the 1000 largest (2000 next largest) stocks by market capitalization to represent large (small) stocks. He considers both long-only (long the top 30%) and long-short (long the top 30% and short the bottom 30%) portfolios. He also considers the incremental benefit of incorporating stock price momentum based on return over the previous 11 months with a skip-month (11-1) into stock selection. He estimates trading frictions based on calculated turnover and effective bid-ask spreads. Using stock prices and associated firm fundamentals during July 1963 through December 2011, he finds that: More…

Should investors regard any of the exchange-traded products (ETP) based on S&P 500 Index option-implied volatility (VIX) futures as long-term holdings? In the May 2013 draft of his paper entitled “Trading Volatility: At What Cost?”, Robert Whaley describes these ETPs and evaluates them as buy-and-hold investments. VIX ETPs are based on VIX futures indexes with daily rebalancing, subject to management fees and expenses including commissions and trading fees, licensing fees and (for some ETPs) foregone interest income. Many of the ETPs are exchange-traded notes (ETN), secured not by underlying assets but rather only by the good faith and collateral of the issuer. Using daily price and trading data for VIX futures (starting March 2004) and options (starting February 2006) and for 30 ETPs based on VIX futures (starting January 2009) through March 2012, he finds that: More…

How should investors think about alternative asset allocation strategies for risk management? In his May 2013 paper entitled “Advances in Portfolio Risk Control. Risk! Parity?”, Winfried Hallerbach offers a practitioner’s review of new and revived portfolio allocation strategies, including: Equal Weight, Maximum Diversification, Minimum Variance; Equal Risk Contribution (Risk Parity); Inverse Volatility; Maximum Sharpe Ratio; and, Volatility Targeting. He addresses their pluses and minuses and compares them to each other. He observes that the large contribution of equities to (downside) risk within portfolios that lean only moderately toward stocks provides the impetus for risk management research. Based on key studies of portfolio risk management and examples using monthly data for four U.S. asset classes (risk-free rate, stocks, aggregate Treasuries, corporate investment grade bonds, and corporate high-yield bonds) during June 2002 through May 2012, he finds that: More…

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for April 2013. The actual total (core) inflation rate for April is lower (lower) than forecasted.

The new actual and forecasted inflation rates will flow into Real Earnings Yield Model projections about the end of May.

Are returns on real estate usefully predictable? In the June 2012 version of their book chapter entitled “Forecasting Real Estate Prices”, Eric Ghysels, Alberto Plazzi, Walter Torous and Rossen Valkanov examine the evidence of predictability in U.S. residential and commercial real estate markets. They review methodologies used in constructing widely used real estate price indexes. They then survey the key empirical findings from academic studies of short-run momentum and long-run reversals in real estate returns. Finally, they test the ability of different variables (past stock market return, stock market dividend yield, 3-month Treasury bill (T-bill) yield relative to its 12-month moving average, inflation rate, term spread between 5-year and 3-month maturities, combination of forward interest rates and industrial production growth) to predict real estate returns as calculated from several price indexes and a real estate investment trust (REIT) index. Using monthly and quarterly index levels for the real estate market proxies and values for the predictive variables as available, focusing on 1991 through 2010, they find that: More…

Are prominent stock market bloggers in aggregate able to predict the market’s direction? The Ticker Sense Blogger Sentiment Poll “is a survey of the web’s most prominent investment bloggers, asking ‘What is your outlook on the U.S. stock market for the next 30 days?’” (bullish, bearish or neutral) on a weekly basis. The site currently lists 33 participating bloggers. Participation has varied over time. Because Ticker Sense collects data weekly, we look at weekly measurements and changes in weekly measurements. Because the poll question asks for a 30-day outlook, we test the forecasts against stock market behavior four weeks into the future. Because polling takes place Thursday-Sunday, we use the coincident Friday close to represent the state of the stock market for each poll (except for the poll of 10/13/08, which took place on Monday and therefore relates to the Monday close). We use [% Bullish] minus [% Bearish] as the net sentiment measure for each poll. Using poll results from inception on 7/10/06 through 5/6/13 (347 polls) and contemporaneous weekly closes of the S&P 500 Index as representative of the broad stock market, we find that: More…

Does Google search activity anticipate stock market action? In their paper entitled “Quantifying Trading Behavior in Financial Markets Using Google Trends”, Tobias Preis, Helen Susannah Moat and Eugene Stanley analyze the power of changes in Google search intensity (term search volume relative to total Google search volume) for 98 terms to predict the behavior of the Dow Jones Industrial Average (DJIA). They apply Google Trends to measure each week the average search intensity for a term over the prior three weeks. They then measure changes in this weekly average search intensity relative to its average behavior over several weeks (with three weeks as baseline). They test a trading strategy that sells (buys) DJIA at the close on the first trading day of the next week if the change in weekly search term intensity is negative (positive) and exits the position at the close on the first trading day of the following week. They consider three benchmarks based on DJIA: (1) buy-and-hold; (2) random weekly timing; and, (3) an index reversion strategy with rules similar to the search intensity strategy. They ignore trading frictions, which involve a maximum of 104 one-way trades per year. Using weekly search intensity data for the specified search words and weekly DJIA closing levels as specified during January 2004 through most of February 2011, they find that: More…

How should researchers address the issue of aggregate/cumulative data snooping bias, which derives from many researchers exploring approximately the same data over time? In their April 2013 draft paper entitled “. . . and the Cross-Section of Expected Returns”, Campbell Harvey, Yan Liu and Heqing Zhu examine this issue with respect to studies that discover factors explaining differences in future returns among U.S. stocks. They argue that aggregate/cumulative data snooping bias makes conventional statistical significance cutoffs (for example, a t-statistic of at least 2.0) too low. Researchers should view their respective analyses not as independent single tests, but rather as one of many within a multiple hypothesis testing framework. Such a framework raises the bar for significance according to the number of hypotheses tested, and the authors give guidance on how high the bar should be. They acknowledge that they do not (cannot) count past tests of factors falling short of conventional significance levels (and consequently not published). Using the body of published and near-published (working papers) research that discovers new factors explaining the cross-section of future U.S. stock returns from the mid-1960s through 2012, they find that: More…

Kathleen Peddicord, publisher of the Live and Invest Overseas group, opens her 2013 book, How to Buy Real Estate Overseas, by stating: “The idea of diversifying your investments, your assets, your life and your future overseas can seem frightening, intimidating, even paralyzing. Could you really do it? Yes, you could. I say that based on 30 years of experience at this.” The book takes the perspective of a U.S. citizen seeking to diversify assets via direct ownership of non-U.S. real estate. Using examples based on her experience investing in real estate in 20 countries and operating businesses in seven, she concludes that: More…