Equity Premium

Do stocks entering (exiting) factor indexes experience a price jump (drop) due to increased (decreased) demand? In their October 2016 paper entitled “Price Response to Factor Index Decompositions”, Joop Huij and Georgi Kyosev examine price impacts for stocks entering and exiting MSCI Minimum Volatility factor indexes covering U.S., European, global and emerging markets. To isolate the factor index effect, they exclude changes affecting both a factor index and its parent broad market index and changes due to corporate actions (such as spin-off or acquisition). They distinguish between the effective day (ED) of a change (first day the change occurs in the index portfolio) and the announcement day (AD) of a change (nine business days before ED). They define daily abnormal return of a stock as return in excess of the return of its factor index. They define daily abnormal trading volume of a stock as the ratio of dollar trading volume of the stock to dollar trading volume of its factor index, multiplied by the ratio of average dollar trading volume of the index to average dollar trading volume of the stock during a 40-day window ending 10 days before AD. Using index changes and daily returns and trading volumes of all stocks in the Minimum Volatility factor indexes and their parent broad market indexes during November 2010 through December 2015 (11 index rebalancings), they find that: Keep Reading

Do the realities of trading (bids and asks, stale prices, large orders, noise traders and technical traders) that may drive asset price away from fundamental value affect some stocks more than others? If so, is the effect exploitable? In their October 2016 draft paper entitled “(Priced) Frictions”, Kewei Hou, Sehoon Kim and Ingrid Werner assess the impact of such microstructure frictions on the cross-section of stock returns. Using a rolling 250-day window, they first estimate a stock’s friction-free average daily return as average two-day return divided by average lagged one-day return. They then compute the stock’s microstructure friction (FRIC) as average daily return minus friction-free average daily return over the same 250-day rolling window. To explore cross-sectional effects, they each month sort stocks into tenths (deciles) based on FRIC and construct a hedge portfolio that is long the high-FRIC decile and short the low-FRIC decile. They also perform double-sorts of FRIC and other stock return factors/predictors into fifths (quintiles) to investigate interactions. Using daily returns and firm data for a broad sample of U.S. stocks, and monthly returns for various stock return factors/predictors, during July 1963 through June 2013, they find that: Keep Reading

Are stocks with high prices or low prices inherently better deals? In their October 2016 paper entitled “Nominal Stock Price Investing”, Ulrich Hammerich, Christian Fieberg and Thorsten Poddig examine the relationship between stock price and future stock performance in the German equity market. Specifically, they each month sort stocks by price and measure the difference in average total returns between the equally weighted tenth (decile) of stocks with the highest prices and the equally weighted decile with the lowest prices. Using monthly prices and total returns for a broad set of German stocks from the end of January 1990 through December 2013, they find that: Keep Reading

Enterprise multiple (EM) is the ratio of enterprise value (EV) to earnings before interest, taxes, depreciation and amortization (EBITDA), with EV market value of equity plus total debt and preferred stock value minus cash and short-term investments. What happens when EM disagrees with other stock valuation metrics? In their October 2016 paper entitled “Why Do Enterprise Multiples Predict Expected Stock Returns?”, Steve Crawford, Wesley Gray and Jack Vogel investigate how EM interacts with other stock valuation metrics. They first sort stocks into fifths (quintiles) ranked by EM and then re-sort EM quintiles into sub-quintiles based on each of 12 fundamental valuation metrics: (1) financial distress; (2) O-Score (probability of bankruptcy); (3) net stock issuance; (4) composite equity issuance; (5) total accruals; (6) net operating assets; (7) momentum; (8) gross profitability; (9) asset growth; (10) return on assets; (11) investment-to-assets ratio; and, (12) a combination metric derived by first ranking stocks based on each of the 11 individual metrics and then averaging ranks for each stock. There are thus 25 double-sort portfolios for each valuation metric. They then focus on two value-weighted hedge portfolios that concentrate disagreement/agreement between EM and other valuation metrics:

• High-mispricing – long (short) stocks with low (high) EMs and high (low) fundamental valuations, representing extreme disagreement.
• Low-mispricing – long (short) stocks with low (high) EMs and low (high) fundamental valuations, representing extreme agreement.

Portfolio reformations are at mid-year annually for all variables except momentum, for which reformations are monthly. They measure portfolio performance based on monthly return, market alpha, three-factor (market, size, book-to-market) alpha and four-factor (adding momentum) alpha. Using prices and firm fundamentals required to construct specified metrics for a broad sample of U.S. common stocks during July 1972 through December 2015, they find that: Keep Reading

Does identification of trends in the CBOE Volatility Index (VIX) via simple moving averages (SMA) support effective timing of the U.S. stock market or VIX futures exchange-traded notes (ETN)? to investigate we consider timing four asset pairs:

1. SPDR S&P 500 (SPY) – ProShares Short S&P500 (SH) since SH inception on 6/21/06.
2. SPY – iShares 1-3 Year Treasury Bond (SHY) since 6/21/06.
3. VelocityShares Daily Inverse VIX ST ETN (XIV) – iPath S&P 500 VIX ST Futures ETN (VXX) since XIV inception on 11/30/10.
4. XIV – SHY since 11/30/10.

SPY and XIV are offensive assets, and SHY and VXX are defensive assets. We consider five individual SMAs to determine VIX trend: 200-day (SMA200); 100-day (SMA100); 50-day (SMA50); 20-day (SMA20); and, 10-day (SMA10). We also consider one “majority rules” combination wherein at least three of the five individual SMAs agree (SMA-Multi). When daily VIX is above (below) its SMA, expected stock market volatility is trending up (down), and we hold the defensive (offensive) asset of the above pairs. We assume a baseline 0.1% for asset switching frictions. Using daily values of the above assets as specified through most of September 2016 (10.3 years for SPY pairs and 5.8 years for XIV pairs), we find that: Keep Reading

Is there a straightforward way to incorporate current business/economic climate into equity market valuation ratios? In their September 2016 paper entitled “Generalized Financial Ratios to Predict the Monthly Equity Market Premium”, Andres Algaba and Kris Boudt introduce and test a generalized price-dividend ratio (GDPR) that takes into account recent business and discount rate conditions, as follows:

Where P is equity market (index) price, D is aggregate market dividend, the beta exponent for D accounts for changes in the kinds of companies dominating the market (those that retain versus those that pay out earnings) and the lambda multiplier for D accounts for variation in the discount rate used to evaluate dividend streams. The t subscripts indicate that all vary over time. They estimate beta and lambda via regressions using rolling historical windows of five or nine years (representing two views of business cycle length). They test the ability of GPDR to predict the U.S. equity market premium (ERP) using inception-to-date forecasting regressions, without and with a rule that switches to the historical average ERP when recent (last three and six months) GPDR predictions are poor. They use the historical average ERP as a benchmark. They employ the first nine years of data to estimate initial GPDR. They then use the next 20 years (1956-1975) for the first predictive regression, leaving 39 years for out-of-sample monthly ERP predictions (1976-2014). To assess the economic value of using GPDR to predict ERP, they consider a risk-averse, mean-variance optimizing investor who each month reallocates across equities and U.S. Treasury bills (T-bills). This investor employs a 5-year rolling window to estimate volatility, does not sell short and limits leverage to 1.5 with one-way trading friction 0.1%. Using monthly levels of the S&P 500 Index, monthly 12-month historical dividends and monthly 3-month T-bill yield as the risk-free rate during January 1947 through December 2014, they find that: Keep Reading

How (and what) does John Bogle think about the stock and bond markets over the next decade? In their October 2015 article entitled “Occam’s Razor Redux: Establishing Reasonable Expectations for Financial Market Returns”, flagged by a subscriber, John Bogle and Michael Nolan revisit simple models for expected stock market and government bond returns first published in 1991. The stock market model distinguishes between: (1) investment return, defined as initial dividend yield plus expected annual earnings growth rate; and, (2) speculative return, defined as annual percentage change in price-to-earnings ratio (P/E). The government bond model uses the initial interest rate as a reasonable expectation for return over the life of the bond. In both models, the investment horizon is a decade. They update performances of the models to include the 25 years since publication and apply them to determine expectations for stock and bond market returns over the decade ahead. Using data for the stock market since 1871 and for 10-year U.S. Treasury notes (or equivalent) since 1915, both through 2014, they find that: Keep Reading

Does cyclic information flow from the Federal Open Market Committee (FOMC) drive equity market returns? In the June 2016 update of their paper entitled “Stock Returns Over the FOMC Cycle”, flagged by a subscriber, Anna Cieslak, Adair Morse and Annette Vissing-Jorgensen investigate interaction of the FOMC six-week meeting cycle with excess U.S. and worldwide stock market (relative to the U.S. Treasury bill). Within the FOMC meeting cycle, they look at:

• Meetings of the Federal Reserve Board of Governors and public releases of Federal Reserve book updates, statements and meeting minutes.
• Other potentially influential economic news cycles, including reserve maintenance period, macroeconomic news releases and corporate earnings announcements.
• Evidence on leaks from the Federal Reserve via media and private newsletters (with focus on Wall Street Journal articles on monetary policy).
• Public statements of Federal Reserve officials and conversations with current and former officials.

Using these data, FOMC meeting dates  and daily U.S. and global (overall, developed and emerging) stock market returns, returns for individual U.S. stocks, U.S. Treasury bill (T-bill) yield and 10-year U.S. Treasury note yield during 1994 through 2015, they find that: Keep Reading

Is global diversification within asset classes disappearing as worldwide economic and financial integration increases? In their August 2016 paper entitled “Globalization and Asset Returns”, Geert Bekaert, Campbell Harvey, Andrea Kiguel and Xiaozheng Wang examine whether economic and financial integration increases global comovement of country equity, bond and currency exchange market returns. They examine three measures of return comovement for each asset class: average pairwise correlation, average beta relative to the world market and average idiosyncratic volatility. They apply these measures separately to developed markets and emerging markets. Using monthly equity, bond and currency exchange market returns in U.S. dollars for 26 developed markets and 32 emerging markets as available from various inceptions through December 2014, they find that: Keep Reading

Are the widely used stock characteristic/factor sorting practices of ranked fifth (quintile) or ranked tenth (decile) portfolios optimal in terms of interpretative power? In their August 2016 paper entitled “Characteristic-Sorted Portfolios: Estimation and Inference”, Matias Cattaneo, Richard Crump, Max Farrell and Ernst Schaumburg formalize the portfolio sorting process. Specifically, they describe how to choose the number of quantile portfolios best suited to source data via a trade-off between variability of outputs and effects of data abnormalities (such as outliers). They illustrate implications of the procedure for the:

• Size effect – each month sorting stocks by market capitalization and measuring the difference in value-weighted average next-month returns between small stocks and large stocks.
• Momentum effect – each month sorting stocks by cumulative return from 12 months ago to one month ago and measuring the difference in value-weighted average next-month returns between past winners and past losers.

Using monthly data for a broad sample of U.S. common stocks during January 1927 through December 2015, they find that: Keep Reading