Volatility Effects

In the introduction to his 2015 book entitled The 3% Signal: The Investing Technique that Will Change Your Life, author Jason Kelly states: “Ideas count for nothing; opinions are distractions. The only thing that matters is the price of an investment and whether it’s below a level indicating a good time to buy or above a level indicating a good time to sell. We can know that level and monitor prices on our own, no experts required, and react appropriately to what prices and the level tell us. Even better, we can automate the reaction because it’s purely mathematical. This is the essence of the 3 percent signal [3Sig]. …Used with common market indexes, this simple plan beats the stock market. …The performance advantage of the 3 percent signal can be yours after just four fifteen-minute calculations per year…” Based on his experience and analyses, he concludes that: Keep Reading

Do lottery traders create the low-volatility (betting-against-beta) effect by overpricing high-beta stocks? In the December 2014 version of their paper entitled “Betting against Beta or Demand for Lottery”, Turan Bali, Stephen Brown, Scott Murray and Yi Tang investigate whether demand for lottery-like stocks drives the empirically low (high) abnormal returns of stocks with high (low) betas. They measure lottery demand for a stock as the average of its five highest daily returns over the past month. They measure beta for a stock as the slope from a regression of its daily excess (relative to the risk-free rate) stock returns versus daily excess stock market returns over the past 12 months. They hypothesize that lottery traders drive current prices of stocks with high lottery demand upward, thereby depressing their expected returns. They further hypothesize that stocks with high lottery demand tend to be high-beta stocks. Using daily and monthly returns and characteristics for a broad sample of U.S. common stocks (excluding those priced under $5), associated firm accounting data and relevant financial variables during July 1963 through December 2012 (594 months), they find that: Keep Reading

Is there a more precise way to measure the premium available to investors willing to bear volatility risk than overall return variance? In their January 2015 paper entitled “Downside Variance Risk Premium”, Bruno Feunou, Mohammad Jahan-Parvar and Cedric Okou investigate the usefulness of  (1) decomposing the variance risk premium (the difference between option-implied and realized variance) into upside and downside components and (2) defining the difference between these components as the skewness risk premium. They use high-frequency (5-minute) S&P 500 Index squared positive (negative) returns plus squared overnight positive (negative) returns to calculate realized upside (downside) variance. They sum upside and downside components to obtain total realized variance. They derive option-implied volatility from the most liquid out-of-the-money S&P 500 Index put and call options. Using intraday S&P 500 Index returns, daily S&P 500 Index option data and monthly yields for 3-month U.S. Treasury bills as the risk-free rate during September 1996 through December 2010, they find that:

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Do stock return anomalies exhibit January and month-of-quarter (first, second or third, excluding January) effects? In his February 2015 paper entitled “Seasonalities in Anomalies”, Vincent Bogousslavsky investigates whether the following 11 widely cited U.S. stock return anomalies exhibit these effects:

1. Market capitalization (size) – market capitalization last month.
2. Book-to-market – book equity (excluding stocks with negative values) divided by market capitalization last December.
3. Gross profitability – revenue minus cost of goods sold divided by total assets.
4. Asset growth – Annual change in total assets.
5. Accruals – change in working capital minus depreciation, divided by average total assets the last two years.
6. Net stock issuance – growth rate of split-adjusted shares outstanding at fiscal year end.
7. Change in turnover – difference between turnover last month and average turnover the prior six months.
8. Illiquidity – average illiquidity the previous year.
9. Idiosyncratic volatility – standard deviation of residuals from regression of daily excess returns on market, size and book-to-market factors.
10. Momentum – past six-month return, skipping the last month.
11. 12-month effect – average return in month t−k*12, for k = 6, 7, 8, 9, 10.

Each month, he sorts stocks into tenths (deciles) based on each anomaly variable and forms portfolios that are long (short) the decile with the highest (lowest) values of the variable. He updates all accounting inputs annually at the end of June based on data for the previous fiscal year. Using accounting data and monthly returns for a broad sample of U.S. common stocks during January 1964 to December 2013, he finds that: Keep Reading

Does the S&P 500 Implied Volatility Index (VIX) exhibit exploitable seasonality? To check, we calculate average monthly change in VIX and and average iPath S&P 500 VIX Short-Term Futures ETN (VXX) monthly return by calendar month. Using monthly closes of VIX since January 1990 and monthly reverse split-adjusted closes for VXX since January 2009, both through December 2014, we find that: Keep Reading

Does the term structure of the the option-implied expected volatility of the S&P 500 Index (VIX, normally measured at a one-month horizon) predict future returns of variance assets such as variance swaps, VIX futures and S&P 500 Index option straddles? In his January 2015 paper entitled “Risk Premia and the VIX Term Structure”, Travis Johnson investigates the relationship between the VIX term structure slope and the variance risk premium as measured by future returns of such assets. He constructs the VIX term structure by each day calculating six values of VIX from prices of S&P 500 Index options with maturities of one, two, three, six, nine and 12 months. He measures the variance risk premium from daily returns of S&P 500 Index variance swaps, VIX futures and S&P 500 Index option straddles of various maturities. Using daily closing quotes for the specified S&P 500 index options and daily returns for the specified variance assets as available during 1996 through 2013, he finds that: Keep Reading

Do country stock markets act like individual stocks with respect to return for risk taken? In his December 2014 paper entitled “Is There a Low-Risk Anomaly Across Countries?”, Adam Zaremba relates country stock market performance to four market risk metrics: beta (relative to the capitalization-weighted world stock market), standard deviation of returns, value at risk (fifth percentile of observations) and idiosyncratic (unexplained by world market) volatility. He uses historical intervals of 12 to 24 months as available to estimate risk metrics. He then forms capitalization-weighted portfolios of country markets by ranking them into fifths (quintiles) based on risk metric sorts. He also investigates whether risk/size and risk/book-to-market ratio double-sorts enhance country-level size and value effects. Using monthly returns and accounting data for 78 existing and discontinued country stock market indexes in U.S. dollars during February 1999 through September 2014, he finds that: Keep Reading

Are stock betas calculated with price jumps (arguably derived from informed trading) more useful than those calculated conventionally (arguably dominated by noise trading)? In the December 2014 version of their paper entitled “Roughing Up Beta: Continuous vs. Discontinuous Betas, and the Cross-Section of Expected Stock Returns”, Tim Bollerslev, Sophia Zhengzi Li and Viktor Todorov compare the powers of standard or “smooth” stock betas and jumpy or “rough” stock betas to predict stock returns. They measure smooth beta in two ways: from 75-minute returns during normal trading hours; and, from daily close-to-close returns. They measure rough beta also in two ways: from unusual jumps among 75-minute returns during normal trading hours; and, from close-to-open (overnight) returns. For all beta measurements, they employ the past year as the measurement interval. Using intraday prices and firm characteristics for the 985 stocks included in the S&P 500 Index during 1993 through 2010 (an average of 738 stocks per month), they find that: Keep Reading

How diverse are the beliefs of experts on the Capital Asset Pricing Model (CAPM)? In his November paper entitled “CAPM: The Model and 233 Comments about It”, Pablo Fernandez reproduces 52 largely disagreeing and 181 largely agreeing comments solicited from professors, finance professionals and Ph.D. students regarding his prior paper entitled “CAPM: an Absurd Model” (summarized in “Forget CAPM Beta?”). The range of beliefs in the comments is extreme, from

“I was shocked at how horrible your paper is. It is without a doubt the worst excuse for an academic study I have ever seen (and believe me that is saying a lot).”

to

“I totally agree with the absurdity of CAPM model.”

After reflecting on the body of comments, he concludes that: Keep Reading

Are gains from tax-loss harvesting, the systematic taking of capital losses to offset capital gains, additive to or subtractive from premiums from portfolio tilts toward common factors such as value, size, momentum and volatility (smart beta)? In their October 2014 paper entitled “Factor Tilts after Tax”, Lisa Goldberg and Ran Leshem look at the effects on portfolio performance of combining factor tilts and tax-loss harvesting. They call the incremental return from tax-loss harvesting tax alpha, which (while investor-specific) is typically in the range 1%-2% per year for wealthy investors holding broad capitalization-weighted portfolios. They test six long-only factor tilts based on Barra equity factor models: (1) value (high earnings yield and book-to-market ratio); (2) momentum (high recent past return); (3) value/momentum; (4) small/value; (5) quality (value stocks with low earnings variability, leverage and volatility); and, (6) minimum volatility/value (low volatility with diversification constraint and value tilt). Their overall benchmark is the MSCI All Country World Index (ACWI). Their tax alpha benchmark derives from a strategy that harvests losses in a capitalization-weighted portfolio (no factor tilts) without deviating far from the overall benchmark. The rebalancing interval is monthly for all portfolios. Using monthly returns for stocks in the benchmark index during January 1999 through December 2013, they find that: Keep Reading