Volatility Effects

Does combining avoidance of fat tail losses with a traditional portfolio optimization strategy enhance performance? In her January 2011 paper entitled “The Economic Value of Controlling for Large Losses in Portfolio Selection”, Alexandra Dias investigates the effectiveness of combining tail loss risk management with minimum variance efficiency. This approach essentially seeks to add avoidance of Black Swans to the benefit of diversification. The investigation consists of testing four long-only strategies using 224 months of rolling historical returns on all possible combinations of three Dow Jones Industrial Average (DJIA) stocks by choosing each month: (1) the minimum variance portfolio with the smallest variance (benchmark strategy); (2) the minimum variance portfolio with the smallest probability of a large loss; (3) the minimum variance portfolio with the thinnest losses tail; and, (4) the minimum Value at Risk (VaR) portfolio with the smallest VaR. Strategies (2), (3) and (4) are alternatives for managing return distribution tail risk. Using monthly returns for the 24 DJIA stocks for which which prices are available during February 1973 through June 2010 (allowing 2,024 combinations of three stocks), she finds that: Keep Reading

Can investors straightforwardly diversify equity portfolios with volatility futures? In the January 2011 draft of their paper entitled “The Hazards of Volatility Diversification”, Carol Alexander and Dimitris Korovilas explore the potential benefits and costs of combining ‘buy-and-hold’ positions in volatility futures with a long-term equity portfolio. Specifically, they examine diversification of long exposure to the S&P 500 Index via S&P Depository Receipts (SPY) with a rolling long position in VIX futures. They distinguish “diversification” from “hedging” based on permanence of positions. They consider three VIX futures strategies using one-month-to-expiration, three-month-to-expiration or longest-maturity-available series, with rollover either at or five business days before expiration. Using daily trading data for VIX futures, SPY and the 1-month Treasury bills from March 26, 2004 through December 31, 2010 (about 69 months), they find that: Keep Reading

Are there stock/firm characteristics that usefully predict which stocks will exhibit extreme returns? In their January 2011 paper entitled “Predicting Extreme Returns and Portfolio Management Implications”, Kevin Krieger, Andy Fodor, Nathan Mauck and Greg Stevenson investigate the predictability of extreme returns for individual stocks and the practical import of such predictability for investment portfolios. The define stocks with extreme returns by year as those within the highest 3% or lowest 2.5% of equity returns, or those delisted for bankruptcy or bankruptcy-like reasons. Using implied volatility, stock price/return, trading volume, firm age, accounting and analyst forecast data for the period is 1996 through 2008 (1,100 to 1,500 firms per year with sufficient data), they find that: Keep Reading

Is there a reliable and exploitable cross-sectional relationship between beta and future returns? In the October 2010 draft of their paper entitled “Betting Against Beta”, Andrea Frazzini and Lasse Pedersen investigate exploitability of historical beta within U.S. equities and 19 other stock markets, across 20 global equity markets, and for Treasuries, corporate bonds and commodity futures. They argue that leverage-inhibited investors overweight high-beta assets as a leverage substitute (preferring unleveraged risky assets to leveraged safe assets), and this demand results in overpricing of high-beta assets relative to low-beta assets. Their principal investigatory tool is a market-neutral “betting-against-beta” portfolio that is long low-beta assets (leveraged up to an aggregate beta of one) and short high-beta assets (de-leveraged to an aggregate beta of one), reformed monthly. Calculations of historical beta vary somewhat depending on the data available. Using asset return data as available through 2009 for a variety of assets and asset classes (for example, 1926-2009 for individual U.S. stocks), they find that: Keep Reading

What is the best way to measure and interpret market reaction to new information? In their October 2010 paper entitled “Measuring Flow Toxicity in a High Frequency World”, David Easley, Marcos López de Prado and Maureen O’Hara introduce a new method to estimate the degree to which trading in financial markets is informed. They name this metric Volume-Synchronized Probability of Informed Trading (VPIN), approximated by the fraction of trading volume that is imbalanced (absolute difference between seller-initiated and buyer-initiated volumes, divided by total volume).  Their approach builds on three beliefs: (1) new orders indicate arrival of new information potentially predictive of subsequent price moves; (2) a specific volume of trades therefore represents a more consistent metric for information arrival than an interval of time; and, (3) a trade imbalance is the hallmark of arrival of important information. In a related November 2010 paper entitled “The Microstructure of the ‘Flash Crash’: Flow Toxicity, Liquidity Crashes and the Probability of Informed Trading”, these same authors focus this method on the May 6, 2010 market crash. Using high-frequency (one-minute intervals) price and volume data for a variety of futures contracts during January 2008 through August 2010 to construct rolling sets of equal-volume increments, they find that: Keep Reading

Does the relatively illiquid, opaque, retail environment of over-the-counter (OTC) stocks make them behave differently from comparable listed stocks? In their November 2010 paper entitled “The Cross Section of Over-the-Counter Equities”, Andrew Ang, Assaf Shtauber and Paul Tetlock test the abilities of market capitalization, book-to-market ratio, liquidity, return momentum and idiosyncratic volatility to predict OTC stock returns and compare results to those for listed stocks with comparable market capitalizations. As a part of the study, they examine hedge portfolios that are long/short extreme fifths of OTC stocks ranked by these characteristics to estimate of the magnitudes of the respective  premiums. Using trading volumes, market capitalizations, book-to-market ratios (as available) and closing, bid and ask prices for a large sample of OTC-only firms with at least one Financial Industry Regulatory Authority market maker, and for comparable listed firms, during 1975 through 2008, they find that: Keep Reading

Author Jeff Augen describes his 2010 book Trading Realities: The Truth, the Lies, and the Hype In-Between as “designed to help investors understand the economic and political forces that drive financial markets and to invest alongside those forces instead of against them. It also provides a blunt assessment of the limitations that most private investors face. Understanding these limitations and being able to manage risk are as important as choosing the right investments.” Some notable points from the book are: Keep Reading

Is aggregate equity investor fear multifaceted? In the August 2010 version of his paper entitled “The Equity Fear Premium and Daily Comovements of the S&P 500 E/P ratio and Treasury Yields before and during the 2007 Financial Crisis”, Christophe Faugère introduces a Total Fear Premium derived from independent flight-to-safety and flight-to-liquidity impulses. The flight-to-safety component assumes investors fleeing stocks buy long-term Treasuries, driving the real, after-tax yield on Treasury bonds below long-term trend (growth rate of real Gross Domestic Product per capita). The flight-to-liquidity component assumes investors fleeing stocks buy short-term instruments (such as Treasury bills), driving their yields below the federal funds target rate. Using daily levels of the S&P 500 forward earnings yield, Treasury yields and S&P 500 Index implied volatility (VIX) over the period July 2004 through February 2010, he finds that: Keep Reading

Is beta an effective tool for selecting exchange-traded funds (ETF)? In their October 2010 paper entitled “Black Swans, Beta, Risk, and Return”, Javier Estrada and Mari­a Vargas investigate the usefulness of beta as a metric for constructing portfolios of country and industry ETFs. They use the MSCI world market index (consisting of developed markets only before 1988 and both developed and emerging markets thereafter) as the beta reference. They calculate beta based on a rolling historical window of the most recent 36-60 months (depending on data availability). They arbitrarily define black swan months as those with a world market index return of at least 5% down or up. Using monthly total returns of MSCI indexes for 47 countries (23 developed and 24 emerging) and 57 industries from the earliest month available for each (1970 for the oldest) through December 2009, they find that: Keep Reading

Does high-frequency trading amplify noise and thereby reduce the signal-to-noise ratio in stock returns? In his August 2010 paper entitled “The Effect of High-Frequency Trading on Stock Volatility and Price Discovery”, Frank Zhang examines the effect of high-frequency trading on stock price volatility and on incorporation of fundamental news into price. He defines high-frequency trading as that driven by fully automated trading strategies with very high trading volume and extremely short holding periods ranging from milliseconds to minutes and possibly hours (typically not overnight). He estimates the volume of high-frequency trading as the residual after accounting for institutional and individual investor activities. Using price, trading and institutional holdings data for a broad sample of U.S. stocks from the first quarter of 1985 through the second quarter of 2009, he finds that: Keep Reading