Volatility Effects

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: More…

Are there economic and financial variables that meaningfully predict financial markets return volatility? In the January 2011 draft of their paper entitled “A Comprehensive Look at Financial Volatility Prediction by Economic Variables”, Charlotte Christiansen, Maik Schmeling and Andreas Schrimpf investigate the ability of macroeconomic and financial variables to predict volatility of returns for four major asset classes (foreign exchange, bonds, stocks and commodities). To calculate return volatilities, they use: (1) spot price changes for various currencies against the U.S. dollar for foreign exchange; (2) 10-year Treasury note futures contract prices for bonds; (3) S&P 500 Index futures contract prices for stocks; and, (4) S&P GSCI commodity index levels for commodities. They use an autoregressive (simple volatility persistence) model as a benchmark. Using monthly observations of 29 macroeconomic and financial variables and realized return volatilities for the four asset classes during 1983 through 2008 (311 months), they find that: More…

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: More…

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: More…

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: More…

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: More…

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: More…

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 María 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: More…

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: More…

Should long-term investors view their retirement portfolios more like houses than savings plans? In other words, should they start out with considerable leverage and draw the leverage down gradually over time? In their October 2010 paper entitled “Diversification Across Time”, Ian Ayres and Barry Nalebuff investigate the effects of initially implementing and then gradually phasing out leverage for long-term (retirement) equity investment. This strategy exploits the large present value of investments made early in life, while protecting accumulated wealth from equity market volatility late in life. Tests limit initial leverage to 200%. Using return data for U.S. stocks and margin interest rate estimates over the period 1871 through 2009, they conclude that: More…