Volatility Effects

Do often frenzied investors/traders tend to overdo buying and selling, coming to their senses shortly thereafter? In other words, does the broad U.S. stock market tend to revert after short-term moves up or down? To check, we relate sequential past and future return intervals of 1, 2, 3, 5, 10, 15 and 21 trading days. Using daily closes of the S&P 500 Index over the period January 1928 through mid-March 2022, we find that: Keep Reading

How should the variability of stock market returns shape the outlooks of short-term traders and long-term investors? How strong is the tailwind of the general drift upward in stock prices? How powerful is the turbulence of variability? Does the tailwind ever overcome the turbulence? To investigate we consider all holding periods for the S&P 500 Index ranging from one week to 208 weeks (about four years). Using weekly closes for the index during January 1928 through mid-March 2022 (4,915 weeks or about 94 years), we find that:

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What happens after extreme up days or extreme down days for the U.S. stock market? To investigate, we define extreme up or down days as those with daily returns at least X standard deviations above or below the average daily return over the past four years (the U.S. political cycle, about 1,000 trading days). This methodology allows identification of extreme days for the S&P 500 Index starting in January 1932. Focusing on three standard deviations, we then look at average returns and return variabilities over the next 63 trading days (three months). Using daily closes for the S&P 500 Index during January 1928 through late January 2022, we find that:

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Do widely accepted equity factor premiums exist in data older than generally employed in academic studies? In their November 2021 paper entitled “The Cross-Section of Stock Returns before 1926 (And Beyond)”, Guido Baltussen, Bart van Vliet and Pim van Vliet look for some of the most widely accepted factor premiums in a newly assembled sample of U.S. stocks spanning January 1866 through December 1926 (61 years of additional and independent data). Specifically, they look at: size as measured by market capitalization; value as measured by dividend yield (strongly associated with earnings during the sample period); stock price momentum from 12 months ago to one month ago; short-term (1-month) return reversal; and, risk as measured by market beta. They use only those stocks which trade frequently and apply liquidity/data quality filters. To measure factor premiums, they each month for each factor:

• Regress next-month stock return versus stock factor value and compute slopes of the relationship.
• Reform a value-weighted hedge portfolio that is long (short) stocks with high (low) expected returns based on factor values to measure: (1) average factor portfolio gross return; and, (2) gross factor (CAPM) alphas and betas based on regression of factor portfolio excess return versus market excess return.

They further investigate economic explanations of factor premiums and test machine learning methods found successful with recent data. Using monthly prices, dividends and market capitalizations for 1,488 stocks in the new database, they find that:

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Is the ratio of implied volatility of implied volatility (CBOE VVIX Index), interpretable as a measure of changes in investor fear level, to CBOE VIX Index itself a useful indicator of future stock market returns? To investigate, we relate monthly VVIX/VIX and monthly change in VVIX/VIX to monthly SPDR S&P 500 (SPY) total returns. Using end-of-month levels of both VVIX and VIX and dividend-adjusted monthly SPY closes during January 2007 (limited by VVIX) through July 2021, we find that:

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Is periodic rebalancing of a stock portfolio advantageous? In his May 2021 paper entitled “Does Volatility Harvesting Really Work?”, Magnus Pedersen compares performances of periodic rebalancing versus no rebalancing (buy-and-hold) for thousands of randomly constructed and initially equal-weighted portfolios of U.S. stocks. He tries different numbers of holdings, different start/end dates and different rebalancing intervals (daily, weekly, monthly, annually). He looks at arithmetic average returns, geometric average returns, simplified Sharpe ratios and maximum drawdowns of competing portfolios. Using daily stock-returns for over 900 large and liquid U.S. stocks during January 2007 through early 2021, he finds that: Keep Reading

Is buying assets during banking crises, when assets appear to be at deep discounts, an attractive long-run strategy? In their January 2021 paper entitled “Investing in Crises”, Matthew Baron, Luc Laeven, Julien Penasse and Yevhenii Usenko investigate asset returns across several years before and after banking crises, for which they identify the onset (first month) in three ways:

1. Systemwide banking panics (as specified in a prior paper).
2. Multiple major government interventions (as specified in a prior paper).
3. 30% drop in a country’s bank stock index (bank equity crash).

They test trading strategies in which a U.S. investor exploits banking crises around the world as they occur and otherwise holds U.S. Treasury bills (T-bill). They focus on bank stock and other (non-financial) stock indexes, but also consider government bonds, currencies and residential real estate. Using monthly asset index returns in both local currencies and U.S. dollars, monthly U.S. T-bill yield, crisis starting months and economic data across 44 developed and emerging market countries during 1960 through 2018, they find that: Keep Reading

Can investors use leveraged exchange-traded funds (ETF) as building blocks for long-term portfolios? In his January 2021 presentation package entitled “One Year Later. Leveraged ETFs in Portfolio Construction and Portfolio Protection”, Mikhail Smirnov updates multi-year performance of a monthly rebalanced partially 3X-leveraged portfolio consisting of:

• 40% ProShares UltraPro QQQ (TQQQ)
• 20% Direxion Daily 20+ Year Treasury Bull 3X Shares (TMF)
• 40% iShares 20+ Year Treasury Bond ETF (TLT)

The last three years are out-of-sample with respect to specification of this portfolio. He also looks at a more conservative portfolio of 20% TQQQ and 80% TLT, rebalanced monthly. Using pre-inception simulated and actual monthly total returns for these ETFs during January 1, 2005 through January 15, 2021, he finds that: Keep Reading

Does the low-risk stock anomaly hold for China A shares, dominated by local private investors rather than institutions and characterized by high volatility and herding? In their January 2021 paper entitled “The Volatility Effect in China”, David Blitz, Matthias Hanauer and Pim van Vliet examine the performance of low-volatility China A shares. At the end of each month, they rank these stocks into value-weighted tenths (deciles) based on volatility or market beta over the last 36 months. To ensure comparability to other widely studied factors, they then construct a volatility (VOL) factor following the Fama-French 2×3 factor portfolio construction method. To mitigate concerns about exploitability, they exclude micro-cap stocks and set size breakpoints using only large mid-cap stocks stocks. They calculate next-month excess total returns in U.S. dollars relative to the 1-month U.S. Treasury bill (T-bill) yield. For comparison, they similarly construct and measure returns for size, value, profitability, investment and momentum factor portfolios among China A shares. Using monthly total returns and monthly accounting data for all constituents of the MSCI China A Onshore Index and the  MSCI China A Onshore Investable Market Index (about 1,200 stocks per month on average) and monthly T-bill yield during November 2000 through December 2018, they find that:

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Do differences in equity sector responses to stock market crashes (and associated volatility spikes) support an exploitably attractive sector rotation strategy? In the November 2020 update of his paper entitled “Actively Using Passive Sectors to Generate Alpha Using the VIX”, Michael Gayed examines a cyclical-defensive sector rotation strategy using the level of the CBOE S&P 500 Volatility Index (VIX) as trigger. Specifically, he iteratively favors defensive sectors (Utilities, Consumer Staples and Healthcare) when daily VIX is relatively low in anticipation of VIX increases and favors cyclical sectors (Technology, Industrials, Materials and Consumer Discretionary) to buy into high-VIX panics and benefit from VIX reversion. He considers:

• Three allocation schemes: (1) 100% long favored sectors and 100% short unfavored sectors; (2) 100% long favored sectors; and, (3) overweighting (underweighting) favored (unfavored) sectors by 5% to form a modified S&P 500 Index.
• Two weighting schemes for sets of defensive and cyclical sectors: (1) equal weighting, and (2) S&P 500 sector weighting.
• Two ways of applying the Nelder-Mead method to identify daily VIX levels that trigger defensive-to-cyclical and cyclical-to-defensive switches: (1) using only the first five years (1999-2004) of the full sample, and (2) using a 5-year rolling window throughout the sample period.

Using daily levels of VIX and the S&P 500 Index and daily prices of Select Sector SPDR Exchange Traded Funds (ETF) during January 1999 through October 2020, with strategy tests starting January 2005, he finds that: Keep Reading