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Volatility Effects

Reward goes with risk, and volatility represents risk. Therefore, volatility means reward; investors/traders get paid for riding roller coasters. Right? These blog entries relate to volatility effects.

Use Short-term S&P 500 Index Indicators to Predict VIX Futures?

Does the S&P 500 Index (SPX) or the CBOE Volatility Index (VIX) yield better short-term trading signals for stocks and VIX futures? In the May 2024 revision of his paper entitled “Chicken and Egg: Should you use the VIX to time the SPX? Or use the SPX to time the VIX?”, Robert Hanna explores mutual predictive relationships between SPX and VIX, with an eye toward exploitation via market timing strategies. He considers several long-term trend indicators to investigate whether SPX or VIX data offers better SPX return predictions. He considers two types of short-term overbought/oversold predictive rules: (1) short-term relative strength index (RSI) readings of 2, 3 and 4 days; and, (2) short-term high and low readings of 5 to 25 days in length. He applies both sets of short-term rules separately to SPX and VIX to predict movements of SPX and VIX futures. Using daily SPX and VIX levels since 1990 and short-term VIX futures prices since 2007, all through 2023, he finds that: Keep Reading

Are Low Volatility Stock ETFs Working?

Are low volatility stock strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider eight of the largest low volatility ETFs, all currently available, in order of longest to shortest available histories:

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the low volatility stock ETFs and their benchmark ETFs as available through May 2024, we find that: Keep Reading

Are IPO ETFs Working?

Are exchange-traded funds (ETF) focused on Initial Public Offerings of stocks (IPO) attractive? To investigate, we consider three of the largest IPO ETFs and one recent Special Purpose Acquisition Company (SPAC) ETF, one of which is no longer available, in order of longest to shortest available histories:

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). For all these ETFs, we use SPDR S&P 500 (SPY) as the benchmark. Using monthly returns for the IPO ETFs and SPY as available through April 2024, we find that:

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Invest with the Fed?

Does Federal Reserve (Fed) policy strongly and differently affect individual stock? In his April 2024 paper entitled “Navigating Federal Reserve Policy with IFED”, Rufus Rankin analyzes performance of the Invest With the Fed (IFED) stock selection strategy, which selects portfolios positioned to prosper across environments signaled by Fed actions. Specifically, the strategy selects individual equities based on 12 factors, adjusting weights of these factors based on Fed policy signals. The strategy rebalances with Fed policy changes or in June when there is no policy change for a year. He looks at two indexes representing different versions of the strategy:

  1. IFED US-Large Cap Index (IFED-L), with the S&P 500 Index (S&P 500) as a benchmark.
  2. IFED US Large-Cap Low Volatility Index (IFED-LV), with the S&P 500 Low Volatility Index (S&P 500 LV) as a benchmark.

Using monthly returns during April 2002 through September 2023, he finds that: Keep Reading

Inverse-volatility Weighting of Volatility Assets

Can long volatility investors improve performance of their portfolios by scaling positions inversely to some measure of volatility? In his March 2024 paper entitled “Volatility-Managed Volatility Trading”, Aoxiang Yang tests volatility risk premium (VRP) timing strategies that hold a volatility asset and a risk-free asset, with the weight of the former inverse to some measure of volatility. He considers three volatility assets that are each month:

  1. Long a 1-month variance swap contract, held to maturity (with prices sometimes approximated using VIX-squared).
  2. Long a 1-month constant-maturity VIX futures portfolio (ignoring both a margin requirement and frictions required to maintain constant maturity).
  3. Short a 1-month constant-maturity S&P 500 Index at-the-money (ATM) straddle (including a margin requirement of 100% of selling proceeds plus 20% of current S&P 500 Index level, but ignoring frictions required to maintain constant maturity).

Each month, he weights each asset by one of four measures related to stock market volatility:

  1. Inverse of realized volatility.
  2. Inverse of implied volatility (VIX).
  3. Inverse of an autoregression forecast of next-month volatility.
  4. Forecast of next-month VRP (which has an inverse VIX term) from a vector autoregression of realized volatility and VIX.

For each measure of volatility, he multiplies by a scaling constant that makes the respective long volatility portfolio have the same standard deviation of monthly returns as the S&P 500 Index. His benchmark portfolios hold the same assets with constant weights. He further analyzes performance of volatility portfolios during times of high volatility (highest 20%) and low volatility (lowest 80%). Using estimates for actual monthly prices for variance swaps during 1990-2023 (and actual prices for recent subperiods), for a constant-maturity VIX futures portfolio during 2004-2023 and for a constant-maturity S&P 500 Index ATM straddles portfolio during 1996-2022, he finds that: Keep Reading

Using SVXY to Capture the Volatility Risk Premium

In response to “Shorting VXX with Crash Protection”, which investigates shorting iPath S&P 500 VIX Short-Term Futures (VXX) to capture the equity volatility risk premium, a subscriber asked about instead using a long position in ProShares Short VIX Short-Term Futures (SVXY). To investigate, we consider two scenarios based on monthly measurements:

  1. Buy and Hold – buy an initial amount of SVXY and let this position ride indefinitely. This is a long-term investment strategy.
  2. Monthly Skim – buy the same initial amount of SVXY and move to SPDR Bloomberg 1-3 Month T-Bill ETF (BIL) any month-end gains over the initial investment (the beginning-of-month SVXY position may become smaller, but not larger, than the initial investment). This is more an income-generating investment strategy.

The offeror changed the SVXY investment objective at the end of February 2018 (when short VIX strategies crashed), more conservatively targeting henceforth -0.5 times the daily performance of the S&P 500 VIX Short-Term Futures Index rather than -1.0 times as before. We therefore examine SVXY performance separately before and after that change. We assume switching frictions of 0.25% for movements of funds from SVXY to BIL in scenario 2. Using monthly adjusted closing prices for SVXY and BIL during October 2011 through December 2023, we find that: Keep Reading

Simple Ways to Beat Equal-weighted Stock Portfolios

Academic studies of stock portfolio optimization often use an equal-weighted (EW) strategy as benchmark. Are there simple EW enhancements that researchers ought to consider instead? In their December 2023 paper entitled “Outperforming Equal Weighting”, Antonello Cirulli and Patrick Walker test three sets of enhanced long-only EW portfolios relying solely on past returns:

  1. Momentum-enhanced EW – sort stocks into tenths (deciles) from lowest to highest average weekly return over the last 12 months.
  2. Volatility-enhanced EW – sort stocks into deciles from highest t0 lowest standard deviation of weekly returns over the last five years.
  3. Sharpe ratio-enhanced EW – sort stocks into deciles from lowest to highest Sharpe ratio calculated with weekly returns over the last years.

For each set, they then exclude the bottom 1, 2, 3, 4 or 5 deciles and weight stocks in retained deciles equally for a total of 15 enhanced EW portfolios. They reform all portfolios on the first Wednesday of each month. They then compare net performances of these portfolios to those of simple EW and capitalization-weighted portfolios of all stocks in the universe after debiting 0.1% frictions for turnover. They focus on large-capitalization/liquid stocks and check robustness of findings to subperiods, lookback intervals, level of frictions and rebalancing frequency. Using weekly returns in U.S. dollars, adjusted for splits and dividends, of MSCI USA, Europe, Emerging Markets and Developed Markets stocks starting five years before the test period of April 2002 through March 2022, they find that:

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Effects of Market Volatility on Market Trend Strategies

Does market volatility predictably affect returns to simple moving average (SMA) trend-following strategies? In their November 2023 paper entitled “Market Volatility and the Trend Factor”, Ming Gu, Minxing Sun, Zhitao Xiong and Weike Xu investigate how stock market volatility affects multi-SMA trend factor profitability. They first assess significance of the trend factor premium, as follows:

  • For each stock at the close on the last trading day of each month:
    • Compute SMAs of prices for lookback intervals of 3, 5, 10, 20, 50, 100, 200, 400, 600, 800 and 1000 trading days, and divide each SMA by the end price.
    • Starting five years into the sample period (1931), regress next-month stock returns on corresponding monthly SMA ratios over the past 60 months.
    • Average the SMA ratio regression coefficients separately over the past 12 months to estimate next-month coefficients and apply these coefficients to estimate next-month return.
  • At the end of each month, sort all stocks into tenths, or deciles, based on estimated next-month returns and form a trend factor hedge portfolio that is long (short) the equal-weighted top (bottom) decile. The trend factor premium is the monthly gross return for this portfolio.

They then assess how trend factor hedge portfolio returns interact with monthly stock market return volatility (standard deviation of monthly value-weighted market returns over the past 12 months) by specifying volatility has high or low when its prior-month value is above or below the full-sample median. Using data for all listed U.S. common stocks, excluding those priced below $5 or in the lowest tenth of NYSE market capitalizations, during January 1926 through December 2022, they find that:

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Sector Rotation Based on Relative Rotation Graphs

Do Relative Rotation Graphs (RRG), which visually segregate assets into leading, weakening, lagging or improving quadrants by relative performance, effectively identify equity sectors with relatively strong future returns? In his September 2023 paper entitled “Dynamic Sector Rotation”, John Rothe tests an RRG-based sector relative momentum strategy with stop-loss risk management based on volatility. Specifically, he:

  • Selects a universe of 31 sector sector/subsector exchange-traded funds (ETFs) based on daily trading volume, years in existence, overlap with other sector/subsectors, assets under management and liquidity.
  • Each week, holds the equal-weighted top 5 ETFs crossing into the RRG improving quadrant.
  • Manages the risk of each holding continuously via a Wilder Volatility Stop with a 5-day range.
  • Assumes a 2% annual management fee.

His benchmark is the S&P 500 Momentum Index. Using weekly returns for the selected ETF universe during a test period spanning January 2013 through mid-2023, he finds that: Keep Reading

Machine Stock Return Forecast Disagreement and Future Return

Is dispersion of stock return forecasts from different machine learning models trained on the same history (as a proxy for variation in human beliefs) a useful predictor of stock returns? In their August 2023 paper entitled “Machine Forecast Disagreement”, Turan Bali, Bryan Kelly, Mathis Moerke and Jamil Rahman relate dispersion in 100 monthly stock return predictions for each stock generated by randomly varied versions of a machine learning model applied to 130 firm/stock characteristics. They measure machine return forecast dispersion for each stock as the standard deviation of predicted returns. They then each month sort stocks into tenths (deciles) based on this dispersion, form either a value-weighted or an equal-weighted portfolio for each decile and compute average next-month portfolio return. Their key metric is average next-month return for a hedge portfolio that is each month long (short) the stocks in the lowest (highest) decile of machine return forecast dispersions. Using the 130 monthly firm/stock characteristics and associated monthly stock returns for a broad sample of U.S. common stocks (excluding financial and utilities firms and stocks trading below $5) during July 1966 through December 2022, they find that:

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