Volatility Effects

Is the ability of the VIX percentile threshold rule described in “Using VIX and Investor Sentiment to Explain Stock Market Returns” to explain future stock market excess return in-sample readily exploitable out-of-sample? To investigate, we test a strategy (VIX Percentile Strategy) that each month holds SPDR S&P 500 ETF Trust (SPY) or 3-month U.S. Treasury bills (T-bills) according to whether a recent end-of-month level of the CBOE Volatility Index (VIX) is above or below a specified inception-to-date (not full sample) percentage threshold. To test sensitivities of the strategy to settings for its two main features, we consider:

• Each of 70th, 75th, 80th, 85th or 90th percentiles as the VIX threshold for switching between T-bills and SPY.
• Each of 0, 1, 2 or 3 skip months between VIX measurement and strategy response.

We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as essential performance metrics and use buy-and-hold SPY as a benchmark. We do not quantify frictions due to switching between SPY and T-bills for the VIX Percentile Strategy. Using end-of-month VIX levels since January 1990 and dividend-adjusted SPY prices and T-bill yields since January 1993 (SPY inception), all through May 2023, we find that: Keep Reading

Do stock market return volatility (as a measure of risk) and aggregate investor sentiment (as a measure of risk tolerance) work well jointly to explain stock market returns? In their June 2023 paper entitled “Time-varying Equity Premia with a High-VIX Threshold and Sentiment”, Naresh Bansal and Chris Stivers investigate the in-sample power an optimal CBOE Volatility Index (VIX) threshold rule and a linear Baker-Wurgler investor sentiment relationship to explain future variation in U.S. stock market excess return (relative to U.S. Treasury bill yield). They skip one month between VIX/sentiment measurements and stock market returns to accommodate investor digestion of new information. They consider return horizons of 1, 3, 6 and 12 months. They also extend this 2-factor model to include the lagged Treasury implied-volatility index (ICE BofAML MOVE Index) as a third explanatory variable. Using monthly excess stock market return and VIX during January 1990 through December 2022, monthly  investor sentiment during January 1990 through June 2022 and monthly MOVE index during October 1997 through December 2022, they find that:

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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:

• Invesco S&P 500 Low Volatility Portfolio (SPLV) – the 100 stocks from the S&P 500 Index with the lowest realized volatility over the past 12 months, reformed quarterly. The benchmark ETF for SPLV is SPDR S&P 500 (SPY).
• iShares Edge MSCI Min Vol USA (USMV) – seeks to track an index composed of U.S. equities that, in the aggregate, have lower volatility characteristics relative to the broader U.S. equity market. The benchmark ETF for USMV is iShares Russell 3000 (IWV).
• iShares Edge MSCI Min Vol EAFE (EFAV) – seeks to track an index composed of developed market equities that, in the aggregate, have lower volatility characteristics relative to the broader developed equity markets, excluding the U.S. and Canada. The benchmark ETF for EFAV is iShares MSCI EAFE Index (EFA).
• iShares Edge MSCI Min Vol Emerging Markets (EEMV) – seeks to track an index composed of emerging market equities that, in the aggregate, have lower volatility characteristics relative to the broader emerging equity markets. The benchmark ETF for EEMV is iShares MSCI Emerging Markets Index (EEM).
• iShares Edge MSCI Min Vol Global (ACWV) – seeks to track an index composed of developed and emerging market equities that, in the aggregate, have lower volatility characteristics relative to the broader developed and emerging equity markets. The benchmark ETF for ACWV is iShares MSCI ACWI (ACWI).
• Invesco S&P International Developed Low Volatility Portfolio (IDLV) – the 200 least volatile stocks of the S&P Developed excluding U.S. and South Korea LargeMid Cap BMI Index over the past 12 months, reformed quarterly. The Index is computed using net return, which withholds taxes applicable to non-resident investors. The benchmark ETF for IDLV is Vanguard FTSE All-Wld ex-US ETF (VEU).
• Invesco S&P MidCap Low Volatility Portfolio (XMLV) –  the 80 out of 400 medium-capitalization stocks from the S&P MidCap 400 Index with the lowest realized volatility over the past 12 months, reformed quarterly. The benchmark ETF for XMLV is SPDR S&P MidCap 400 (MDY).
• Invesco S&P SmallCap Low Volatility Portfolio (XSLV) – the 120 out of 600 small-capitalization stocks from the S&P SmallCap 600 Index with the lowest realized volatility over the past 12 months, reformed quarterly. The benchmark ETF for XSLV is SPDR S&P 600 Small Cap (SLY).

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 2023, we find that: Keep Reading

What characteristics of U.S. equity factor return series are most relevant to respective factor performance? In his May 2023 paper entitled “The Cross-Section of Factor Returns” David Blitz explores long-term average returns and market alphas, 60-month market betas and factor performance cyclicality for U.S. equity factors. He also assesses potentials of three factor rotation strategies: low-beta, seasonal and return momentum. Using monthly returns for 153 published U.S. equity market factors, classified statistically into 13 groups, during July 1963 through December 2021, he finds that:

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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, all currently available with moderate trading volumes, in order of longest to shortest available histories:

• First Trust US Equity Opportunities ETF (FPX) – seeks gross investment results that track price and yield of the IPOX-100 U.S. Index.
• Renaissance IPO ETF (IPO) – reflects approximately the top 80% of new public firms weighted by free float capitalization with a 10% cap for any one position. Large IPOs enter quickly and others enter during quarterly reviews, while all exit two years after initial trade date.
• First Trust International Equity Opportunities ETF (FPXI) – seeks gross investment results that track price and yield of the IPOX International Index.
• Defiance Next Gen SPAC Derived ETF (SPAK) – 60% weight to IPOs derived from SPACs and 40% weight to common stock of newly listed SPACs, excluding warrants (dead as of the end of August 2022).

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 2023, we find that:

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A subscriber requested review of a strategy that seeks to exploit “Sell in May” by switching between risk-on assets during November-April and risk-off assets during May-October, with assets specified as follows:

• Risk-on – SPDR S&P 500 (SPY), iShares Russell 2000 Index (IWM) and Invesco DWA Technical Leaders (PDP).
• Risk-off – iShares Barclays 20+ Year Treas Bond (TLT) and Invesco S&P 500 Low Volatility (SPLV).

On each portfolio switch date, assets receive equal weight with 0.25% overall penalty for trading frictions. We focus on compound annual growth rate (CAGR), maximum drawdown (MaxDD) measured at 6-month intervals and Sharpe ratio measured at 6-month intervals as key performance statistics. As benchmarks, we consider buying and holding SPY, IWM or TLT and a 60%-40% SPY-TLT portfolio rebalanced frictionlessly at the ends of April and October (60-40). Using April and October dividend-adjusted closes of SPY, IWM, PDP, TLT and SPLV as available during October 2002 (first interval with at least one risk-on and one risk-off asset) through April 2023, and contemporaneous 6-month U.S. Treasury bill (T-bill) yield as the risk-free rate, we find that: Keep Reading

Does an allocation to gold truly protect a portfolio from downside risk? In their April 2023 paper entitled “The Golden Rule of Investing”, Pim van Vliet and Harald Lohre examine downside risks for portfolios of stocks (value-weighted U.S. stock market) and bonds (10-year U.S. Treasury notes) with and without gold (bullion) based on real returns and a 1-year investment horizon. They also investigate substitution of low-volatility stocks for the broad stock market in search of further downside risk protection. Using monthly returns for the specified assets and U.S. inflation data during 1975 (when gold becomes truly tradable) through 2022, they find that:

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Is timing of U.S. equity factors broadly and reliably attractive? In their March 2023 paper entitled “Timing the Factor Zoo”, Andreas Neuhierl, Otto Randl, Christoph Reschenhofer and Josef Zechner analyze effectiveness of 39 timing signals applied to 318 known factors. Factors include such categories as intangibles, investment, momentum, profitability, trading frictions and value/growth. Timing signals encompass momentum, volatility, valuation spread, characteristics spread, issuer-purchaser spread and reversal. Specifically, they:

• Forecast monthly returns for each factor and each signal (12,402 timed factors).
• Aggregate timing signals using partial least squares regression.
• Construct multi-factor portfolios that are each month long (short) the fifth, or quintile, of factors with the highest (lowest) predicted returns.
• Investigate composition of optimal factor timing portfolios, considering such properties such as turnover and style tilt.

Using monthly factor and signal data as available (different start dates) during 1926 through 2020, they find that: Keep Reading

A subscriber asked about boosting the performance of the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS), and thereby the Combined Value-Momentum Strategy (SACEVS-SACEMS), by substituting ProShares Ultra S&P500 (SSO) for SPDR S&P 500 ETF Trust (SPY) in these strategies whenever:

1. SPY is above its 200-day simple moving average (SMA200); and,
2. The CBOE Volatility Index (VIX) SMA200 is below 18.

Substitution of SSO for SPY applies to portfolio holdings, but not SACEMS asset ranking calculations. To investigate, we test all versions of SACEVS, SACEMS and monthly rebalanced 50% SACEVS-50% SACEMS (50-50) combinations. We limit SPY SMA200 and VIX SMA200 conditions to month ends as signals for next-month actions (no intra-month changes). We consider baseline SACEVS and SACEMS (holding SPY as indicated) and versions of SACEVS and SACEMS that always hold SSO instead of SPY as benchmarks. We look at average gross monthly return, standard deviation of monthly returns, monthly gross reward/risk (average monthly return divided by standard deviation), gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio as key performance metrics. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using daily unadjusted SPY and VIX values for SMA200 calculations since early September 2005 and monthly total returns for SSO since inception in June 2006 to modify SACEVS and SACEMS inputs, all through February 2023, we find that: Keep Reading

Can investors reliably exploit the somewhat opaquely presented strategy summarized in “Using Wilder Volatility Stops to Time the U.S. Stock Market”, which employs Welles Wilder’s Average True Range (ATR) volatility metric to generate buy and sell signals for broad U.S. stock market indexes? To investigate, we each trading day for the SPDR S&P 500 ETF Trust (SPY):

1. Compute true range as the greatest of: (a) daily high minus low; (b) absolute value of daily high minus previous close; and, (c) absolute value of daily low minus previous close.
2. Calculate ATR as the simple average of the last five true ranges (including the current one).
3. Generate a Wilder Volatility Stop (WVS) by multiplying ATR by a risk factor of 2.5.
4. When out of SPY, buy when it closes above a dynamic trendline defined by a trend minimum plus current WVS (breakout). When in SPY, sell when it closes below a dynamic trendline defined by a trend maximum minus current WVS (breakdown).

We perform the above calculations using raw (not adjusted for dividends) daily SPY prices, but use dividend-adjusted prices to calculate returns. We assume any breakout/breakdown signal and associated SPY-cash switch occurs at the same close. We initially ignore SPY-cash switching frictions, but then test outcome sensitivity to different levels of frictions. We ignore return on cash due to frequency of switching. We further test outcome sensitivity to parameter choices and to an alternative definition of ATR. We use buy-and-hold SPY as a benchmark. Using daily raw and dividend-adjusted prices for SPY during January 1993 (inception) through most of February 2023, we find that: Keep Reading