Volatility Effects

Are equity multifactor strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider seven ETFs, all currently available (in order of decreasing assets):

• Goldman Sachs ActiveBeta U.S. Large Cap Equity (GSLC) – holds large U.S. stocks based on good value, strong momentum, high quality and low volatility.
• iShares Edge MSCI Multifactor USA (LRGF) – holds large and mid-cap U.S. stocks with focus on quality, value, size and momentum, while maintaining a level of risk similar to that of the market.
• iShares Edge MSCI Multifactor International (INTF) – holds global developed market ex U.S. large and mid-cap stocks based on quality, value, size and momentum, while maintaining a level of risk similar to that of the market.
• JPMorgan Diversified Return U.S. Equity (JPUS) – holds U.S. stocks based on value, quality and momentum via a risk-weighting process that lowers exposure to historically volatile sectors and stocks.
• John Hancock Multifactor Large Cap (JHML) – holds large U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns.
• John Hancock Multifactor Mid Cap (JHMM) – holds mid-cap U.S. stocks based on smaller capitalization, lower relative price and higher profitability, which academic research links to higher expected returns.
• Xtrackers Russell 1000 Comprehensive Factor (DEUS) – seeks to track, before fees and expenses, the Russell 1000 Comprehensive Factor Index, which seeks exposure to quality, value, momentum, low volatility and size factors.

Because available sample periods are very short, we focus on daily return statistics, along with cumulative returns. We use four benchmarks according to fund descriptions: SPDR S&P 500 (SPY), iShares MSCI ACWI ex US (ACWX), SPDR S&P MidCap 400 (MDY) and iShares Russell 1000 (IWB). Using daily returns for the seven equity multifactor ETFs and benchmarks as available through September 2018, we find that: Keep Reading

Quantitative investing involves disciplined rule-based approaches to help investors structure optimal portfolios that balance return and risk. How has such investing evolved? In their June 2018 paper entitled “The Current State of Quantitative Equity Investing”, Ying Becker and Marc Reinganum summarize key developments in the history of quantitative equity investing. Based on the body of research, they conclude that: Keep Reading

Does focusing on downside risk (volatility or beta) consistently produce more accurate forecasts of asset returns? In their July 2018 paper entitled “Tail Risk in the Cross Section of Alternative Risk Premium Strategies”, Bernd Scherer and Nick Baltas investigate how well downside risk explains cross-sectional returns of 260 risk factor strategies spanning asset classes and investment styles from six global investment banks. Their main model is a two-pass regression that distinguishes between conventional market beta and market downside beta. For corroboration, they consider four other indicators of downside risk (return skewness, correlation of tail returns with equity market returns, TED spread and economic policy uncertainty as measured by relative VIX level). Using weekly data risk factor returns and downside risk indicators during February 2008 through January 2018, they find that: Keep Reading

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:

• PowerShares 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).
• PowerShares 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).
• PowerShares 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).
• PowerShares 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 June 2018, we find that: Keep Reading

A betting against beta (BAB) portfolio is long low-beta assets and short high-beta assets, with each side leveraged to a beta of one. Do strong past stock market returns (when investors tend to overweight high-beta stocks) predict an increase in BAB returns? In his June 2018 paper entitled “Time-Varying Leverage Demand and Predictability of Betting-Against-Beta”, Esben Hedegaard tests the prediction that BAB performs better in times and in countries after high past stock market returns in three ways: (1) regression of BAB returns versus past market returns; (2) sorts of BAB returns into fifths (quintiles) based on past market returns; and, (3) a timing strategy that is long BAB half the time and short BAB half the time based on detrended inception-to-date past market returns, scaled to 10% annualized volatility for comparability. Using daily and monthly data, including monthly BAB returns, for U.S. common stocks and the U.S. stock market since 1931 and for 23 other countries from as early as 1988, all through January 2018, he finds that: Keep Reading

Does volatility targeting improve Sharpe ratios and provide crash protection across asset classes? In their May 2018 paper entitled “Working Your Tail Off: The Impact of Volatility Targeting”, Campbell Harvey, Edward Hoyle, Russell Korgaonkar, Sandy Rattray, Matthew Sargaison, and Otto Van Hemert examine return and risk effects of long-only volatility targeting, which scales asset and/or portfolio exposure higher (lower) when its recent volatility is low (high). They consider over 60 assets spanning stocks, bonds, credit, commodities and currencies and two multi-asset portfolios (60-40 stocks-bonds and 25-25-25-25 stocks-bonds-credit-commodities). They focus on excess returns (relative to U.S. Treasury bill yield). They forecast volatility using realized daily volatility with exponentially decaying weights of varying half-lives to assess sensitivity to the recency of inputs. For most analyses, they employ daily return data to forecast volatility. For S&P 500 Index and 10-year U.S. Treasury note (T-note) futures, they also test high-frequency (5-minute) returns transformed to daily returns. They scale asset exposure inversely to forecasted volatility known 24 hours in advance, applying a retroactively determined constant that generates 10% annualized actual volatility to facilitate comparison across assets and sample periods. Using daily returns for U.S. stocks and industries since 1927, for U.S. bonds (estimated from yields) since 1962, for a credit index and an array of futures/forwards since 1988, and high-frequency returns for S&P 500 Index and 10-year U.S. Treasury note futures since 1988, all through 2017, they find that:

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Does implied stock market volatility (IV) predict stock market returns? In their March 2018 paper entitled “Implied Volatility Measures As Indicators of Future Market Returns”, Roberto Bandelli and Wenye Wang analyze the relationship between S&P 500 Index IV and future S&P 500 Index returns. They consider volatilities implied either by S&P 500 Index options (VIX) or by 30-day at-the-money S&P 500 Index straddles. Specifically, they each day:

1. Rank current S&P 500 Index IV according to ranked tenth (decile) of its daily distribution over the past two years. If current IV is higher than any value of IV over the past two years, its rank is 11.
2. Calculate S&P 500 Index returns over the next one, five and 20 trading days.
3. Relate these returns to IV rank.

They calculate statistical significance based on the difference between the average IV-ranked log returns and log returns over all intervals of the same length. Using daily data for the selected variables during December 1991 through November 2017, they find that: Keep Reading

Is there an easy way for investors to capture jointly the most reliable stock return factor premiums? In their March 2018 paper entitled “The Conservative Formula: Quantitative Investing Made Easy”, Pim van Vliet and David Blitz propose a stock selection strategy based on low return volatility, high net payout yield and strong price momentum. Specifically, at the end of each quarter they:

1. Segment the then-current 1,000 largest stocks into 500 with the lowest and 500 with the highest 36-month return volatilities.
2. Within each segment, rank stocks based on total net payout yield (NPY), calculated as dividend yield minus change in shares outstanding divided by its 24-month moving average.
3. Within each segment, rank stocks based on return from 12 months ago to one month ago (with the skip-month intended to avoid return reversals).
4. Within the low-volatility segment, average the momentum and NPY ranks for each stock and equally weight the top 100 to reform the Conservative Formula portfolio.
5. Within the high-volatility segment, average the momentum and NPY ranks for each stock and equally weight the bottom 100 to reform the Speculative Formula portfolio.

Limiting the stock universe to the top 1,000 based on market capitalization suppresses liquidity risk. Limiting screening parameters to three intensely studied factors that require no accounting data mitigates data snooping and data availability risks. They focus on the 1,000 largest U.S. stocks to test a long sample, but also consider the next 1,000 U.S. stocks (mid-caps) and the 1,000 largest stocks from each of Europe, Japan and emerging markets. They further examine: (1) sensitivity to economic conditions doe the long U.S. sample; and, (2) impact of trading frictions in the range 0.1%-0.3% for developed markets and 0.2%-0.6% for emerging markets. Using quarterly prices, dividends and shares outstanding for the contemporaneously largest 1,000 U.S. stocks since 1926, European and Japanese stocks since 1986 and emerging markets stocks since 1991, all through 2016, they find that:

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Is shorting pairs of leveraged exchange-traded funds (ETF) reliably profitable? In their December 2017 paper entitled “Shorting Leveraged ETF Pairs”, Christopher Hessel, Jouahn Nam, Jun Wang, Xing Cunyu and Ge Zhang examine monthly returns from shorting a pair of leveraged and inverse leveraged ETFs for the same index. They first investigate what circumstances make this strategy profitable. They then test the strategy on each of the triple/inverse triple (3X/-3X) pairs associated with the following six base ETFs: Financial Select Sector SPDR (XLF: FAS/FAZ), Powershares QQQ (QQQ: TQQQ/SQQQ), iShares Russell 2000 Index (IWM: TNA/TZA), SPDR S&P 500 (SPY: UPRO/SPXU), VanEck Vectors Junior Gold Miners ETF (GDXJ: JNUG/JDST) and Energy Select Sector SPDR (XLE: ERX/ERY). Their analysis assumes rebalancing pair short positions to equal value at the end of each month and holding them to the end of the next month. Using monthly data for the selected leveraged ETFs from the end of 2007 (except the end of November 2009 for the leveraged versions of GDXJ) through the end of December 2016, they find that: Keep Reading

How might an investor construct a portfolio of very risky assets? To investigate, we consider:

• Diversifying (based on pairwise correlations) by combining: (1) Bitcoin Investment Trust (GBTC), representing a very long-term option on Bitcoins; (2) VanEck Vectors Junior Gold Miners ETF (GDXJ), representing a very long-term option on gold; and, (3) ProShares Short VIX Short-Term Futures (SVXY), to capture the U.S. stock market volatility risk premium by shorting short-term S&P 500 Index implied volatility (VIX) futures.
• Capturing upside volatility and managing portfolio drawdown via monthly rebalancing and gain-skimming to a cash position.

We assume equal initial allocations of $10,000 to each of the three risky assets and $0 to cash. If the risky assets have a month-end combined value less than the combined initial allocations, we rebalance them to equal weights for next month. If the risky assets have a combined month-end value greater than the combined initial allocations, we rebalance to the initial allocations and move the excess permanently (skim) to cash. We assume monthly portfolio reformation frictions of 2% of month-end combined values of risky assets. We assume accrued, skimmed cash earns the 3-month U.S. Treasury bill (T-bill) yield. Using monthly adjusted values of GBTC, GDXJ and SVXY and contemporaneous T-bill yield during May 2015 (limited by GBTC) through mid-February 2018, we find that:

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