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Allocations for September 2022 (Final)
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Momentum Investing Strategy (Strategy Overview)

Allocations for September 2022 (Final)
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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.

Resilience of Low-volatility Stocks

The body of research indicates that low-volatility/low-beta stock investing suppresses exposure to overall equity market risk. Does it work equally well for different sources of such risk? In his September 2022 paper entitled “Macro Risk of Low-Volatility Portfolios”, David Blitz examines the separate exposures of low-volatility portfolios to interest rate, implied volatility, liquidity, commodity, sentiment, macroeconomic and climate (CO2 emissions) risk factors. Specifically, he compares the contemporaneous interactions with these risks of the MSCI USA Minimum Volatility Index (based on minimum variance optimization), the S&P 500 Low Volatility Index (the 100 inverse volatility-weighted stocks in the S&P 500 with the lowest volatilities over the past one year) and the S&P 500 Index as the market benchmark. He measures risk factor-index interactions via univariate regressions of monthly excess returns versus monthly risk factor values. He also considers risk factor interactions with ten (decile) equally weighted portfolios of the 1,000 largest U.S. stocks at each point in time sorted by preceding 36-month volatilities. Using monthly total returns for the indexes/portfolios in U.S. dollars in excess of the risk-free rate and monthly risk factor values during January 1991 through December 2021, he finds that:

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Are Equity Multifactor ETFs Working?

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

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the seven equity multifactor ETFs and benchmarks as available through August 2022, we find that: Keep Reading

Asset Class ETF Interactions with VIX

How have different asset classes recently interacted with the CBOE Volatility Index (VIX)? To investigate, we consider relationships between VIX and the exchange-traded fund (ETF) asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) or the Simple Asset Class ETF Value Strategy (SACEVS) at a monthly measurement frequency. We consider both overall relationships and relationships across ranges of VIX. Using end-of-month levels of VIX since January 1990 and dividend-adjusted monthly closing prices for the asset class proxies as available since July 2002, all through July 2022, we find that: Keep Reading

SACEMS with Inverse VIX-based Lookback Intervals

One concern about simple momentum strategies is data snooping bias impounded in selection of the lookback interval(s) used to measure asset momentum. To circumvent this concern, we consider the following argument:

  • The CBOE Volatility Index (VIX) broadly indicates the level of financial markets distress and thereby the tendency of investors to act complacently (when VIX is low) or to act in panic (when VIX is high).
  • Complacency translates to resistance in changing market outlook (long memory and lookback intervals), while panic translates to rapid changes of mind (short memory and short lookback intervals).
  • The inverse of VIX is therefore indicative of the actual aggregate current lookback interval affecting investor actions.

We test this argument by:

  • Setting a range for VIX using monthly historical closes from January 1990 through July 2002, before the sample period used for any tests of the Simple Asset Class ETF Momentum Strategy (SACEMS).
  • Applying buffer factors to the bottom and top of this actual inverse VIX range to recognize that it could break above or below the historical range in the future.
  • Segmenting the buffer-extended inverse VIX range into 12 equal increments and mapping these increments by rounding into momentum lookback intervals of 1 month (lowest segment) to 12 months (highest segment).
  • Applying this same method to future end-of-month inverse VIX levels to select the SACEMS lookback interval for the next month.

We test the top one (Top 1), the equally weighted top two (EW Top 2) and the equally weighted top three (EW Top 3) SACEMS portfolios. We focus on compound annual growth rate (CAGR), maximum drawdown based on monthly measurements, annual returns and Sharpe ratio as key performance statistics. To calculate excess annual returns for the Sharpe ratio, we use average monthly yield on 3-month Treasury bills during a year as the risk-free rate for that year. Benchmarks are these same statistics for tracked SACEMS. Using monthly levels of VIX since inception in January 1990 and monthly dividend-adjusted prices of SACEMS assets since February 2006 (initial availability of a commodities ETF), all through July 2022, we find that: Keep Reading

Characterizing S&P 500 Index Bear Market Rallies

A subscriber asked about frequency, magnitude and duration of bear market rallies. To investigate, we employ the S&P 500 Index and consider three ways to define a bear market:

  1. From the day the index is first down over 20% from a prior peak until the day it closes no more than 20% down (< -20% Drawdown).
  2. From the day the index is first down over 30% from a prior peak until the day it closes nor more than 30% down (< -30% Drawdown).
  3. From the day the index crosses below its 200-day simple moving average until the day it crosses back above this moving average (SMA200).

Based on bear market statistics for these three definitions, we then look at ways to characterize bear market rallies. Using daily S&P 500 Index closes from the end of December 1927 through mid-August 2022, we find that: Keep Reading

Maximum Drawdown as Fund Performance Predictor

Is past rolling maximum drawdown, a simple measure of recent downside risk, a useful indicator of future mutual fund performance? In their June 2022 paper entitled “Maximum Drawdown as Predictor of Mutual Fund Performance and Flows”, Timothy Riley and Qing Yan investigate whether style-adjusted maximum drawdown based on daily returns over the last 12 months usefully predicts mutual fund performance. To adjust for fund style differences, they subtract from each individual unadjusted drawdown the average unadjusted drawdown across all funds in the same style during the measurement interval. Their principal performance metric is alpha based on a 4-factor (market, size, book-to-market, momentum) model of stock returns. Using daily net returns for 2,188 actively managed long-only U.S. equity mutual funds that are at least two years old and have at least $20 million in assets during January 1999 through December 2019, they find that: Keep Reading

Failure of Equity Multifactor Funds?

Multifactor funds offer rules-based, diversified exposures to firm/stock factors found to beat the market in academic studies. Do the funds beat the market in real life? In his June 2022 paper entitled “Multifactor Funds: An Early (Bearish) Assessment”, Javier Estrada assesses performance of such funds across U.S., global and emerging markets relative to that of corresponding broad capitalization-weighted indexes and associated exchange-traded funds (ETF). He focuses on multifactor funds with exposure to at least three factors that are explicitly marketed as multifactor funds. Using monthly total returns for 56 U.S.-based equity multifactor funds with at least three years of data and $10 million in assets from respective inceptions (earliest June 2014) through March 2022, and total returns for matched broad market indexes and ETFs, he finds that:

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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 2022, we find that: Keep Reading

Test of Seasonal Risk Adjustment Strategy

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:

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 2022, and contemporaneous 6-month U.S. Treasury bill (T-bill) yield as the risk-free rate, we find that: Keep Reading

Classic Stocks-Bonds Portfolios with Leveraged ETFs

Can investors use leveraged exchange-traded funds (ETF) to construct attractive versions of simple 60%/40% (60/40) and 40%/60% (40/60) stocks-bonds portfolios? In their March 2020 presentation package entitled “Robust Leveraged ETF Portfolios Extending Classic 40/60 Portfolios and Portfolio Insurance”, flagged by a subscriber, Mikhail Smirnov and Alexander Smirnov consider several variations of classic stocks/bonds portfolio as implemented with leveraged ETFs. They ultimately focus on 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)

To verify findings, we consider this portfolio and several 60/40 and 40/60 stocks/bonds portfolios. We look at net monthly performance statistics, along with compound annual growth rate (CAGR), maximum drawdown (MaxDD) based on monthly data and annual Sharpe ratio. To estimate monthly rebalancing frictions, we use 0.5% of amount traded each month. We use average monthly 3-month U.S. Treasury bill yield during a year as the risk-free rate in Sharpe ratio calculations for that year. Using monthly adjusted prices for TQQQ, TMF, TLT and for SPDR S&P 500 ETF Trust (SPY) and Invesco QQQ Trust (QQQ) to construct benchmarks during February 2010 (limited by TQQQ inception) through April 2022, we find that: Keep Reading

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