Does the U.S. stock market have a predictable pattern of returns around the ends of calendar quarters? Do funds deploy cash to bid stocks up at quarter ends to boost portfolio values in quarterly reports (with subsequent reversals)? Or, do they sell stocks to raise cash for fund redemptions? Is any end-of-quarter effect distinct from the Turn-of-the-Month (TOTM) effect? To investigate, we calculate average daily stock market (S&P 500 Index) returns before and after ends of calendar quarters and compare those returns to TOTM returns. Using daily closes of the S&P 500 Index during January 1950 through September 2018 (275 quarters), we find that: Keep Reading

Below is a weekly summary of our research findings for 10/15/18 through 10/19/18. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

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“Stock Size and Excess Stock Portfolio Growth” finds that an equal-weighted portfolio of the (each day) 1,000 largest U.S. stocks beats its market capitalization-weighted counterpart by about 2% per year. However, the underlying research does not account for portfolio reformation/rebalancing costs and may not be representative of other stock universes. Do exchange-traded funds (ETF) that implement equal weight for various U.S. stock indexes confirm findings? To investigate, we consider four equal weight ETFs:

• Invesco S&P 500 Equal Weight (RSP) – seeks to track investment results (before fees and expenses) of the S&P 500 Equal Weight Index. The capitalization-weighted counterpart is SPDR S&P 500 (SPY).
• First Trust NASDAQ-100 Equal Weight (QQEW) – seeks to track investment results (before fees and expenses) of the NASDAQ-100 Equal Weight Index. The capitalization-weighted counterpart is Invesco QQQ Trust (QQQ).
• Invesco Russell MidCap Equal Weight (EQWM) – seeks to track investment results (before fees and expenses) of the Russell Midcap Equal Weight Index. The capitalization-weighted counterpart is iShares Russell Mid-Cap (IWR).
• Invesco Russell 2000 Equal Weight (EQWS) – seeks to track investment results (before fees and expenses) of the Russell 2000 Equal Weight Index. The capitalization-weighted counterpart is iShares Russell 2000 (IWM).

We calculate monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly dividend-adjusted prices for the eight ETFs as available (limited by  equal weight funds) through September 2018, we find that: Keep Reading

Why do simple stock portfolios such as equal weighting and random weighting beat market capitalization weighting over the long run? In their June 2018 paper entitled “Diversification, Volatility, and Surprising Alpha”, Adrian Banner, Robert Fernholz, Vassilios Papathanakos, Johannes Ruf and David Schofield tackle this question by decomposing expected stock portfolio log-return into average growth rate and excess growth rate (EGR). They focus on average log-return because, unlike arithmetic and geometric averages, it is an unbiased estimator of long-term performance. They apply two formulas derived in prior work to estimate portfolio log-returns:

1. Expected portfolio log-return = weighted average stock log-return + EGR
2. EGR = (weighted average stock return variance – portfolio return variance)/2

They apply these formulas to the following five portfolios, each consisting of monthly overlapping sub-portfolios formed from the 1,000 U.S. stocks with the (each day) largest market capitalizations and rebalanced annually with stock weights normalized to a sum of one:

1. Capitalization-weighted (CW) – stock weights are proportional to their respective market capitalizations.
2. Equal-weighted (EW) – weight of each stock is 1/1000.
3. Large-overweighted (LO) – stock weights are proportional to the square of their respective market capitalizations.
4. Random-weighted (RW) – stock weights are proportional to random values between zero and one (median of 1,000 trials).
5. Inverse random-weighted (IRW) – stock weights are proportional to the reciprocals of random values between zero and one (median of 1,000 trials).

EGR quantifies the extent to which portfolio volatility is less than constituent stock volatilities and is always positive for long-only portfolios. Higher constituent stock volatilities generate higher portfolio EGRs. Using daily prices for the 1,000 U.S. stocks with the largest market capitalizations each day during 1964 through 2012 (5,384 distinct stocks over 49 years), they find that:

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

• IQ Hedge Multi-Strategy Tracker (QAI) – seeks to track, before fees and expenses, risk-adjusted returns of a collection of long/short equity, global macro, market neutral, event-driven, fixed income arbitrage and emerging markets hedge funds.
• JPMorgan Diversified Alternatives (JPHF) – aims to provide direct, diversified exposure to hedge fund strategies via a bottom-up approach across equity long/short, event-driven and global macro strategies.
• ProShares Hedge Replication (HDG) – seeks to track, before fees and expenses, an equally weighted composite of over 2000 hedge funds.
• AlphaClone Alternative Alpha (ALFA) – seeks to track price and yield, before fees and expenses, of U.S.-traded equity securities to which hedge funds and institutional investors have disclosed significant exposures.
• IQ Hedge Market Neutral Tracker (QMN) – seeks to track, before fees and expenses, risk-adjusted returns of market neutral hedge funds.
• ProShares Morningstar Alternatives Solution (ALTS) – seeks to track, before fees and expenses, performance of a diversified set of alternative ETFs.

We consider both daily and monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We use two benchmarks, SPDR S&P 500 (SPY) and the Eurekahedge Hedge Fund Index (HFI). Using daily and monthly returns for the six hedge fund ETFs and SPY as available through September 2018 and monthly returns for HFI through August 2018, we find that: Keep Reading

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

Below is a weekly summary of our research findings for 10/8/18 through 10/12/18. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Under what conditions should speculators buy protective equity options when they expect realized stock market volatility to increase? In their September 2018 paper entitled “Being Right is Not Enough: Buying Options to Bet on Higher Realized Volatility”, Roni Israelov and Harsha Tummala analyze the relationship between: (1) long volatility return (delta-hedged options) and same-interval changes in realized volatility; and, (2) the volatility risk premium (VRP, implied volatility minus realized volatility) and same-interval changes in realized volatility. They specify long volatility as a portfolio of cash-settled equity index options, reformed monthly, that:

• On each options expiration date, buys one-third of a -25 delta put option, one-third of a +25 delta call option and one-sixth each of at-the-money put and call options. All options initially have about a month to expiration.
• Each day until expiration, hedges option deltas via equity index futures. 
• Holds the options to expiration.

They also examine sensitivity of outcome to different portfolio initiation and termination points relative to significant volatility increases. They focus on the S&P 500 Index, using VIX as implied volatility and hedging via S&P 500 Index futures, during January 1996 through December 2016. They also consider for robustness testing corresponding data for Eurostoxx 50, FTSE 100 and Nikkei 225. Using daily data as specified, they find that:

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The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for September 2018. The actual total (core) inflation rate for September is lower than (slightly lower than) forecasted.