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Value Investing Strategy (Strategy Overview)

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Momentum Investing Strategy (Strategy Overview)

Allocations for May 2024 (Final)
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Value Premium

Is there a reliable benefit from conventional value investing (based on the book-to-market value ratio)? these blog entries relate to the value premium.

Measuring the Value Premium with Value and Growth ETFs

Do popular style-based exchange-traded funds (ETF) offer a reliable way to exploit the value premium? To investigate, we compare differences in returns (value-minus-growth, or V – G) for each of the following three matched pairs of value-growth ETFs:

  • iShares Russell 2000 (Smallcap) Growth Index (IWO)
  • iShares Russell 2000 (Smallcap) Value Index (IWN)
  • iShares Russell Midcap Growth Index (IWP)
  • iShares Russell Midcap Value Index (IWS)
  • iShares Russell 1000 (Largecap) Growth Index (IWF)
  • iShares Russell 1000 (Largecap) Value Index (IWD)

To aggregate, we define monthly value return as the equally weighted average monthly return of IWN, IWS and IWD and monthly growth return as the equally weighted average monthly return of IWO, IWP and IWF. Using monthly dividend-adjusted closing prices for these ETFs during August 2001 (limited by IWP and IWS) through March 2024, we find that: Keep Reading

Causality in the 5-factor Model of Stock Returns

Does the Fama-French 5-factor model of stock returns stand up to causality analyses? Do the factors cause the returns? In their December 2023 paper entitled “Re-Examination of Fama-French Factor Investing with Causal Inference Method”, Lingyi Gu, Ellen Zhang, Andrew Heinz, Jingxuan Liu, Tianyue Yao, Mohamed AlRemeithi and Zelei Luo construct causal graphs to analyze the relationship between future (next-month) stock return and each of the five factors in the model, which are:

  1. Market – value-weighted market return minus the risk-free rate.
  2. Size – return on small stocks minus the return on big stocks.
  3. Value –  return on high book-to-market ratio stocks minus the return on low book-to-market ratio stocks.
  4. Profitability – return on robust profitability stocks minus the return on weak profitability stocks.
  5. Investment – return on conservative investment stocks minus the return on aggressive investment stocks.

They consider a constraint-based algorithm, a score-based algorithm and a functional model to estimate causality. For each approach, they evaluate the stability and strength of the causal relationships across different conditions by explore robustness to data loss or alterations. Their goal is to replicate initial conditions and datasets used in the 2015 paper that introduced the 5-factor model. Using monthly returns for a broad sample of U.S. common stocks and the five specified factors during July 1963 through December 2013, they find that:

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Exhaustively Timing Equity Factor Premiums

Can investors reliably time the market, size, value and profitability long-short equity factor premiums? In their October 2023 paper entitled “Another Look at Timing the Equity Premiums”, Wei Dai and Audrey Dong test strategies that time these four premiums in U.S., developed ex-U.S. and emerging equity markets. They define the premiums as:

  1. Market – the capitalization-weighted market return minus the U.S. Treasury bill yield.
  2. Size – average return on small-capitalization stocks minus average return on large-capitalization stocks.
  3. Value – average return on value stocks minus average return on growth stocks.
  4. Profitability – average return on stocks of high-profitability firms minus average return on stocks of low-profitability firms.

They time each premium separately based on each of:

  1. Valuation ratio – When the difference in aggregate price-to-book ratio between the long and short sides of a premium becomes high (low) relative to its historical distribution, switch to the short (long) side.
  2. Mean reversion – When the premium itself becomes high (low) relative to its historical distribution, switch to the short (long) side  of the premium.
  3. Momentum – When the premium over the last year becomes relatively high (low), switch to the long (short) side of the premium.

To measure historical premium distributions, they consider an expanding window of initial length 10 years or a rolling 10-year window. For switching to the short side of premiums, they consider historical distribution thresholds of top 10%, 20% or 50% (bottom 10%, 20% or 50%) for valuation ratio and mean reversion (momentum). For switching to the long side of premiums, they consider thresholds of bottom 10%, 20% or 50% (top 50%) for valuation ratio and mean reversion (momentum). They consider  monthly or annual portfolio rebalancing. The number of timing strategies tested is thus 720. For the U.S. sample, monthly returns start in July 1963 for profitability and July 1927 for the other three premiums. For the developed ex-U.S. (emerging markets) sample, premium returns start in July 1990 (July 1994). Benchmarks are returns to strategies that continuously hold just the long side of each premium portfolio. Using monthly data as specified through December 2022, they find that: Keep Reading

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 2023, we find that: Keep Reading

Long-only Factor Investing with Little or No Trading

What is the right balance between seeking alpha and avoiding taxes? In their August 2023 paper entitled “Alpha Now, Taxes Later: Tax-Efficient Long-Only Factor Investing”, Yin Chen and Roni Israelov assess trade-offs between rebalancing benefits and tax avoidance from overlapping 10-year backtests of long-only momentum, value, quality and safety factor stock portfolios. They measure momentum as cumulative return from 12 months ago to one month ago, value as book-to-market ratio, quality as operating profitability and safety as winsorized market betas. All portfolios start with the equal-weighted top fifth (300 stocks) as ranked by the factor metric. After initial formation, they consider five monthly portfolio management rules:

  1. Fully Rebalanced, each month selling stocks that drop out of the top fifth and buying stocks that enter the top fifth, but not adjusting weights of stocks that remain in the portfolio.
  2. Buy-and-Hold (no rebalancing over the 10-year portfolio life).
  3. Sell Losers at Losses, each month selling stocks that have migrated to the bottom fifth if they have capital losses.
  4. Tax Loss Harvesting, each month selling stocks with more than 5% unrealized losses and not buying them back until at least 30 days later.
  5. Tax Loss Harvesting and Sell Losers, selling stocks that have migrated to the bottom fifth even if they have unrealized capital gains so long as the aggregate realized capital gain is zero.

They form the first portfolio for each factor in June 1964 and initiate new portfolios every six months until January 2012, such that the last portfolio is held through December 2021. They focus on 1-factor (market) alpha, averaged across overlapping portfolios, as the key performance metric. To calculate net performance, they assume 0.08% 1-way trading frictions, 23.8% dividend tax rate and 23.8% (40.8%) long-term (short-term) capital gain tax rate. Based on initial findings, they repeat all tests on composite portfolios of value, quality and safety factors constructed by ranking stocks on individual factors and investing equally in the fifth of stocks with the highest combined rankings. Using data as specified for the 1,500 U.S. stocks with the largest market capitalizations at the end of each prior year during 1964 to 2021, they find that:

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Comparing Long-term Returns of U.S. Equity Factors

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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Boosting Retirement Outcome via Capture of Factor Premiums

Can investors improve long-term retirement portfolio outcomes by targeting equity factor premiums in their stock allocations? In his April 2023 paper entitled “How Targeting the Size, Value, and Profitability Premiums Can Improve Retirement Outcomes”, Mathieu Pellerin investigates whether stock portfolios that target size, value and profitability factor premiums better sustain retirement spending and generate larger bequests than those holding the broad stock market. His hypothetical investor:

  • Starts saving at 25, retires at 65 and dies at 95.
  • Initially allocates 100% to stocks, at age 45 reduces this allocation linearly to 50% at age 65 by shifting to bonds, and thereafter maintains 50%/50% stocks/bonds.
  • Makes $1,042 monthly contributions ($12,500 per year, or $500,000 from age 25 to 65).
  • After retirement, withdraws (consumes) a constant annual 4% in real terms of the balance at retirement.
  • For the stock allocation, chooses either a broad value-weighted market index (CRSP 1-10) or the Dimensional US Adjusted Market 1 index that emphasizes size, value and profitability factors with low turnover.
  • Earns real annual broad stock market returns of either 8.1% (actual historical average) or 5.0% (a conservative 5th percentile of historical return distribution).
  • For the bond allocation, holds 5-year U.S. Treasury notes.

He simulates 100,000 lifecycles by, for each lifecycle: (1) extracting 70-year (840-month) real asset class return subsamples from the full histories; and, (2) applying block bootstrapping with 10-year mean block size to generate lifecycle portfolio returns. Using monthly historical returns for the specified stock/bond proxies and monthly U.S. inflation data during June 1927 through December 2022, he finds that:

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Growth Versus Value and Interest Rates

In his 2007 book The Little Book That Makes You Rich: A Proven Market-Beating Formula for Growth Investing, expert Louis Navellier hypothesizes that growth (value) stocks tend to do relatively better when interest rates are rising (falling). Growth stocks benefit from the economic expansions associated with rising rates. Value stocks benefit from refinancing opportunities as interest rates fall. To test this hypothesis, we compare the performances of the following paired growth and value exchange-traded funds (ETF) and mutual funds as interest rates, proxied by the yield on the 10-year U.S. Treasury note (T-note), vary:

We consider both abstract predictive power based on correlation of changes in T-note yield with future fund returns and explicit performance of a strategy that switches between value and growth according to changes in T-note yield. Using end-of-month dividend-adjusted prices for the selected funds and contemporaneous T-note yield starting January 1983 for the mutual funds (limited by FDGRX) and May 2000 for the ETFs, all through February 2023, we find that: Keep Reading

Can Investors Capture Academic Equity Factor Premiums via Mutual Funds?

Do factor investing (smart beta) mutual funds capture for investors the premiums found in academic factor research? In their November 2022 paper entitled “Factor Investing Funds: Replicability of Academic Factors and After-Cost Performance”, Martijn Cremers, Yuekun Liu and Timothy Riley analyze the performance of funds seeking to capture of published (long-side) factor premiums. They group factor investing funds into four styles: dividend, volatility, momentum and q-factor (profitability and investment). They separately measure how closely fund holdings adhere to the long sides of academic factor specifications. They measure fund outperformance (alpha) relative to the market factor via the Capital Asset Pricing Model (CAPM) and via a multi-factor model (CPZ6) that accounts for the market factor and for granular size/value interactions. Using monthly returns for 233 hand-selected factor investing mutual funds and for the academic research factors during January 2006 (16 funds available) through September 2020 (207 funds available), they find that:

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Equity Factor Performance Before and After the End of 2000

Do the widely used U.S. stock return factors exhibit long-term trend changes and shorter-term cyclic behaviors? In his November 2022 paper entitled “Trends and Cycles of Style Factors in the 20th and 21st Centuries”, Andrew Ang applies various methods to compare trends and cycles for equity value, size, quality, momentum and low volatility factors, with focus on a breakpoint at the end of 2000. He measures size using market capitalization, value using book-to-market ratio, quality using operating profitability, momentum using return from 12 months ago to one month ago and low volatility using idiosyncratic volatility relative to the Fama-French 3-factor (market, size, book-to-market) model of stock returns. He each month for each factor sorts stocks into tenths, or deciles, and computes gross monthly factor return from a portfolio that is long (short) the average return of the two deciles with the highest (lowest) expected returns. As a benchmark, he uses the value-weighted market return in excess of the U.S. Treasury bill yield. Using market and factor return data from the Kenneth French data library during July 1963 through August 2022, he finds that:

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