Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for October 2020 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for October 2020 (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.

Combining the Smart Money Indicator with SACEMS and SACEVS

“Verification Tests of the Smart Money Indicator” reports performance results for a specific version of the Smart Money Indicator (SMI) stocks-bonds timing strategy, which exploits differences in futures and options positions in the S&P 500 Index, U.S. Treasury bonds and 10-year U.S. Treasury notes between institutional investors (smart money) and retail investors (dumb money). Do these sentiment-based results diversify those for the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS)? To investigate, we look at correlations of annual returns between variations of SMI (no lag between signal and execution, 1-week lag and 2-week lag) and each of SACEMS equal-weighted (EW) Top 3 and SACEVS Best Value. We then look at average gross annual returns, standard deviations of annual returns and gross annual Sharpe ratios for the individual strategies and for equal-weighted, monthly rebalanced portfolios of the three strategies. Using gross annual returns for the strategies during 2008 through 2019, we find that: Keep Reading

The Post-publication Value Premium

Does the value premium for U.S. stocks, as measured by book-to-market ratio, persist after its initial discovery/publication in 1992? In their January 2020 paper entitled “The Value Premium”, Eugene Fama and Kenneth French assess whether the value premium in the U.S. declines or disappears in a post-publication sample that is as long as the discovery sample. Unlike many researchers, they focus on difference in returns between high book-to-market (value) stocks and the value-weighted market, not the return spread between value and low book-to-market (growth) stocks. To control for firm market capitalization effects, they consider separately stocks with capitalizations above (big) and below (small) the NYSE median. They specify value (growth) stocks as those at or above the 70th (below the 30th) percentile of book-to-market values of NYSE stocks. They re-sort stocks at the end of each June, with book-to-market ratio measured at the end of the fiscal year during the prior calendar year. The overall value premium is the capitalization-weighted combination of big value and small value. Using annual book-to-market ratios and market capitalizations, and monthly returns, for all NYSE, AMEX and NASDAQ stocks during July 1963 through June 2019, they find that:

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A Better Stock Value Ratio?

Is there a better stock value ratio than commonly used ones such as book-to-market, dividend-to-price, earnings-to-price and cash flow-to-price ratios? In the January 2020 revision of his paper entitled “A New Value Strategy”, Baolian Wang investigates the effectiveness of cash-based operating profitability-to-price (COP/P) as a value ratio. He computes COP as operating profitability minus accruals, with operating profitability defined as revenue minus cost of goods sold and reported selling, general and administrative expenses (not including expenditures on research and development). He each year at the end of June sorts stocks into tenths, or deciles, based on COP/P and then calculates next-month excess returns for a value-weighted or equal-weighted hedge portfolio that is long (short) the decile with the highest (lowest) values of COP/P. Using monthly returns and annual, 6-month lagged and groomed accounting data for non-financial U.S. common stocks during 1963 through 2018 period, he finds that: Keep Reading

Doing Momentum with Style (ETFs)

“Beat the Market with Hot-Anomaly Switching?” concludes that “a trader who periodically switches to the hottest known anomaly based on a rolling window of past performance may be able to beat the market. Anomalies appear to have their own kind of momentum.” Does momentum therefore work for style-based exchange-traded funds (ETF)? To investigate, we apply a simple momentum strategy to the following six ETFs that cut across market capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

We test a simple Top 1 strategy that allocates all funds each month to the one style ETF with the highest total return over a set momentum ranking (lookback) interval. We focus on the baseline ranking interval from the “Simple Asset Class ETF Momentum Strategy (SACEMS)”, but test sensitivity of findings to ranking intervals ranging from one to 12 months. As benchmarks, we consider an equally weighted and monthly rebalanced combination of all six style ETFs (EW All), and buying and holding S&P Depository Receipts (SPY). As an enhancement we consider holding the Top 1 style ETF (3-month U.S. Treasury bills, T-bills) when the S&P 500 Index is above (below) its 10-month simple moving average at the end of the prior month (Top 1:SMA10), with a benchmark substituting SPY for Top 1 (SPY:SMA10). We consider the performance metrics used for SACEMS. Using monthly dividend-adjusted closing prices for the six style ETFs and SPY, monthly levels of the S&P 500 index and monthly yields for T-bills during August 2001 (limited by IWS and IWP) through December 2019, we find that:

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Style Performance by Calendar Month

Trading Calendar presents full-year and monthly cumulative performance profiles for the overall stock market (S&P 500 Index) based on its average daily behavior since 1950. How much do the corresponding monthly behaviors of the various size and value/growth styles deviate from an overall equity market profile? To investigate, we consider the the following six exchange-traded funds (ETF) that cut across capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

Using monthly dividend-adjusted closing prices for the style ETFs and S&P Depository Receipts (SPY) over the period August 2001 through December 2019 (limited by data for IWS/IWP), we find that: Keep Reading

Factor Portfolio Longs vs. Shorts

Do both the long and short sides of portfolios used to quantify widely accepted equity factors benefit investors? In their November 2019 paper entitled “When Equity Factors Drop Their Shorts”, David Blitz, Guido Baltussen and Pim van Vliet decompose and analyze gross performances of long and short sides of U.S. value, momentum, profitability, investment and low-volatility equity factor portfolios. The employ 2×3 portfolios, segmenting first by market capitalization into halves and then by selected factor variables into thirds. The extreme third with the higher (lower) expected return constitutes the long (short) side of a factor portfolio. When looking at just the long (short) side of factor portfolios, they hedge market beta via a short (long) position in liquid derivatives on a broad market index. Using monthly returns for the specified 2×3 portfolios during July 1963 through December 2018, they find that:

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Value Investing Dead?

Why has value investing (long undervalued stocks and short overvalued stocks) performed poorly since 2007? Is it dead, or will it recover? In their August 2019 paper entitled “Explaining the Demise of Value Investing”, Baruch Lev and Anup Srivastava examine the performance of the Fama-French value (HML) factor portfolio, long stocks with high book value-to-market capitalization ratios and short those with low ratios, because it is the most widely used value strategy. They then investigate reasons for its faltering performance. Using value factor returns and accounting data for a broad sample of U.S. stocks during January 1970 through December 2018, they conclude that: Keep Reading

Equity Factor Time Series Momentum

In their July 2019 paper entitled “Momentum-Managed Equity Factors”, Volker Flögel, Christian Schlag and Claudia Zunft test exploitation of positive first-order autocorrelation (time series, absolute or intrinsic momentum) in monthly excess returns of seven equity factor portfolios:

  1. Market (MKT).
  2. Size – small minus big market capitalizations (SMB).
  3. Value – high minus low book-to-market ratios (HML).
  4. Momentum – winners minus losers (WML)
  5. Investment – conservative minus aggressive (CMA).
  6. Operating profitability – robust minus weak (RMW).
  7. Volatility – stable minus volatile (SMV).

For factors 2-7, monthly returns derive from portfolios that are long (short) the value-weighted fifth of stocks with the highest (lowest) expected returns. In general, factor momentum timing means each month scaling investment in a factor from 0 to 1 according its how high its last-month excess return is relative to an inception-to-date window of past levels. They consider also two variations that smooth the simple timing signal to suppress the incremental trading that it drives. In assessing costs of this incremental trading, they assume (based on other papers) that realistic one-way trading frictions are in the range 0.1% to 0.5%. Using monthly data for a broad sample of U.S. common stocks during July 1963 through November 2014, they find that: Keep Reading

Factor Premium Reliability and Timing

How reliable and variable are the most widely accepted long-short factor premiums across asset classes? Can investors time factor premium? In their June 2019 paper entitled “Factor Premia and Factor Timing: A Century of Evidence”, Antti Ilmanen, Ronen Israel, Tobias Moskowitz, Ashwin Thapar and Franklin Wang examine multi-class robustness of and variation in four prominent factor premiums:

  1. Value – book-to-market ratio for individual stocks; value-weighted aggregate cyclically-adjusted price-to-earnings ratio (P/E10) for stock indexes; 10-year real yield for bonds; deviation from purchasing power parity for currencies; and, negative 5-year change in spot price for commodities.
  2. Momentum – past excess (relative to cash) return from 13 months ago to one month ago.
  3. Carry – front-month futures-to-spot ratio for equity indexes since 1990 and excess dividend yield before 1990; difference in short-term interest rates for currencies; 10-year minus 3-month yields for bonds; and, percentage difference in prices between the nearest and next-nearest contracts for commodities.
  4. Defensive – for equity indexes and bonds, betas from 36-month rolling regressions of asset returns versus equal-weighted returns of all countries; and, no defensive strategies for currencies and commodities because market returns are difficult to define.

They each month rank each asset (with a 1-month lag for conservative execution) on each factor and form a portfolio that is long (short) assets with the highest (lowest) expected returns, weighted according to zero-sum rank. When combining factor portfolios across factors or asset classes, they weight them by inverse portfolio standard deviation of returns over the past 36 months. To assess both overfitting and market adaptation, they split each factor sample into pre-discovery subperiod, original discovery subperiod and post-publication subperiod. They consider factor premium interactions with economic variables (business cycles, growth and interest rates), political risk, volatility, downside risk, tail risk, crashes, market liquidity and investment sentiment. Finally, they test factor timing strategies based on 12 timing signals based on 19 methodologies across six asset classes and four factors. Using data as available from as far back as February 1877 for 43 country equity indexes, 26 government bonds, 44 exchange rates and 40 commodities, all through 2017, they find that: Keep Reading

Effects of Factor Crowding

Does crowding of factor investing strategies reliably predict returns for those strategies? In his March 2019 paper entitled “The Impact of Crowding in Alternative Risk Premia Investing”, Nick Baltas explores mechanics of alternative risk (factor) premium crowding and implications of crowding for future performance. He classifies factor premiums as: divergent (such as momentum), inherently destabilizing due to positive feedback loops and lack of fundamental anchors; or, convergent (such as value), having self-correcting negative feedback loops and fundamental anchors. To test crowding effects, he considers the following premiums: equity value (book-to-market), size (market capitalization), momentum (from regression of return from 12 months ago to one month ago versus volatility), quality (return on assets) and low beta (versus the MSCI World Index); commodities momentum (12-month return); and, currencies value (purchasing power parity) and momentum (12-month return). Each premium consists of returns from a hedge portfolio that is each week long (short) the equal-weighted assets with the highest (lowest) expected returns. For equities, he uses top and bottom tenths. For commodities and currencies, he uses top and bottom thirds. His crowding metric (CoMetric) is average pairwise correlation of factor-adjusted returns of assets within the long or short sides of premium portfolios over the last 52 weeks (except 260 weeks for value). He defines the 20% of weeks with the highest (lowest) CoMetrics as most (least) crowded. Using the specified factor and return data for liquid developed market stocks since September 2004, 24 constituents of the S&P GSCI Commodity Index since January 1999, and 26 developed and emerging markets currency pairs versus the U.S. dollar since January 2000, all through May 2018, he finds that:

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