Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for April 2020 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for April 2020 (Final)
1st ETF 2nd ETF 3rd ETF

Fundamental Valuation

What fundamental measures of business success best indicate the value of individual stocks and the aggregate stock market? How can investors apply these measures to estimate valuations and identify misvaluations? These blog entries address valuation based on accounting fundamentals, including the conventional value premium.

SACEVS with SMA Filter

Does  applying a simple moving average (SMA) filter improve performance of the “Simple Asset Class ETF Value Strategy” (SACEVS), which seeks diversification across the following three asset class exchange-traded funds (ETF) plus cash according to the relative valuations of term, credit and equity risk premiums?

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

Since many technical traders use a 10-month SMA (SMA10), we test effectiveness of requiring that each of the ETFs pass an SMA10 filter by comparing performances for three scenarios:

  1. BaselineSACEVS as currently tracked.
  2. With SMA10 Filter – Run Baseline SACEVS and then apply SMA10 filters to dividend-adjusted prices of ETF allocations. If an allocated ETF is above (below) its SMA10, hold the allocation as specified (Cash). This rule is inapplicable to any Cash allocation.
  3. With Half SMA10 Filter – Same as scenario 2, but, if an allocated ETF is above (below) its SMA10, hold the allocation as specified (half the specified allocation and half cash at the T-bill yield).

We focus on gross compound annual growth rates (CAGR), maximum drawdowns (MaxDD) and annual Sharpe ratios (using average monthly T-bill yield during a year as the risk-free rate for that year) of SACEVS Best Value and SACEVS Weighted portfolios. We also look at how the SMA rule affects a 60%-40% SPY-TLT benchmark (60-40) portfolio. Using SACEVS historical data and monthly dividend-adjusted closing prices for the asset class proxies and yield for Cash during July 2002 (the earliest all ETFs are available) through November 2019, we find that: Keep Reading

Alternative Test of Using P/E10 Thresholds to Time the U.S. Stock Market

A subscriber proposed an alternative to the strategy tested in “Using P/E10 Thresholds to Time the U.S. Stock Market”, which rebalances a stocks-bonds portfolio based on Shiller cyclically adjusted price-to-earnings ratio (P/E10 or CAPE) thresholds, as follows:

  1. If P/E10 > 22, hold 40% stocks and 60% bonds.
  2. If 14 < P/E10 < 22, hold 60% stocks and 40% bonds.
  3. If P/E10 < 14, hold 80% stocks and 20% bonds.

The alternative strategy (P/E10 Variable Timing) uses linear scaling of the allocation to stocks from 40% to 80% as the P/E10 rises from 14 to 22. To test the alternative, we apply it to SPDR S&P 500 (SPY) since inception in 1993 as stocks and Vanguard Long-Term Treasury Investor Shares (VUSTX) as bonds, with monthly rebalancing/reallocation based on P/E10. We consider gross average monthly and annual returns, standard deviations of monthly and annual returns, compound annual growth rate (CAGR), maximum drawdown (MaxDD), and monthly and annual Sharpe ratio as strategy performance metrics. We use monthly and annual average monthly yield on 3-month U.S. Treasury bills (T-bill) to calculate Sharpe ratios. The benchmark is the original strategy (P/E10 Fixed Timing). Using the specified inputs, allowing a test of nearly 27 years, we find that: Keep Reading

Best Factor Model of U.S. Stock Returns?

Which equity factors from among those included in the most widely accepted factor models are really important? In their October 2019 paper entitled “Winners from Winners: A Tale of Risk Factors”, Siddhartha Chib, Lingxiao Zhao, Dashan Huang and Guofu Zhou examine what set of equity factors from among the 12 used in four models with wide acceptance best explain behaviors of U.S. stocks. Their starting point is therefore the following market, fundamental and behavioral factors:

They compare 4,095 subsets (models) of these 12 factors models based on: Bayesian posterior probability; out-of-sample return forecasting performance; gross Sharpe ratios of the optimal mean variance factor portfolio; and, ability to explain various stock return anomalies. Using monthly data for the selected factors during January 1974 through December 2018, with the first 10 (last 12) months reserved for Bayesian prior training (out-of-sample testing), they find that: Keep Reading

Using P/E10 Thresholds to Time the U.S. Stock Market

A subscriber requested verification of a fundamental U.S. stock market timing strategy with rebalancing/reallocation of a stocks-bonds portfolio based on Shiller cyclically adjusted price-to-earnings ratio (P/E10 or CAPE) thresholds, as follows:

  1. If P/E10 > 22, hold 40% stocks and 60% bonds.
  2. If 14 < P/E10 < 22, hold 60% stocks and 40% bonds.
  3. If P/E10 < 14, hold 80% stocks and 20% bonds.

The benchmark is an annually rebalanced 60% stocks-40% bonds portfolio (60-40). To assess reasonableness of the P/E10 thresholds chosen, we use P/E10 monthly levels since 1881 and S&P 500 Index monthly returns since 1927. To verify and assess robustness of the specified strategy (P/E10 Timing), we apply it to SPDR S&P 500 (SPY) since inception in 1993 as stocks and Vanguard Long-Term Treasury Investor Shares (VUSTX) as bonds, with monthly rebalancing/reallocation based on P/E10. We consider gross average monthly and annual returns, standard deviations of monthly and annual returns, compound annual growth rate (CAGR), maximum drawdown (MaxDD), and monthly and annual Sharpe ratio as strategy performance metrics. We use monthly and annual average monthly yield on 3-month U.S. Treasury bills (T-bill) to calculate Sharpe ratios. As an additional benchmark, we include a simple technical strategy that is in SPY when prior-month S&P 500 Index is above its 10-month simple moving average and VUSTX when it is below (SPY SMA10). Using the specified inputs, allowing a P/E10 Timing test of nearly 27 years, we find that: Keep Reading

Including Basis to Qualify Multi-class Intrinsic Momentum

Does including a measure of asset valuation as a qualifier improve the performance of intrinsic (absolute or time series) momentum? In their October 2019 paper entitled “Carry and Time-Series Momentum: A Match Made in Heaven”, Marat Molyboga, Junkai Qian and Chaohua He investigate modification of an intrinsic momentum strategy as applied to futures using the sign of the basis (difference between nearest and next-nearest futures prices) for four asset classes: equity indexes (12 series), fixed income (18 series), currencies (7 series) and commodities (28 series). Their benchmark intrinsic momentum strategy is long (short) assets with positive (negative) returns over the last 12 months, with either: (1) equal allocations to assets, or (2) dynamic allocations that each month target 40% annualized volatility for each contract series. The modified strategy limits long (short) positions to assets with positive (negative) prior-month basis. They account for frictions due to portfolio rebalancing and rolling of contracts using cost estimates from a prior study. They focus on Sharpe ratio to assess strategy performance. Using monthly returns for 65 relatively liquid futures contract series during January 1975 through December 2016, they find that:

Keep Reading

Trading U.S. Stocks on Core Earnings

Does careful accounting for transitory expenses in SEC Form 10-Ks provide a better view of future firm/stock performance than that provided by Generally Accepted Accounting Practices (GAAP) earnings per share (EPS)? In their October 2019 paper entitled “Core Earnings: New Data and Evidence”, Ethan Rouen, Eric So and Charles Wang define Core Earnings, which adds to GAAP 10-K net non-operating expenses related to: (1) acquisitions, (2) currency exchange adjustments, discontinued operations, (4) legal or regulatory events, (5) pension adjustments, (6) restructuring, (7) gains and losses designated “other” by firms and (8) other unclassified gains and losses deemed non-operating. Using a dataset compiled by a combination of human analysts and machine learning that identifies and classifies quantitative disclosures in 10-Ks of Russell 3000 firms, and associated stock prices, during 1998 through 2017, they find that:

Keep Reading

Stock Index Earnings-returns Lead-lag

A subscriber asked about the lead-lag relationship between S&P 500 earnings and S&P 500 Index returns. To investigate, we relate actual aggregate S&P 500 operating and as-reported earnings to S&P 500 Index returns at both quarterly and annual frequencies. Earnings forecasts are available well in advance of returns. Actual earnings releases for a quarter occur throughout the next quarter. Using quarterly S&P 500 earnings and index levels during March 1988 through June 2019 and September 2019, respectively, we find that: Keep Reading

Systemic Risk Impacts of Growth in Passive Investing

How does a shift in emphasis from active to passive investing affect the financial market risk landscape? In their September 2019 paper entitled “The Shift From Active to Passive Investing: Potential Risks to Financial Stability?”, Kenechukwu Anadu, Mathias Kruttli, Patrick McCabe, Emilio Osambela and Chaehee Shin analyze how a shift from active to passive investing affects:

  1. Investment fund redemption liquidity risks.
  2. Market volatility.
  3. Asset management industry concentration.
  4. Co-movement of asset returns and liquidity.

They also assess how effects are likely to evolve if the active-to-passive shift continues. Based on their framework/analysis, they conclude that: Keep Reading

FactSet S&P 500 Earnings Growth Estimate Evolutions

A subscriber, citing the weekly record of S&P 500 earnings growth estimates in the “FactSet Earnings Insight” historical series, wondered whether estimate trends/revisions are exploitable. To investigate, we collect S&P 500 quarterly year-over-year earnings growth estimates as recorded in this series. These data are bottom-up (firm by firm) aggregates, whether purely from analyst estimates (before any actual earnings releases), or a blend of actual earnings and estimates (during the relevant earnings season). Using these data and contemporaneous weekly levels of the S&P 500 Index during April 2011 through mid-September 2019, we find that: Keep Reading

Exploiting Stocks that Incorporate News Slowly

Can investors identify stocks that incorporate news slowly enough to allow exploitation? In their August 2019 paper entitled “Tomorrow’s Fish and Chip Paper? Slowly Incorporated News and the Cross-Section of Stock Returns”, Ran Tao, Chris Brooks and Adrian Bell classify stocks incorporating news quickly (QI) or slowly (SI) into prices and investigate implications for associated future returns. Specifically, they each month:

  1. Assign a sentiment score to each current-month news article about each stock based on words in the article.
  2. Double-sort stocks by thirds based first on current-month abnormal (adjusted for size, industry value and industry momentum) returns and then on news sentiment scores, yielding nine groups.
  3. Classify stocks that are: (a) high return/low sentiment (HRLS) or low return/high sentiment (LRHS) as SI; and, (b) high return/high sentiment (HRHS) or low return/low sentiment (LRLS) as QI.
  4. Measure average next-month returns of equally-weighted SI and QI portfolios that are, respectively: (a) long LRHS stocks and short HRLS stocks; and, (b) long HRHS stocks and short LRLS stocks.
  5. Measure average next-month return of an equally weighted portfolio that is long the SI portfolio and short the QI portfolio (Slow-Minus-Quick, SMQ).

They then examine whether limited investor attention or differences in news complexity and informativeness better explain results. Using firm-level news data, firm characteristics and associated stock returns for a broad sample of U.S. common stocks during 1979 through 2016, they find that: Keep Reading

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