Fundamental Valuation

Do investors systematically and exploitably underreact to deviations in firm fundamentals from recent averages? In their January 2020 paper entitled “Anchoring on Past Fundamentals”, Doron Avramov, Guy Kaplanski and Avanidhar Subrahmanyam investigate how deviations of quarterly firm accounting variables from averages over recent quarters relate to future returns across stocks. They first construct a stock performance deviation index (PDI) based on seven variables: (1) cash and short-term investments, (2) retained earnings, (3) operating income, (4) sales, (5) capital expenditures, (6) invested capital and (7) inventories. They then each month for each stock starting June 1977:

• Calculate the deviation for each variable as the difference between its most recent quarterly value and its average over the preceding three quarters, scaled by total assets.
• Rank each deviation (in percentiles) relative to deviations for the same variable for all stocks.
• Calculate PDI for a stock as the equally weighted average of percentile rankings across all seven variables.

They extend this approach to a more comprehensive fundamental-based deviation index (FDI) that considers deviations of all Compustat accounting variables plus 14 commonly used accounting ratios, with weights of deviation percentile rankings optimized via least absolute shrinkage and selection operator (LASSO) regression starting January 1979. For all variables, if the exact release date is unavailable, they assume a 60-day delay in release. For portfolio tests, they calculate returns to hedge portfolios that are long (short) stocks in the top (bottom) tenth, or decile, of PDIs or FDIs, with holding intervals ranging from one to 24 months. Using monthly data needed to construct PDI, FDI and 30 style, technical, fundamental and liquidity control variables across a broad sample of reasonably liquid U.S. common stocks with positive book values and prices over $5 during January 1976 through October 2017, they find that: Keep Reading

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

Are there any seasonal, technical or fundamental strategies that reliably time the U.S. stock market as proxied by the S&P 500 Total Return Index? In the February 2018 version of his paper entitled “Investing In The S&P 500 Index: Can Anything Beat the Buy-And-Hold Strategy?”, Hubert Dichtl compares excess returns (relative to the U.S. Treasury bill [T-bill] yield) and Sharpe ratios for investment strategies that time the S&P 500 Index monthly based on each of:

• 4,096 seasonality strategies.
• 24 technical strategies (10 slow-fast moving average crossover rules; 8 intrinsic [time series or absolute] momentum rules; and, 6 on-balance volume rules).
• 18 fundamental variable strategies based on a rolling 180-month regression, with 1950-1965 used to generate initial predictions.

In all cases, when not in stocks, the strategies hold T-bills as a proxy for cash. His main out-of-sample test period is 1966-2014, with emphasis on a “crisis” subsample of 2000-2014. He includes extended tests on seasonality and some technical strategies using 1931-2014. He assumes constant stock index-cash switching frictions of 0.25%. He addresses data snooping bias from testing multiple strategies on the same sample by applying Hansen’s test for superior predictive ability. Using monthly S&P 500 Index levels/total returns and U.S. Treasury bill yields since 1931 and values of fundamental variables since January 1950, all through December 2014, he finds that:

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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

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

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

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:

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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:

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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

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