Fundamental Valuation

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

Do long lags between end of firm quarterly and annual financial reporting periods and issuance of SEC-required financial reports (10-Q and 10-K) indicate internal firm inefficiencies and/or reluctance to disclose adverse performance? In their August 2019 paper entitled “Filing, Fast and Slow: Reporting Lag and Stock Returns”, Karim Bannouh, Derek Geng and Bas Peeters study the impact of reporting lag (number of days between the end of reporting period and filing date of the corresponding report) on future stock returns. They focus on firms with market capitalizations greater than $750 million that have deadlines of 40 days after quarter end for quarterly reports and 60 days after year end for annual reports (accelerated filers). They each month:

1. Sort stocks into fifths, or quintiles, based on reporting lag separately for the most recent 10-K and the most recent 10-Q filings.
2. Reform a portfolio that is long (short) the equal-weighted quintile with the shortest (longest) lags.

They measure risk-adjusted portfolio performance via monthly gross 1-factor (market), 3-factor (plus size and book-to-market) and 4-factor (plus momentum) alphas. Using 10-K and 10-Q filings from the SEC and monthly characteristics and stock returns for a broad (but groomed) sample of U.S. accelerated filers (roughly 1,500 stocks), and a comparable sample of European stocks, during 2007 through 2018 period, they find that:

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Can investors effectively use firm characteristics to screen European stocks? In their August 2019 paper entitled “Predictability and the Cross-Section of Expected Returns: Evidence from the European Stock Market”, Wolfgang Drobetz, Rebekka Haller, Christian Jasperneite and Tizian Otto examine the power of 22 firm characteristics to predict stock returns individually and jointly. They assume market-based characteristics are available immediately and accounting-based characteristics are available four months after firm fiscal year end. For multi-characteristic predictions, they consider 5-characteristic, 8-characteristic and 22-characteristic models. For regression-based forecasts, they use either 10-year rolling or inception-to-date monthly inputs. For economic tests, they form equal-weighted or value-weighted portfolios that are each month long (short) the tenth, or decile, of stocks with the the highest (lowest) expected next-month returns based on 22-characteristic regression outputs. To estimate net performance, they apply one-way trading frictions of 0.57%. Using groomed monthly data for all firms in the STOXX Europe 600 index during January 2003 through December 2018, they find that:

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How does the market react when firms announce adoption of blockchain technology? In the May 2019 draft of their paper entitled “Bitcoin Speculation or Value Creation? Corporate Blockchain Investments and Stock Market Reactions”, Don Autore, Nicholas Clarke and Danling Jiang study stock price reactions to initial public announcements of investments in blockchain technology by listed U.S. firms. Their key metric is buy-and-hold abnormal return (BHAR) relative to each of five benchmarks: (1) portfolios of stocks matched on size and book-to-market (BM); (2) portfolios of stocks matched on market beta; 3) a broad value-weighted market index; (4) iShares Global Financials ETF (IXG); and, (5) iShares Global Tech ETF (IXN). Their announcement event windows is five trading days before initial public announcement of an investment in blockchain technology (-5) to 65 trading days after (65). Using dates of initial public announcements of investments in blockchain technology and contemporaneous daily returns for 207 stocks listed on NYSE and NASDAQ during October 2008 through March 2018, they find that:

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Does duration (relative arrival sequence) of firm cash flows explain many widely accepted equity factor returns? In their April 2019 paper entitled “Duration-Driven Returns”, Niels Gormsen and Eben Lazarus investigate whether firm cash flow duration explains value, profitability, investment, low risk, idiosyncratic volatility and payout factor returns. They measure cash flow duration monthly via multiple regressions that relate analyst long-term growth estimates for each firm to its profitability, investment, low risk (market beta), idiosyncratic volatility and payout. They then each month for U.S. and global stocks separately reform four value-weighted sub-portfolios:

1. Above-median NYSE market capitalization and top 30% of duration.
2. Above-median NYSE market capitalization and bottom 30% of duration.
3. Below-median NYSE market capitalization and top 30% of duration.
4. Below-median NYSE market capitalization and bottom 30% of duration.

They specify the duration factor as return to a portfolio that is each month long (short) the two equal-weighted long-duration (short-duration) sub-portfolios. As a robustness test, they separately analyze a sample of single-stock dividend futures (dividend strips, claims to dividends to be paid out during a given calendar year), which allow varying duration characteristics while keeping maturity of cash flows fixed. Using monthly data for a broad sample of U.S. stocks starting August 1963, monthly data for global stocks starting July 1990, and annual data for 150 single-stock dividend futures with up to 5-year maturity starting January 2010, all through December 2018, they find that: Keep Reading

Are variables determined in published papers to be statistically significant predictors of stock market returns really useful to investors? In their November 2018 paper entitled “On the Economic Value of Stock Market Return Predictors”, Scott Cederburg, Travis Johnson and Michael O’Doherty assess whether strength of in-sample statistical evidence for 25 stock market predictors published in top finance journals translates to economic value after accounting for some realistic features of returns and investors. Predictive variables include valuation ratios, volatility, variance risk premium, tail risk, inflation, interest rates, interest rate spreads, economic variables, average correlation, short interest and commodity prices. Their typical investor makes mean-variance optimal allocations between the stock market and a risk-free security (yielding a fixed 2% per year) via Bayesian inference based on a vector autoregression model of market return-predictor dynamics. The investor has moderate risk aversion and a 1-month or longer investment horizon (reallocates monthly). Stock market returns and predictors exhibit randomly varying volatility. They focus on annual certainty equivalent return (CER) gain, which incorporates investor risk aversion, to quantify economic value of market predictability. Using monthly U.S. stock market returns and data required to construct the 25 predictive variables as available (starting as early as January 1927 and as late as June 1996 across variables) through December 2017, they find that:

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How should investors assess whether the market is fairly valuing cryptocurrencies such as Bitcoin? In his March 2019 paper entitled “Bitcoin Spreads Like a Virus”, Timothy Peterson offers a way to value Bitcoin based on Metcalf’s Law (network economics) and  a Gompertz function (often used to describe biological activity). The former model estimates fair price based on number of active users, and the latter model estimates the growth rate of active users. Using findings from prior research plus daily Bitcoin price and active account data from coinmetrics.io and blockchain.info during July 2010 through February 2019, he finds that: Keep Reading

Can a deep dive into company accounting data reliably predict stocks that will underperform? In their February 2019 paper entitled “Earnings Quality on the Street”, Urooj Khan, Venkat Peddireddy and Shivaram Rajgopal examine proprietary reports from a research firm that screens publicly available accounting data for over 9,000 North American and 5,500 other global companies to identify those in poor financial health (book value and earnings quality). Mutual funds, money managers, hedge funds, insurance companies, banks, CPA firms, law firms and individual investors subscribe to these reports. The research firm emphasizes importance of industry-specific metrics, evaluating whether each metric for a company is abnormal (aggressively optimistic) relative to peers and to its own history. Using 1,029 reports on aggressive reporting practices for 348 unique companies, and associated future daily stock returns, during 2004 through 2015, they find that:

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What kinds of fundamental and technical indicators play well together? In their August 2018 paper entitled “When Buffett Meets Bollinger: An Integrated Approach to Fundamental and Technical Analysis”, Zhaobo Zhu and Licheng Sun test performance of six stock portfolios that jointly exploit one of three popular fundamental indicators and one of two popular technical indicators, as follows:

1. Piotroski’s FSCORE – each quarter long (short) stocks having high (low) scores summarizing a composite of accounting variables.
2. Standardized unexpected earnings (SUE) – each quarter long (short) the fifth of stocks with the highest (lowest) earnings surprises.
3. Return on equity (ROE) – each quarter long (short) the fifth of stocks with the highest (lowest) ROEs.
4. Moving averages (MA) – each month long (short) stocks with 20-day MAs above (below) 125-day MAs at the end of the prior month.
5. Bollinger bands (BOLL) – long (short) stocks below (above) one standard deviation of daily prices below (above) the average prices over the past 20 trading days.

Specifically, for each of six fundamental-technical pairs, they each month reform a portfolio that is long (short) stocks with both fundamental and technical buy (sell) signals. For risk adjustment, they employ widely used 5-factor (market, size, book-to-market, profitability, investment) alpha. Using accounting data and stock returns for a broad sample of U.S. common stocks priced at least $5, plus monthly factor returns, during January 1985 through December 2015, they find that:

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Can stock return forecasts from fundamental analysis make conventional mean-variance stock portfolio optimization work? In their December 2018 paper entitled “Optimized Fundamental Portfolios”, Matthew Lyle and Teri Yohn construct a portfolio that combines fundamentals-based stock return forecasts and mean-variance optimization and then compare results with portfolios from each employed separately. To suppress implementation costs, they focus on long-only portfolios reformed quarterly. Their fundamentals return forecasting model uses cross-sectionally normalized versions of book-to-market ratio, return on equity, change in net operating assets divided by book value and change in financial assets divided by book value. They update fundamental variables quarterly at the end of the reporting month. They generate stock return forecasts via a complicated multivariate regression of cross-sectionally normalized versions of the variables based on five years of rolling historical data. They then form a portfolio of the tenth (decile) of stocks with the highest expected returns, either value-weighted or equal-weighted. They consider several portfolio optimization methods, including minimum variance (requiring no return forecasts); mean-variance optimization with target expected return; and, Sharpe ratio maximization. Their combined approach employs fundamental stock return forecasts as inputs to those portfolio optimization methods that require returns. They use data from 1991-1995 to generate initial model inputs and 1996-2015 for out-of-sample testing. Using end-of-month data for a broad but groomed sample of U.S. common stocks with at least three years of historical data during January 1991 through December 2015, they find that:

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