Fundamental Valuation

Will equity funds “managed” by artificial intelligence (AI) outperform human investors? To investigate, we consider the performance of AI Powered Equity ETF (AIEQ), which “seeks to provide investment results that exceed broad U.S. Equity benchmark indices at equivalent levels of volatility.” Per the offeror, the EquBot model supporting AIEQ: “…leverages  IBM’s Watson AI to conduct an objective, fundamental analysis of U.S.-listed common stocks and real estate investment trusts…based on up to ten years of historical data and apply that analysis to recent economic and news data. Each day, the EquBot Model…identifies approximately 30 to 125 companies with the greatest potential over the next twelve months for appreciation and their corresponding weights… The EquBot model limits the weight of any individual company to 10%. At times, a significant portion of the Fund’s assets may consist of cash and cash equivalents.” We use SPDR S&P 500 (SPY) as a simple benchmark for AIEQ performance. Using daily dividend-adjusted closes of AIEQ and SPY from AIEQ inception (October 18, 2017) through June 2019, we find that: Keep Reading

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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A subscriber asked about current evidence that high-dividend stocks outperform the market. To investigate, from a practical perspective, we compare performances of five high-dividend stock exchange-traded funds (ETFs) with relatively long histories to that of SPDR S&P 500 (SPY) as a proxy for the U.S. stock market. The five high-dividend stock ETFs are:

iShares Select Dividend (DVY), with inception November 2003.
PowerShares Dividend Achievers ETF (PFM), with inception September 2005.
SPDR S&P Dividend ETF (SDY), with inception November 2005.
WisdomTree Dividend ex-Financials ETF (DTN), with inception June 2006.
Vanguard High Dividend Yield ETF (VYM), with inception November 2006.

For each of these ETFs, we compare average monthly total (dividend-reinvested) return, standard deviation of total monthly returns, monthly reward-risk ratio (average monthly return divided by standard deviation), compound annual growth rate (CAGR) and maximum drawdown (MaxDD) to those for SPY over matched sample periods. We also look at alphas and betas for the five ETFs based on simple regressions of monthly returns versus SPY returns. Using monthly total returns for five high-dividend stock ETFs and SPY over available sample periods through February 2019, we 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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Is value of a firm’s patents a reliable predictor of its stock returns? In their November 2018 paper entitled “Patent-to-Market Premium”, Jiaping Qiu, Kevin Tseng and Chao Zhang investigate firm patent-to-market (PTM) ratio (percentage of market value attributable to patents) as a predictor of stock returns. They specify PTM ratio for each firm as follows:

1. Measure stock reaction to each patent grant date.
2. Depreciate each patent since grant date via an inventory depreciation method.
3. Estimate cumulative market value of all patents held by adding current depreciated values.
4. Divide cumulative patent value by firm market value.

They then at the end of June each year reform a hedge portfolio that is long (short) the tenth, or decile, of stocks with the highest (lowest) PTM ratios. Using market and lagged accounting data for a broad sample of U.S. common stocks/firms and intersecting patent data (5,475 distinct firms) during 1965 through 2010, they find that: Keep Reading

Does active stock factor exposure management boost overall portfolio performance? In their November 2018 paper entitled “Optimal Timing and Tilting of Equity Factors”, Hubert Dichtl, Wolfgang Drobetz, Harald Lohre, Carsten Rother and Patrick Vosskamp explore benefits for global stock portfolios of two types of active factor allocation:

1. Factor timing – exploit factor premium time series predictability based on economic indicators and factor-specific technical indicators.
2. Factor tilting – exploit cross-sectional (relative) attractiveness of factor premiums.

They consider 20 factors spanning value, momentum, quality and size. For each factor each month, they reform a hedge portfolio that is long (short) the equal-weighted fifth, or quintile, of stocks with the highest (lowest) expected returns for that factor. For implementation of factor timing, they consider: 14 economic indicators standardized by subtracting respective past averages and dividing by standard deviations; and, 16 technical indicators related to time series momentum, moving averages and volatilities. They suppress redundancy and noise in these indicators via principal component analysis separately for economic and technical groups, focusing on the first principal component of each group. They translate any predictive power embedded in principal components into optimal factor portfolio weights using augmented mean-variance optimization. For implementation of factor tilting, they overweight (underweight) factors that are relatively attractive (unattractive) based on valuations of factor top and bottom quintile stocks, top-bottom quintile factor variable spreads, prior-month factor returns (momentum) and volatilities of past monthly factor returns. Their benchmark portfolio is the equal-weighted combination of all factor hedge portfolios. For all portfolios, they assume: monthly portfolio reformation costs of 0.75% (1.15%) of turnover value for the long (short) side; and, annual 0.96% cost for an equity swap to ensure a balanced portfolio of factor portfolios. For monthly factor timing and tilting portfolios only, they assume an additional cost of 0.20% of associated turnover. Using monthly data for a broad sample of global stocks from major equity indexes and for specified economic indicators during January 1997 through December 2016 (4,500 stocks at the beginning and 5,000 stocks at the end), they find that: Keep Reading