Fundamental Valuation

Which factor models of stock returns are currently best? In their June 2018 paper entitled “q⁵“,  Kewei Hou, Haitao Mo, Chen Xue and Lu Zhang, introduce the q⁵ model of stock returns, which adds a fifth factor (expected growth) to the previously developed q-factor model (market, size, asset growth, return on equity). They measure expected growth as 1-year, 2-year and 3-year ahead changes in investment-to-assets (this year total assets minus last year total assets, divided by last year total assets) as forecasted monthly via predictive regressions. They define an expected growth factor as average value-weighted returns for top 30% 1-year expected growth minus bottom 30% 1-year expected growth, calculated separately and further averaged for big and small stocks. They examine expected growth as a standalone factor and then conduct an empirical horse race of recently proposed 4-factor, 5-factor (including q⁵) and 6-factor models of stock returns based on their abilities to explain average return differences for value-weighted extreme tenth (decile) portfolios for 158 significant anomalies. Using monthly return and accounting data for a broad sample of non-financial U.S. common stocks during July 1963–December 2016, they find that:

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Do changes in firm financial reporting practices signal bad news to come? In the February 2018 update of their paper entitled “Lazy Prices”, Lauren Cohen, Christopher Malloy and Quoc Nguyen investigate relationships between changes in firm financial reporting practices (SEC 10-K, 10-K405, 10-KSB and 10-Q filings) and future firm/stock performance. They focus on quarter-to-quarter changes in content bases on four distinct textual similarity metrics. Each month, they rank all firms into fifths (quintiles) for each of the four metrics. They then compute equally weighted or value-weighted returns for these quintiles over future months (such that there are overlapping portfolios for each quintile and each metric), with stock weights within quintile portfolios rebalanced monthly for equal weighting. They measure the effect of changes in financial reporting practices as monthly return for a hedge portfolio that is long (short) the quintile with the smallest (greatest) past changes. Using the specified quarterly and annual SEC filings by U.S. corporations from the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) database and corresponding monthly stock returns during 1995 through 2014, they find that:

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How well do different measures of stock quality perform as portfolio screens? In the May 2018 update of paper entitled “Does Earnings Growth Drive the Quality Premium?”, Georgi Kyosev, Matthias Hanauer, Joop Huij and Simon Lansdorp review commonly used quality definitions, test their respective powers to predict stock returns and analyze usefulness in constructing international stocks and corporate bonds settings. They consider the following definitions of quality:

• Industry – return on equity (ROE); earnings-to-sales ratio (margin); annual growth in ROE; total debt-to-common equity (leverage); and, earnings variability.
• Academia – gross profitability; accruals; and, net stock issues.

To compare predictive powers, at the end of each month they rank assets into fifths (quintiles) based on each metric and examine equally weighted performances of these quintiles. They calculate gross annualized average excess returns (relative to the risk-free rate) and gross annualized Sharpe ratios for the top and bottom quintiles and the difference between these two quintiles (top-minus-bottom). They also calculate four-factor (market, size, book-to-market and momentum) alphas for top-minus-bottom portfolios. They further analyze equally weighted combinations of all industry metrics and all academic metrics. They consider the largest stocks globally, regionally and from emerging markets. For robustness, they also consider samples of investment-grade and high-yield corporate bonds (with a 12-month rather than one-month holding interval). Using samples of relatively large non-financial common stocks for developed markets (starting December 1985) and emerging markets (starting December 1992) and samples of investment-grade and high-yield corporate bonds (starting January 1994) through December 2014, they find that: Keep Reading

Do firms with predictable sales seasonality continually “surprise” investors with good high season (bad low season) sales and thereby have predictable stock return patterns? In their May 2018 paper entitled “When Low Beats High: Riding the Sales Seasonality Premium”, Gustavo Grullon, Yamil Kaba and Alexander Nuñez investigate firm sales seasonality as a stock return predictor. Specifically, for each quarter, after excluding negative and zero sales observations, they divide quarterly sales by annual sales for that year. To mitigate impact of outliers, they then average same-quarter ratios over the past two years. They then each month:

1. Use the most recent average same-quarter, two-year sales ratio to predict the ratio for next quarter for each firm.
2. Rank firms into tenths (deciles) based on predicted sales ratios.
3. Form a hedge portfolio that is long (short) the market capitalization-weighted stocks of firms in the decile with the lowest (highest) predicted sales ratios.

Their hypothesis is that investors undervalue (overvalue) stocks experiencing seasonally low (high) sales. They measure portfolio monthly raw average returns and four alphas based on 1-factor (market), 3-factor (market, size, book-to-market), 4-factor (adding momentum to the 3-factor model) and 5-factor (adding profitability and investment to the 3-factor model) models of stock returns. Using data for a broad sample of non-financial U.S common stocks during January 1970 through December 2016, they find that: Keep Reading

Is use of a sampling interval much shorter than input variable measurement interval a useful statistical practice in financial markets research? In the April 2018 update of their paper entitled “Long Horizon Predictability: A Cautionary Tale”, flagged by a subscriber, Jacob Boudoukh, Ronen Israel and Matthew Richardson examine statistical reliability gains from overlapping measurements of long-horizon variables (such as daily or monthly sampling of 5-year returns or 10-year moving average earnings). They employ the widely used cyclically adjusted price earnings ratio (CAPE, or P/E10) for some examples. Based on illustrations and mathematical derivations, they conclude that: Keep Reading

Is the strong gain in the U.S. stock market following the November 2016 national election rational or irrational? In their February 2018 paper “Why Has the Stock Market Risen So Much Since the US Presidential Election?”, flagged by a subscriber, Olivier Blanchard, Christopher Collins, Mohammad Jahan-Parvar, Thomas Pellet and Beth Anne Wilson examine sources of the 25% U.S. stock market advance during November 2016 through December 2017. They consider four sources: (1) increases in actual and expected dividends; (2) perceived probability and the fact of a reduction in the corporate tax rate; (3) decrease in the U.S. equity risk premium; and, (4) an irrational price bubble. For the impact of the tax rate reduction on corporate income, they use estimates from the Joint Congressional Committee on Taxation. For the relationship between dividends and the equity risk premium, they assume the difference between dividend-price ratio and risk-free rate equals equity risk premium minus expected dividend growth rate. They also consider the effect of U.S. and European economic policy uncertainty on the U.S. equity risk premium. Using the specified data during November 2016 (and earlier for validation) through December 2017, they find that: Keep Reading

Does the increase in number of Bitcoin wallets at a rate that far exceeds growth in number of Bitcoins explain the dramatic rise in Bitcoin price? In the December revision of his paper entitled “Metcalfe’s Law as a Model for Bitcoin’s Value”, Timothy Peterson models Bitcoin price according to Metcalfe’ Law, which posits that the value of a network (Bitcoin) is a function of the number of possible pair connections (among Bitcoin wallets, assuming all are equal) and is therefore proportional to the square of the number of participants. Said differently, he models Bitcoin value based on supply (number of Bitcoins) and demand (number of Bitcoin wallets). Per Metcalfe’s Law, Bitcoin return is proportional to twice the growth rate of Bitcoin wallets. He tests the model via a least squares regression of actual Bitcoin price on modeled price with adjustment for inflation due to new Bitcoin creation. He applies the model to investigate claims of Bitcoin price manipulation during 2013-2014. Using number of Bitcoins and number of Bitcoin wallets at 60-day intervals during December 31, 2011 through September 30, 2017, he finds that:

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“Usefulness of P/E10 as Stock Market Return Predictor” investigates whether P/E10 (or Cyclically Adjusted Price-Earnings ratio, CAPE) usefully predicts U.S. stock market returns over the long run. That analysis employs Robert Shiller’s data set, which defines P/E10 as inflation-adjusted S&P Composite Index level divided by average monthly inflation-adjusted 12-month trailing earnings of index companies over the last ten years. Do more timely country P/E10 series work for timing country stock markets and trading pairs of country stock markets? Within each country market, higher (lower) P/E10 suggests overvaluation (undervaluation). Across countries, variation in P/E10 gaps arguably indicates which country markets are relatively overvalued and undervalued. To investigate, we consider:

• P/E10 time series for Germany, Japan and the U.S. evaluated separately over available sample periods using DAX, Nikkei 225 and S&P 500 indexes, respectively. We also look at separately timing SPDR S&P 500 (SPY) and iShares MSCI Japan (EWJ).
• Japan P/E10 versus U.S. P/E10 for pair trading of SPY versus EWJ over the available sample period.

Using monthly data for the three P/E10s, the three associated stock market indexes, SPY, EWJ and 3-month U.S. Treasury bill (T-bill) yield as available during December 1981 through December 2017, we find that: Keep Reading

Is value investing particularly profitable when the price spread between cheap and expensive assets (the value spread) is extremely large (deep value)? In their November 2017 paper entitled “Deep Value”, Clifford Asness, John Liew, Lasse Pedersen and Ashwin Thapar examine how the performance of value investing changes when the value spread is in its largest fifth (quintile). They consider value spreads for seven asset classes: individual stocks within each of four global regions (U.S., UK, continental Europe and Japan); equity index futures globally; currencies globally; and, bond futures globally. Their measures for value are:

• Individual stocks – book value-to-market capitalization ratio (B/P).
• Equity index futures – index-level B/P, aggregated using index weights.
• Currencies – real exchange rate based on purchasing power parity.
• Bonds – real bond yield (nominal bond yield minus forecasted inflation).

For each of the seven broad asset classes, they each month rank assets by value. They then for each class form a hedge portfolio that is long (short) the third of assets that are cheapest (most expensive). For stocks and equity indexes, they weight portfolio assets by market capitalization. For currencies and bond futures, they weight equally. To create more deep value episodes, they construct 515 sub-classes from the seven broad asset classes. For asset sub-classes, they use hedge portfolios when there are many assets (272 strategies) and pairs trading when there are few (243 strategies). They conduct both in-sample and out-of-sample deep value tests, the latter buying value when the value spread is within its top inception-to-date quintile and selling value when the value spread reverts to its inception-to-date median. Using data as specified and as available (starting as early as January 1926 for U.S. stocks and as late as January 1988 for continental Europe stocks) through September 2015, they find that:

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What firm/asset/market conditions signal mispricing? In the November 2017 version of their paper entitled “Bonds, Stocks, and Sources of Mispricing”, Doron Avramov, Tarun Chordia, Gergana Jostova and Alexander Philipov investigate drivers of U.S. corporate stock and bond mispricing based on interactions among asset prices, financial distress of associated firms and investor sentiment. They measure financial distress via Standard & Poor’s long term issuer credit rating downgrades. They measure investor sentiment primarily with the multi-input Baker-Wurgler Sentiment Index, but they also consider the University of Michigan Consumer Sentiment index and the Consumer Confidence Index. They each month measure asset mispricing by:

1. Ranking firms into tenths (deciles) based on each of 12 anomalies: price momentum, earnings momentum, idiosyncratic volatility, analyst forecast dispersion, asset growth, investments, net operating assets, accruals, gross profitability, return on assets and two measures of net share issuance.
2. Computing for each firm the equally weighted average of its anomaly rankings, such that a high (low) average ranking indicates the firms’s assets are relatively overpriced (underpriced).

Using monthly firm, stock and bond data for a sample of U.S. firms with sufficient data and investor sentiment during January 1986 through December 2016, they find that: Keep Reading