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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.

Industry vs. Academia on Asset Quality

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

Firm Sales Seasonality as Stock Return Predictor

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

Using Long-horizon Returns to Predict/Time the Stock Market

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

Will the November 2016-December 2017 Run-up in U.S. Stocks Stick?

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

Bitcoin Return Based on Supply and Demand Model

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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P/E10 for Country Stock Market Timing?

“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

Stock Market Earnings Yield and Inflation Over the Long Run

How does the U.S. stock market earnings yield (inverse of price-to-earnings ratio, or E/P) interact with the U.S. inflation rate over the long run? Is any such interaction exploitable? To investigate, we employ the long run dataset of Robert Shiller. Using monthly data for the S&P Composite Stock Index, estimated aggregate trailing 12-month earnings and dividends for the stocks in this index, and estimated U.S. Consumer Price Index (CPI) during January 1871 through June 2017 (about 147 years), and the monthly yield on 3-month U.S. Treasury bills (T-bills) since January 1951, we find that:

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SACEVS-SACEMS Leverage Sensitivity Tests

“SACEVS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Value Strategy” (SACEVS). “SACEMS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS). In response, a subscriber requested a sensitivity test of 1.25X, 1.50X and 1.75X leverage targets. To investigate effects of these leverage targets, we separately augment SACEVS Best Value, SACEMS EW Top 3 and the equally weighted combination of these two strategies by: (1) initially applying target leverage via margin; (2) for each month with a positive portfolio return, adding margin at the end of the month to restore target leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore target leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratiocompound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate. Using monthly total (dividend-adjusted) returns for the specified assets since July 2002 for SACEVS and since July 2006 for SACEMS, both through December 2017, we find that:

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Combined Simple Value-Momentum Asset Class ETF Strategy

The Value Strategy tracks the performance of two versions of the “Simple Asset Class ETF Value Strategy”  (SACEVS), which seeks diversification across a small set of asset class exchange-traded funds (ETF) plus a monthly tactical edge from potential undervaluation of term, credit and equity risk premiums relative to historical averages. The two versions are: (1) most undervalued premium (Best Value); and, (2) weighting all undervalued premiums according to respective degree of undervaluation (Weighted).

The Momentum Strategy tracks the performance of three versions of the “Simple Asset Class ETF Momentum Strategy” (SACEMS), which seeks strategic diversification across asset classes via ETFs plus a monthly tactical edge from intermediate-term momentum. The three versions, all based on total ETF returns over recent months, are: (1) top one of nine ETFs (Top 1); (2) equally weighted top two (EW Top 2); and, (3) equally weighted top three (EW Top 3).

As of today, we commence tracking performance of Combined Value-Momentum Strategy (SACEVS-SACEMS), seeking diversification across asset classes and two widely accepted anomalies. This strategy holds SACEVS Best Value and SACEMS EW Top 3 with equal weights and end-of-month rebalancing coincident with SACEVS and SACEMS portfolio reformations.

Exploitability of Deep Value across Asset Classes

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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