Fundamental Valuation

Does adding fundamental firm quality metrics to refine stock sorts based on traditional value ratios, book-to-market ratio (B/M) and earnings-to-price ratio (E/P), improve portfolio performance? In his 2013 paper entitled “The Quality Dimension of Value Investing”, Robert Novy-Marx tests combination strategies to determine which commonly used quality measures most enhance the performance of value ratios. He considers such quality metrics as Piotroski’s FSCORE, earnings accruals, gross profitability (GP) and return on invested capital (ROIC). His general test approach is to reform capitalization-weighted portfolios annually from stocks sorted at the end of each June according to value ratios and quality metrics for the previous calendar year. He uses the 1000 largest (2000 next largest) stocks by market capitalization to represent large (small) stocks. He considers both long-only (long the top 30%) and long-short (long the top 30% and short the bottom 30%) portfolios. He also considers the incremental benefit of incorporating stock price momentum based on return over the previous 11 months with a skip-month (11-1) into stock selection. He estimates trading frictions based on calculated turnover and effective bid-ask spreads. Using stock prices and associated firm fundamentals during July 1963 through December 2011, he finds that: Keep Reading

Do equity sectors have exploitably measurable relative value? In his February 2013 paper entitled “Equity Sector Rotation via Credit Relative Value” (the National Association of Active Investment Managers’ 2013 Wagner Award winner), Dave Klein outlines a long-only strategy that ranks Standard & Poor’s Select Sector SPDR exchange-traded fund (ETF) based on relative value. The strategy seeks to exploit a belief that sector valuations increase (decrease) differently as macro credit risk falls (rises). Specifically, the strategy each week: (1) regresses the weekly unadjusted price for each sector ETF versus the weekly option-adjusted spread for the Bank of America/Merrill Lynch High Yield B (HY/B) credit index over the last six months to determine a best-fit (fair value) line; (2) ranks sector ETFs from cheapest to most expensive (percentage below or above their respective fair value lines) based on the prior-day HY/B index value; and, (3) forms long-only, equally weighted portfolios of the cheapest one (Top 1) to eight (Top 8) ETFs. He asserts that a six-month regression is long enough to discover the relationship between ETF price and credit index, and short enough to ignore inflation and dividends and “forget” major market disruptions. He uses SPDR S&P 500 (SPY) and an equally weighted portfolio of all nine sector ETFs (All 9) as benchmarks. An alternative strategy substitutes 3-month U.S. Treasury bills (T-bills) for any of the cheapest ETFs in a portfolio that are still expensive (above their respective fair value lines). He also considers a relaxation of weekly portfolio reformation/rebalancing. Using weekly levels of the sector ETFs (both unadjusted and adjusted for dividends), the HY/B credit index and the T-bill yield during July 1999 through December 2012, he finds that: Keep Reading

What works better for currency trading, technical or fundamental analysis? In their April 2013 working paper entitled “Exchange Rate Expectations of Chartists and Fundamentalists”, Christian Dick and Lukas Menkhoff compare the behavior and performance of technical analysts (chartists) and fundamental analysts (fundamentalists) based on monthly surveys of several hundred German professional dollar-euro exchange rate forecasters, in combination with respondent self-assessments regarding emphasis on technical and fundamental analysis. Forecasts are directional only (whether the dollar will depreciate, stay the same or appreciate versus the euro) at a six-month horizon. The authors examine three self-assessments (from 2004, 2007 and 2011) to classify forecasters as chartists (at least 40% weight to technical analysis), fundamentalists (at least 80% weight to fundamental analysis) or intermediates. Using responses from 396 survey respondents encompassing 33,861 monthly time-stamped forecasts and contemporaneous dollar-euro exchange rate data during January 1999 through September 2011 (153 months), they find that: Keep Reading

Does the asset growth effect (growth is bad) exist in non-U.S. equity markets? In their July 2012 paper entitled “The Asset Growth Effect: Insights from International Equity Markets”, Akiko Watanabe, Yan Xu, Tong Yao and Tong Yu investigate the asset growth effect in and across international stock markets. They consider two tests, both based on annual data available as of the end of June each year: (1) form portfolios of stocks ranked in deciles (tenths) by asset growth rate within countries and pooled across countries, and calculate next-year average gross portfolio returns; and, (2) within each country, regress next-year gross stock return versus asset growth rate. Using return and asset data for non-financial stocks in 43 country markets (including the U.S.) that have at least 30 qualifying stocks during July 1982 through June 2010, they find that: Keep Reading

Do Piotroski’s FSCORE for value stocks and Mohanram’s GSCORE for growth stocks predict winners and losers for non-U.S. stocks? In their March 2013 paper entitled “Fundamental Based Market Strategies”, Angelo Aspris, Nigel Finch, Sean Foley and Zachary Meyer apply previously documented fundamental (accounting-based) strategies to identify Australian stocks expected to outperform and underperform. Specifically, they consider FSCORE, GSCORE (sans advertising input due to lack of data) and RM-Index (combining a valuation assessment with measures of financial performance, creditworthiness, liquidity and operational efficiency). The FSCORE (GSCORE) ranking focuses on the fifth of stocks with the highest (lowest) book-to-market ratios, while the RM-Index ranking considers all stocks in the universe. Annual rescoring of stocks occurs at the beginning of the fifth month after financial year-ends to ensure public availability of data at the time of portfolio reformation. Using returns and fundamentals for S&P/ASX 300 firms during 2000 through 2010, they show that: Keep Reading

Wesley Gray (founder and executive managing member of Empiritrage LLC and Turnkey Analyst LLC) and Tobias Carlisle (founder and managing member of Eyquem Investment Management LLC) describe their 2013 book, Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors, as “first and foremost about value investment–treating stock as part ownership of a business valued through analysis of fundamental financial statement data. …There are several quantitative measures that lead to better performance…: enhancing the margin of safety, identifying the highest quality franchises, and finding the cheapest stocks. We canvass the research in each, test it in our own system, and then combine the best ideas in each category into a comprehensive quantitative value strategy.” Using price and fundamental data for a broad sample of U.S. stocks (over about 40 years ending with 2011) to confirm and refine key findings of value investing research streams, they argue and find that: Keep Reading

Are there exploitable long-term cycles in U.S. stock market returns? In the January 2013 update of his paper entitled “Secular Mean Reversion and Long-Run Predictability of the Stock Market”, Valeriy Zakamulin explores mean reversion of the S&P Composite Index over intervals ranging from two to 40 years. He then runs an out-of-sample horse race using inception-to-date data to compare three regression-based models for forecasting long-term stock market returns: (1) mean reversion over the dynamically optimal horizon; (2) the random walk (future mean return equals (evolving) historical mean return); and, (3) valuation based on Robert Shiller’s cyclically adjusted price-to-earnings ratio (P/E10). Using real (Consumer Price Index-adjusted) S&P Composite Index total annual returns and earnings over the period 1871 through 2011 (141 years), he finds that: Keep Reading

Does market-driven deviation of the price of an exchange-traded fund (ETF) from its net asset value (NAV) predict an exploitable future return? In the September 2012 draft of their paper entitled “Reading Tomorrow’s Newspaper: Predictability in ETF Returns”, Jon Fulkerson and Bradford Jordan examine the relationship between price-to-NAV ratio and next-day return for ETFs. Using daily opening and closing prices for 762 equity ETFs and their components as available during January 2000 through early February 2011 equity, with the extreme 1% of recorded price-to-NAV values trimmed to exclude data entry errors, they find that: Keep Reading

The authors of the study summarized in “Exploit ETN Deviation from Indicative Value?” argue that deviations of prices for exchange-traded notes (ETN) from their indicative (immediate redemption) values may be useful as trading signals. How well does this mispricing concept work for the very liquid iPath S&P 500 VIX Short-term Futures ETN (VXX)? To check, we consider several mispricing thresholds and measure the short-term profitability of both long and short trades based on these thresholds. Using daily split-adjusted opening and closing prices and closing indicative values for VXX from inception at the end of January 2009 through mid-September 2012 (916 trading days), we find that: Keep Reading

Issuers of exchange-traded notes (ETN) publish daily indicative (immediate redemption) values for these debt instruments. Does deviation of the market price of an ETN from its indicative value represent an exploitable mispricing? In their September 2012 paper entitled “Mispricing and Trading Profits in ETNs”, Dean Diavatopoulos, Helyette Geman, Lovjit Thukral and Colby Wright investigate the gross profitability of a simple weekly trading strategy that buys (sells) ETNs priced under (over) associated indicative values. Specifically, they identify mispricings at the close each Tuesday (to avoid holiday and weekend effects), enter equally weighted positions at the next open and exit one week later. They test mispricing thresholds ranging from 0.50% to 20%. To assure liquidity, they specify target ETNs based on minimum average daily share volumes of 20,000 (34 ETNs), 50,000 (15 ETNs), 100,000 (12 ETNs) or 250,000 (seven ETNs). They ignore trading frictions, liquidity issues and shorting costs/constraints. Using Tuesday closing prices and indicative values and Wednesday opening prices for the specified ETNs during June 2006 through January 2012 (5.7 years), they find that: Keep Reading