Fundamental Valuation

Can investors rely on overvaluation signals from the market price-earnings ratio (P/E) and the Fed Model to predict major stock market corrections? Which model works better? In their July 2013 paper entitled “Does the Bond-Stock Earning Yield Differential Model Predict Equity Market Corrections Better Than High P/E Models?”, Sebastien Lleo and William Ziemba test the power of eight bond-stock earnings yield differential model (BSEYD) variants and eight market P/E model variants to predict stock market corrections. They specify the bond yield for the BSEYD model as that of the 10-year U.S. Treasury note (T-note). They define a stock market correction as a decline of 10% or more within one year. They specify the 16 model variants based on: (1) either BSEYD, the natural logarithm of BSEYD, P/E or the natural logarithm of P/E; (2) either current year or rolling 10-year average stock market earnings; and, (3) either of two ways of calculating the threshold for extreme overvaluation. Both methods of setting the extreme overvaluation threshold for the 16 indicators are out-of-sample based on indicator average and standard deviation over a rolling one-year historical window. They measure success of model variants based on both the proportion of signals followed by corrections within two years and, conversely, the proportion of crashes preceded by signals within the past two years. Using daily S&P 500 Index levels, S&P 500 earnings data and daily T-note yields during 1962 through 2012, they find that: Keep Reading

How well does stock screening research translate into performance? In the mid-1990s, James O’Shaughnessy identified “cornerstone value” and “cornerstone growth” as best-of-breed equity investment strategies. The former emphasizes dividends among large-capitalization stocks, and the latter momentum/earnings growth for a broader universe. Based on Standard and Poor’s Compustat data, he found that the value (growth) strategy returned 15% (18%) per year during 1952-1994, compared to 8.3% for the S&P 500 Index. He implemented these two strategies in late 1996 via mutual funds and publicized them in early editions of his book What Works on Wall Street: A Guide to the Best-Performing Investment Strategies of All Time. He subsequently sold the mutual funds (which apply slightly different portfolio formation rules from those specified in the original research) to Hennessy Funds in 2000, where they survive as the Hennessy Cornerstone Value Fund (HFCVX) and the Hennessy Cornerstone Growth Fund (HFCGX). Do these funds outperform simpler exchange-traded funds (ETF) that track their respective benchmarks funds: iShares Russell 1000 Value Index (IWD) for HFCVX and iShares Russell 2000 Index (IWM) for HFCGX? Using monthly total returns for HFCVX, HFCGX, IWD and IWM during May 2000 (inception of the ETFs) through July 2013, we find that: Keep Reading

Does the carry trade concept provide a useful framework for valuation of securities within and across all asset classes? In their July 2013 paper entitled “Carry”, Ralph Koijen, Tobias Moskowitz, Lasse Pedersen and Evert Vrugt investigate expected return across asset classes via decomposition into “carry” (expected return assuming price does not change) and expected price appreciation. They measure carry for: global equities; global 10-year bonds; global bond yield spread (10-year minus 2-year); currencies; commodities; U.S. Treasuries; credit; equity index call options; and equity index put options. Their measurements of carry vary by asset class (based on: futures prices for equity indexes, currencies and commodities, modeled futures prices for global bonds, U.S. Treasuries and credit; and, option prices for options). They further decompose carry returns into passive and dynamic components. The passive component is the return to a hedge (carry trade) portfolio designed to capture differences in average carry returns across securities, and the dynamic component indicates how well carry predicts future price appreciation. Finally, they determine the conditions under which carry strategies perform poorly across all asset classes. Using monthly price/yield data for multiple assets within each class as available during January 1972 through September 2012, they find that: Keep Reading

What initial retirement portfolio withdrawal rate is sustainable over long horizons when, as currently, bond yields are well below and stock market valuations well above historical averages? In their June 2013 paper entitled “Asset Valuations and Safe Portfolio Withdrawal Rates”, David Blanchett, Michael Finke and Wade Pfau apply predictions of bond yields and stock market returns to estimate whether various initial withdrawal rates succeed over different retirement periods. They define initial withdrawal rate as a percentage of portfolio balance at retirement, escalated by inflation each year thereafter. They simulate future bond yield as a linear function of current bond yield with noise, assuming a long-term average of 5% and bounds of 1% and 10%. They simulate future U.S. stock mark return as a linear function of Cyclically Adjusted Price-to-Earnings ratio (CAPE, or P/E10), the ratio of current stock market level to average earnings over the last ten years, assuming P/E10 has a long-term average of 16.4 with noise (implying average annual return 10% with standard deviation 20%). They simulate inflation as a function of bond yield, change in bond yield, P/E10 and change in P/E10 with noise. They assume an annual portfolio management fee of 0.5%. They run 10,000 Monte Carlo simulations for each of many initial withdrawal rate scenarios, with probability of success defined as the percentage of runs not exhausting the portfolio before the end of a specified retirement period. Using initial conditions of a government bond yield of 2% and a P/E10 of 22 as of mid-April 2013, they find that: Keep Reading

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