Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

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Momentum Investing Strategy (Strategy Overview)

Allocations for July 2024 (Final)
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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.

Cash as a Valuation Indicator

Does the level of cash held by a company, appropriately normalized for its business characteristics, reliably indicate the prospects for its stock? If so, is a high or low level of cash better for the stock? In his January 2009 paper entitled “Excess Cash Holdings, Risk, and Stock Returns”, Mikhail Simutin investigates the relationship between excess corporate cash holdings and future stock returns. He defines “excess” via multi-factor regression to normalize for key firm characteristics. Using characteristics, fundamentals and stock return data for non-financial U.S. companies over the period 1960-2006, he concludes that: Keep Reading

Methods and Results for ValueInvestorsClub.com Members

How do professional value investors make investment decisions? Do they beat the market? In their January 2009 preliminary paper entitled “Fundamental Value Investors: Characteristics and Performance”, Wesley Gray and Andrew Kern examine the detailed investment decision process and aggregate performance of professional value investors who participate in ValueInvestorsClub.com, an “exclusive [and confidential] online investment club where top investors share their best ideas.” The founders of ValueInvestorsClub.com are Joel Greenblatt and John Petry of Gotham Capital. Using a sample of 2,912 investment recommendations by ValueInvestorsClub.com members during January 2000 through June 2008, along with associated firm fundamentals and stock return data, they conclude that: Keep Reading

S&P 500 Quarterly Aggregate Earnings Estimate Evolutions

Several readers have inquired or commented about the accuracy of  Standard and Poor’s quarterly S&P 500 earnings estimates. How accurate have they been? Since late 2005, we have tracked the evolving bottoms-up S&P 500 year-over-year quarterly operating earnings growth estimates for 2006-2009 at roughly biweekly intervals. During the early part of this period, we recorded the average of the publicly available Standard and Poor’s and Reuters earnings estimates (generally similar). During the latter part, we recorded only the Standard and Poor’s estimates. Using evolving earnings forecasts for 2006-2009, we find that: Keep Reading

“It’s the P/E, Stupid!”

Is there a relationship between investor risk-aversion, as indicated by the aggregate U.S. stock market price-earnings ratio (P/E), and level of public satisfaction with the performance of the President? In their December 2008 paper entitled “Speculating on Presidential Success: Exploring the Link between the Price-Earnings Ratio and Approval Ratings”, Tomasz Wisniewski, Geoffrey Lightfoot and Simon Lilley examine the relationship between aggregate stock market P/E and the surveyed level of public approval of the current President. Using quarterly P/E for the S&P Composite Stock Price Index derived from Robert Shiller’s long-run dataset and Gallup presidential approval survey data from the beginning of 1950 through the third quarter of 2007 (231 observations), they conclude that: Keep Reading

Kicking the Body of the Fed Model

As with many indicators, the Fed Model is presently so far out of multi-generational bounds that reversion seems hopeless. Is the body still warm, or ready for burial? Using the daily S&P 500 earnings yield (E/P) during 1/2/90-12/4/08, as calculated from the historical S&P 500 index and 12-month trailing Standard & Poor’s earnings data, and contemporaneous daily 10-year Treasury note (T-note) yields, we find that… Keep Reading

Long-term Market Timing Model Flyoff

Do long-term stock market timing models work? If so, which type works best? In their October 2005 paper entitled Timing is Everything: A Comparison and Evaluation of Market Timing Strategies, Chris Brooks, Apostolos Katsaris and Gita Persand investigate the profitability of several timing models over a very long sample of S&P 500 index returns. Specifically, they test the timing power of: (1) the ratio of the long-term Treasury bond yield to the stock dividend yield; (2) the spreads between the stock earnings yield and the yields on either the three-month Treasury bills (T-bills) or the 10-year Treasury notes (T-notes); (3) a model for predicting when bear markets will occur based on the spread between T-note and T-bill yields; and, (4) an approach for predicting market turning points based on speculative bubbles. Timing signals trigger binary switching between stocks and T-bills. Using monthly stock return and model parameter data from January 1871-December 1926 for initial model calibration and January 1927-August 2003 for model testing and recalibration (a total of 1,592 months), they find that: Keep Reading

Darlings of the Dow Strategy

A reader asked:

“Have you tested the Darlings of the Dow strategy developed by Larry Williams? He has modified his original strategy several times, and I wonder whether he made revisions because of new insight or because the original strategy proved not much better than the five cheapest Dogs of the Dow. What I find interesting is his timing of the Darlings with Sy Harding’s MACD timing method and his buying the Dow Jones Utilities for the remainder of the year.”

The original Darlings of the Dow strategy employs fundamentals to select the five most undervalued stocks in the Dow Jones Industrials Average and times entries and exits seasonally (enter in October and exit in April). The revised version chooses other entry and exit dates. To evaluate the strategy, we assume that the trading dates/returns for Darlings of the Dow stocks are as listed by Larry Williams and that returns while out of the Darlings are the adjusted returns for the iShares Dow Jones US Utilities (IDU). As benchmarks, we calculate returns based on adjusted closing values for S&P Depository Receipts (SPY) over the same intervals and average 90-day Treasury bill (T-bill) yields as an alternative to IDU returns. We use a test period of 2002-2007 (10/28/02-9/13/07) that is out-of-sample and post-publication with respect to the original strategy. We find that: Keep Reading

Mispricing Versus Liquidity for Earnings Uncertainty

Does the market efficiently bound the mispricings of stocks within the costs of exploiting the mispricings? In their August 2008 paper entitled “Mispricing and Costly Arbitrage”, Ronnie Sadka and Anna Scherbina explore the difficulty of exploiting short-term mispricings of stocks derived from analyst disagreement about future earnings (with mispricing likely due to very pessimistic analysts withholding their views). Using stock price and earnings forecast data for a broad sample of stocks over the period January 1983 through August 2001, they conclude that: Keep Reading

Perspectives on Earnings Growth Forecasts

How should investors view corporate earnings estimates as determinants of stock valuations? Are analyst and management forecasts of any value? Is high growth inherently unsustainable? Is the source of growth important? In his June 2008 paper entitled “Growth and Value: Past Growth, Predicted Growth and Fundamental Growth”, Aswath Damodaran examines the patterns and broad lessons of research on growth forecasts. Using results from past studies and new analyses of earnings data for 1997-2007, he concludes that: Keep Reading

An International Test of Common Stock Return Indicators

Do any indicators systematically predict stock returns across global equity markets? In his June 2008 paper entitled “Predicting Global Stock Returns”, Erik Hjalmarsson tests the power of four common indicators (dividend-price ratio, earnings-price ratio, short interest rate and term spread) to predict stock returns for markets in 24 developed and 16 emerging economies. Using a very large dataset encompassing 20,000 monthly observations of returns and indicators ranging as far back as 1836, he concludes that: Keep Reading

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