Blog - Investing Notes

To determine whether the stock market is expensive or cheap, some experts use aggregate valuation ratios, either trailing or forward-looking, such as price-earnings ratio (P/E) and price-dividend ratio. Operating under a belief that such ratios are mean-reverting, most imminently due to movement of stock prices, these experts expect high (low) future stock market returns when the these ratios are low (high). Where are the ratios now? Using the S&P 500 Index close for 7/28/10 and actual and forecasted earnings and dividend data from Standard & Poor’s as of 7/21/10, we find that: More…

Many gurus express certitude in their forecasts for asset classes (markets) and specific assets and for their associated investment strategy recommendations. How can they be so sure? If they are not really sure, why would they say they are? In his July 2010 paper entitled “Policy Analysis with Incredible Certitude”, Charles Manski categorizes incredible analytical practices that underlie certitude. His context is public policy, but substitution of “investors” for “public” and “investment strategy” for “policy” seems apt. Based on past study of problems that limit credible prediction, he proposes that: More…

Does unreliability of the relationship between reported earnings and cash flow affect stock valuation? In the July 2010 version of their paper entitled “The Accrual Volatility Anomaly”, Sati Bandyopadhyay, Alan Huang and Tony Wirjanto investigate whether the market penalizes firms that consistently report earnings that are different from cash flows, as measured by accrual volatility. They define accrual volatility as the standard deviation of the ratio of accruals to sales over the past 16 quarters. They focus on total accruals, defined as changes in working capital minus deprecation and amortization.Using quarterly accounting data and monthly stock returns for a broad sample of U.S. stocks over the period 1976-2008, they find that: More…

Is participation in stock message boards/forums a net plus or net minus for the typical investor? In their July 2010 paper entitled “Confirmation Bias, Overconfidence, and Investment Performance: Evidence from Stock Message Boards”, JaeHong Park, Prabhudev Konana, Bin Gu, Alok Kumar and Rajagopal Raghunathan investigate how investors process message board information and analyze the impact of message processing on return expectations and investment performance. Using an incentivized online experiment conducted via the South Korean Naver.com message board to measure prior beliefs, information processing behavior and expected performance of 502 individual investors during October 7-21, 2009, they find that: More…

“Beat the Market with Hot-Anomaly Switching?” concludes that “a trader who periodically switches to the hottest known anomaly based on a rolling window of past performance may be able to beat the market. Anomalies appear to have their own kind of momentum.” Does momentum therefore work for style-based exchange-traded funds (ETF)? To investigate, we apply a simple momentum strategy to the following six ETFs that cut across capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

The simple (6-1) strategy allocates all funds each month to the one style ETF with the highest total return over the past six months. A six-month ranking period is intuitively large enough to gauge style momentum but small enough to react to changes in business conditions that might favor one style over others. Using monthly dividend-adjusted closing prices for the style ETFs and S&P Depository Receipts (SPY) over the period 8/01-6/10 (107 months, limited by data for IWS/IWP), we find that: More…

What factors drive profitability for trading price divergences and convergences of pairs of similar stocks based on high-frequency data? In their March 2010 paper entitled “High Frequency Equity Pairs Trading: Transaction Costs, Speed of Execution and Patterns in Returns”, David Bowen, Mark Hutchinson and Niall O’Sullivan examine the characteristics of a high-frequency pairs trading strategy that takes a long (short) position in the relatively underpriced (overpriced) stock of a normally tracking pair upon divergence in price and closes positions upon convergence. They use overlapping 264-hour formation periods to identify the top five and top 20 trading pairs, followed by 132-hour trading periods for those pairs triggered by price divergence of two or three historical standard deviations. Using 60-minute return intervals calculated from tick by tick trade data for a sample of FTSE 100 stocks over calendar year 2007, they find that: More…

The Trading Calendar presents full-year and monthly cumulative performance profiles for the overall stock market (S&P 500 Index) based on its average daily behavior since 1950. How much do the corresponding monthly behaviors of the various size and value/growth styles deviate from an overall equity market profile? To investigate, we consider the the following six exchange-traded funds (ETF) that cut across capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

Using monthly dividend-adjusted closing prices for the style ETFs and S&P Depository Receipts (SPY) over the period 8/01-6/10 (107 months, limited by data for IWS/IWP), we find that: More…

How exposed are hedge funds to “rogue” correlations, wherein returns of assets or asset classes that normally exhibit hedging cancellation instead exhibit hedge-killing reinforcement? In the June 2010 version of their paper entitled “‘When There Is No Place to Hide’: Correlation Risk and the Cross-Section of Hedge Fund Returns”, Andrea Buraschi, Robert Kosowski and Fabio Trojani investigate the exposure of hedge funds to correlation risk (risk of unexpected changes in the correlation between the returns of different assets or asset classes) and the implications of this risk for hedge fund returns. Using data for actual one-month-to-maturity  S&P 500 correlation swaps (based on daily implied versus realized correlation), individual S&P 500 stock and index put and call options and a broad sample of 8,710 individual hedge funds spanning in combination January 1996 through December 2008, they find that: More…

Do any of the major U.S. stock market sectors systematically lead or lag the overall market, perhaps because of some underlying business/economic cycle? To investigate, we examine the behaviors of the nine sectors defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR), all of which have trading data back to December 1998:

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

Using monthly adjusted closing prices for these exchange traded funds (ETF) and for the S&P 500 index mimicking SPY (to represent the overall U.S. stock market) over the period 12/98-6/10 (139 months), we find that: More…

The Trading Calendar presents full-year and monthly cumulative performance profiles for the overall stock market (S&P 500 Index) based on its average daily behavior since 1950. How much do the corresponding monthly behaviors of the various stock market sectors deviate from an overall market profile? To investigate, we consider the nine sectors defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR), all of which have trading data back to December 1998:

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

Using monthly adjusted closing prices for these exchange traded funds (ETF) since inception, along with contemporaneous data for Standard & Poor’s Depository Receipts (SPY) as a benchmark, for 12/98-6/10 (139 months), we find that: More…

Does media coverage of hedge funds indicate their values as investments? In their July 2010 paper entitled “Media and Investment Management”, Gideon Ozik and Ronnie Sadka investigate the level and investment implications of media bias by applying textual analysis to titles of articles from three types of news coverage about equity hedge funds (General newspapers, Specialized investment magazines, and Corporate communications). They frame their investigation by hypothesizing three aspects of bias: reporting style, editorial selection and content. Using the Google News archive to collect approximately 67,000 news articles from about 3,600 unique media sources on a sample of 774 long/short U.S. equity hedge funds over the period 1999-–2008, they find that: More…

Some investing experts cite consumer credit as a potentially important indicator of future stock market behavior, hypothesizing that an expansion (contraction) in credit indicates growing (shrinking) corporate sales, earnings and ultimately stock prices. Is there a reliable relationship between historical variation in consumer credit and stock market returns? The Federal Reserve collects and publishes U.S. consumer credit data on a monthly basis with a delay of about five weeks. Using monthly seasonally adjusted total U.S. consumer credit data for January 1950 through May 2010 and monthly S&P 500 Index closes for January 1950 through June 2010 (725-726 months), we find that: More…

The Inflation Forecast now incorporates actual total and core Consumer Price Index data for June 2010. The total (core) actual for June is  lower than (slightly lower than) forecasted.

Revised actual and forecasted inflation rates will flow into Real Earnings Yield Model projections at the end of July.

Given Warren Buffett’s long-term record of outperformance via Berkshire Hathaway, rational investors should consider following his lead as the the company discloses its holdings. Why would the market not immediately discount his moves as announced? In their July 2010 paper entitled “Overconfidence, Under-Reaction, and Warren Buffett’s Investments”, John Hughes, Jing Liu and Mingshan Zhang investigate how other experts/large traders contribute to market underreaction to Berkshire Hathaway’s moves. Using return, analyst recommendation, insider trading and institutional holdings data for publicly traded stocks listed in Berkshire Hathaway’s quarterly SEC Form 13F filings during 1980-2006 (2,140 quarter-stock observations), they find that: More…

Does investor interest in stocks as measured by Google Insights for Search predict which stocks will exhibit return continuation? In his June 2010 paper entitled “The Demand for Information”, Gordon Sims examines the effects of investor attention to stocks as defined by relative search frequency from Google Insights for Search (Stock Information Demand) to short-term stock momentum. The past return interval for momentum measurement is four weeks, augmented by a one-week delay in portfolio formation to avoid short-term reversal. Search term construction for Stock Information Demand focuses on intent to buy or sell a stock by appending “stock” or “quote” to a company’s name or ticker symbol. Using weekly returns for July 2003 through December 2009 for those S&P 500 stocks (as of July 31, 2003) with sufficient weekly Stock Information Demand data over the period 2004-2009 (214 stocks), he finds that: More…