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Candlesticks? Fiddlesticks!

Does candlestick technical analysis (examining relationships among opening, high, low and closing prices over the past 1-3 days to identify continuation and reversal signals) generate abnormal returns? In their recent paper entitled “Market Timing with Candlestick Technical Analysis”, Ben Marshall, Martin Young and Lawrence Rose test the profitability of trading stocks included in the Dow Jones Industrial Average based on 28 different candlestick signals. They assume a ten-day holding period after trading at the close on the day after a signal appears. Using stock price data for 1/1/92-12/31/02, they conclude that: Keep Reading

How Finance Professors Invest

How does “the group that is arguably best qualified, finance professors, …assess the importance of valuation techniques, asset-pricing models, market anomalies, firm characteristics, corporate events, seasonal variables, and other information” when they invest for themselves? In their April 2007 paper entitled “What Really Matters When Buying and Selling Stocks?”, James Doran and Colby Wright seek to answer this question via an email-initiated electronic survey of 4,525 finance professors at accredited U.S. universities and colleges. Using data provided by 642 qualified respondents, all with Ph.D.’s, they conclude that: Keep Reading

The Value Premium Looking Forward

How much of a long-term total return advantage do investors perceive for high book-to-market (value) stocks over low book-to-market (growth) stocks? Is this perceived premium stable over time? In their April 2007 paper entitled “The Expected Value Premium”, Long Chen, Ralitsa Petkova and Lu Zhang measure investor expectations for the value premium based on economic fundamentals rather than noisy historical returns. They assume that dividend growth rate equals capital gain rate over long periods, and that the top (bottom) 20% represents a high (low) the book-to-market ratio. Using monthly data for the period 1945-2005, they find that: Keep Reading

A Survey of the Factor Landscape

Many equity market researchers assume conventional three-factor (market return, size, book-to-market) and four-factor (plus momentum) models as standards of comparison for discovery of new sources of abnormal returns. Are they the best standards? Could they be derivatives of more economically fundamental sources of differences among individual stock returns? In their March 2007 paper entitled “Too Many Factors! Do We Need Them All?”, Soosung Hwang and Chensheng Lu seek to identify the minimum number of economically fundamental factors needed to explain why different stocks generate different returns. They investigate 16 factors (12 firm characteristics and four macroeconomic measures) that others have found to explain such return differences. Their principal test is to measure returns from zero-cost portfolios that are long stocks with high (top third) values and short stocks with low (bottom third) values of evaluated factors. Using data for a large sample of non-financial stocks during 1963-2005 and contemporaneous macroeconomic data, they conclude that: Keep Reading

Conservatism Bias in Earnings Forecasts

Do earnings forecasts contain information that investors can exploit to generate abnormal stock returns, or does the market efficiently discount these forecasts? In the November 2006 version of their paper entitled “Forecasted Earnings per Share and the Cross Section of Expected Stock Returns”, Ling Cen, John Wei and Jie Zhang investigate whether stocks with high forecasted earnings per share (FEPS) substantially outperform those with low forecasts, after controlling for commonly used risk factors. Using data for a large sample of NYSE, AMEX and Nasdaq-listed common stocks for the period January 1983 through December 2005 (712,563 stock-month observations), they conclude that: Keep Reading

Investors as Social (Relative Wealth) Climbers

Are investors/traders motivated primarily by absolute wealth or relative wealth? Is outperforming peers a strong motivation? In the February 2007 draft of his paper entitled “Why Risk is Not Related to Return”, Eric Falkenstein examines evidence for and implications of relative wealth as the principal motivator of investors. Using a wide range of examples, he argues that: Keep Reading

Does Earnings Acceleration Mean Anything for Investors?

How does the second derivative (acceleration) of earnings relate to stock returns? In their March 2007 paper entitled “Does Earnings Acceleration Convey Information?”, Ying Cao, Linda Myers and Theodore Sougiannis investigate how the change in earnings growth rate (earnings acceleration) relates to stock returns. They examine separately conditions in which earnings growth rate and earnings acceleration have the same and opposite signs. Using a large sample of U.S. non-financial and non-utility firms over the period 1965 to 2002 (66,150 firm-year observations), they conclude that: Keep Reading

A 12-Month Cycle for Stock Returns?

Do stocks have annual rhythms beyond the January effect? In their March 2007 paper entitled “Common Patterns of Predictability in the Cross-Section of International Stock Returns”, Steven Heston and Ronnie Sadka investigate cyclic patterns of return predictability for stocks in Canada, Japan and twelve European countries (chosen based on the number of firms available for analysis). Using monthly returns over the period January 1985 through June 2006 (258 months), they conclude that: Keep Reading

The Size Effect in Up and Down Markets

Does the size effect, the tendency of small capitalization stocks to outperform, hold in both advancing and declining markets? In their March 2007 paper entitled “Stock Market Returns and Size Premium”, Jungshik Hur and Vivek Sharma explore how the size premium differs when the overall stock market is moving up and down. Using monthly return data for a sample of NYSE/AMEX stocks for July 1931 through December 2004 and NASDAQ stocks for June 1975 through December 2004, they conclude that: Keep Reading

Why Gurus Go to Extremes

Are stock market forecasters prone to hyperbole? Is there logic to predicting plunges and melt-ups at probabilities unjustified by rigorous empirical analysis? In their February 2007 paper entitled “Probability Elicitation, Scoring Rules, and Competition among Forecasters”, Kenneth Lichtendahl, Jr. and Robert Winkler apply game theory to model the behavior of forecasters who pit themselves not only against the data, but also against each other. In other words, they examine the logical behavior of a forecaster whose reward depends not only on own accuracy but also on the accuracies of competing forecasters. When forecasters compete, they conclude that: Keep Reading

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