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Investing Research Articles

Hedge Funds Strongest Around the Turns of Odd Years?

Do hedge funds eliminate, or even reverse, seasonal effects in the returns of the stock market? In his September 2006 paper entitled “Seasonality in Hedge Fund Strategies”, Yan Olszewski investigates general seasonal effects for various hedge fund strategies. Using monthly excess return data during 1990-2005 for 30 of the 37 equally-weighted Hedge Fund Research strategy indexes encompassing over 1600 funds, he finds that: Keep Reading

Dynamics of Size and Value Investing

As companies evolve, their characteristics may migrate from one category to another (for example, from small to large, or from growth to value). Does such migration, in aggregate, help explain differences in average returns for different categories of stocks? In the August 2006 draft of their paper entitled “Migration”, Eugene Fama and Kenneth French investigate how migration of firms across categories contributes to the size effect and the value premium. Specifically, at the end of each June from 1926 through 2004 they construct six value-weighted portfolios of stocks from the major U.S. exchanges based on market capitalization and price-to-book ratio. They then examine the effects on portfolio returns of four kinds of annual rebalancing actions: (1) firms that do not move (Same); (2) firms that change size (dSize); (3) firms that improve toward growth, or are acquired (Plus); and, (4) firms that deteriorate toward value, or are delisted (Minus). Using subsequent-year return data for 1927-2005, they conclude that: Keep Reading

Synthetic Hedge Funds?

Is it possible to replicate the returns of hedge funds by decomposing these returns into contributions from easily tradable risk factors? In the August 2006 draft of their paper entitled “Can Hedge-Fund Returns Be Replicated?: The Linear Case”, Jasmina Hasanhodzic and Andrew Lo investigate simple models of the performance of 11 types of hedge funds based on six risk factors (related to stocks, bonds, currencies, commodities, credit, and volatility). They then use the results for the five factors that are easily tradable to create hedge fund “clones” (synthetic hedge funds) from exchange-traded assets and derivatives in two ways: (1) applying data from the full sample period (with the associated look-ahead bias); and, (2) applying data from a rolling historical 24-month period. Using data for the period February 1986 through September 2005 on 1,610 hedge funds still active (“live”) at the end of the period, they find that: Keep Reading

Which Financial Performance Measure Best Fits Stock Valuation?

Is cash flow or earnings a better indicator of stock valuation? In their August 2006 paper entitled “Cash Flow is King? Comparing Valuations Based on Cash Flow Versus Earnings Multiples”, Jing Liu, Doron Nissim and Jacob Thomas extend their prior work on comparing cash flow and earnings as indicators of firm market valuation. The authors assume that financial markets efficiently price stocks and compare the accuracies with which different simple valuation ratios predict stock prices. They hypothesize that: (1) earnings should outperform cash flows as predictors of valuation because earnings include information about both cash flow and accruals; and, (2) forecasts should outperform historical results as predictors of valuation because forecasts typically exclude non-recurring events. Using data from the prior study for the U.S. (1992-1999) and for a large sample of firms across ten international markets (1987-2004), they conclude that: Keep Reading

Risky Stocks + Short Sellers = Low Returns

Do short sellers avoid highly volatile stocks, and thereby leave them overvalued? If so, when short sellers do attack volatile stocks, is the level of overvaluation therefore compelling? In the August 2006 update of their paper entitled “Costly Arbitrage and Idiosyncratic Risk: Evidence from Short Sellers”, Ying Duan, Gang Hu and David McLean test the hypothesis that short sellers tend to avoid stocks with high idiosyncratic risk because of the high cost of hedging such risk. Using data for stock prices, short interest levels and other factors spanning 1988-2003, they find that: Keep Reading

Momentum Strategies Sputtering?

How are momentum stock trading strategies doing these days? In their January 2006 paper entitled “The Vanishing Abnormal Returns of Momentum Strategies and ‘Front-running’ Momentum Strategies”, Thomas Henker, Martin Martens and Robert Huynh examine the returns of various momentum trading strategies in general and during specific market conditions (rising or falling) over the period 1993-2004. They construct a series of self-financing portfolios (equal-weighted) for various holding periods by buying past winners and selling past losers based on various past performance (ranking) periods. Some strategies include a one-month gap between the ranking and holding periods. They repeat portfolio construction monthly over the sample period for each strategy, resulting in overlapping portfolios. Finally, they test “front-running” strategies that set momentum rankings five days before the ends of months rather than at month-ends. Using daily data to calculate monthly returns for a broad sample of stocks (with all distributions reinvested), they find that: Keep Reading

A Republican Risk Premium?

Is the media more likely to accentuate the negative when Republicans hold the Presidency? In their October 2004 paper entitled “Is Newspaper Coverage of Economic Events Politically Biased?”, John Lott Jr. and Kevin Hassett of the American Enterprise Institute test for political bias in the economic (durable goods, GDP, retail sales and unemployment) news coverage of American newspapers, after controlling for economic content. Using headlines from a database of newspaper and wire service articles from 389 newspapers covering January 1991 through May 2004 (and back to 1985 for the top ten newspapers: USA Today, Wall Street Journal, New York Times, Los Angeles Times, Washington Post, New York Daily News, New York Post, Chicago Tribune, Newsday and Houston Chronicle), they find that: Keep Reading

Financial News Sentiment Predicts Stock Returns?

Does exceptionally negative news coverage predict hard times for a company and its stock price? In their August 2006 paper entitled “More Than Words: Quantifying Language to Measure Firms’ Fundamentals”, Paul Tetlock, Maytal Saar-Tsechansky and Sofus Mackassy test whether they can predict a company’s future performance and stock returns by quantifying the sentiment in its financial news coverage. Their sentiment measure is a standardized level of negativity based on word counts and the Harvard psychosocial dictionary. Using Wall Street Journal (WSJ) and Dow Jones News Service (DJNS) stories about individual S&P 500 firms during 1980-2004 (350,000 significant articles), along with contemporaneous financial and stock price data, they find that: Keep Reading

Jim Cramer’s Mad, Mad, Mad, Mad Market?

Can traders exploit irrational reactions to Jim Cramer’s stock recommendations by viewers of CNBC’s Mad Money? In their March 2006 paper entitled “Is the Market Mad? Evidence from Mad Money, Joseph Engelberg, Caroline Sasseville and Jared Williams measure the market’s reaction to Mr. Cramer’s buy recommendations. Using a sample of 246 initial recommendations made by Jim Cramer on Mad Money episodes between July 28, 2005 and October 14, 2005, as recorded by YourMoneyWatch.com, they conclude that: Keep Reading

Classic Paper: Company Valuation Methods

We have selected for retrospective review a few all-time “best selling” research papers of the past few years from the General Financial Markets category of the Social Science Research Network (SSRN). Here we summarize the March 2004 update of the paper entitled “Company Valuation Methods: The Most Common Errors in Valuations” (download count over 6,000) by Pablo Fernandez. In this paper, the author describes the four most widely used company valuation methods: (1) balance sheet-based; (2) income statement-based; (3) goodwill-based; and, (4) cash flow discounting-based. He also illustrates a break-up value calculation and summarizes the valuation errors he has most commonly encountered. He states that: Keep Reading

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