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Investing Research Articles

A Warm Embrace or Cold Shoulder for Hot Hands?

Do sophisticated (wealthy) investors chase hedge fund returns? If so, should they? In their March 2006 paper entitled “Do Sophisticated Investors Believe in the Law of Small Numbers?”, Guillermo Baquero and Marno Verbeek investigate whether sophisticated hedge fund investors exhibit “hot hands” bias by overreacting to small samples of fund performance. They hypothesize that investors who believe that hedge fund performance is predominantly skill (luck) are prone to overestimate the likelihood of performance persistence (mean reversion) in small samples, leading to an overly trend-following (contrarian) investing style. Using quarterly performance and funds flow data for 752 hedge funds between 1994 and 2000, they conclude that: Keep Reading

The Turn-of-the-Month Effect

Do stock prices move systematically at the turn of calendar months? In the July 2006 draft of their paper entitled “Equity Returns at the Turn of the Month”, Wei Xu and John McConnell examine equity returns from the beginning the last trading day of one month through the first three trading days of the next month. Using daily returns for a broad range of stocks across exchanges over the period 1926-2005 (with focus on the segment 1987-2005), they conclude that: Keep Reading

Stock Picking in a “Fruit Fly Lab”

Does a natural selection metaphor apply to stock picking models? In other words, can competition among a large set of dynamic models to mimic historical stock performance data evolve the most fit models? In their May 2006 paper entitled “Stock Selection – An Innovative Application of Genetic Programming Methodology”, Ying Becker, Peng Fei and Anna Lester address these questions by applying genetic programming to stock picking. Genetic programming enables the testing of a wide range of stock performance indicators in linear, non-linear and non-obvious combinations. The authors choose the S&P 500, excluding financials and utilities, as their universe of stocks and define two distinct types of stock-picking model fitness: (1) risk-adjusted outperformance compared to a traditional stock-picking model; and, (2) highest possible return independent of risk. They construct for comparison a traditional stock return forecasting model based on a linear combination of four composite factors: valuation, quality, analyst expectations and price. They use monthly data (65 variables for each of about 350 stocks) over the period January 1990 through December 2005 to create environments for model development and out-of-sample testing. They show that: Keep Reading

A Short-term VIX Trading Strategy That Works?

Can you trade on the CBOE Volatility Index (VIX), the “investor fear gauge,” or not? If so, what should you trade and should your trades be short-term or long-term? In their September 2005 paper entitled “VIX Signaled Switching for Style-Differential and Size-Differential Short-term Stock Investing”, Dean Leistikow and Susana Yu test the usefulness of VIX level as a signal for short-term switching between: (1) value and growth stock indexes; and, (2) small-capitalization and large-capitalization stock indexes. They note that “…VIX can be viewed as a market-determined forecast of short-term market volatility that, by construction, has a constant one-month forecast horizon.” They determine signals according to whether VIX is high or low compared to its 75-day moving average. They examine index returns for 1 day and 5 days after a VIX signal. Using data for the VIX and for various Standard & Poor’s and Russell stock indexes from the early 1990s through 2004, they find that: Keep Reading

Another Test of Hedge Fund Returns

Do most hedge funds outperform broad market indexes? Do some types of hedge funds do better than others? In their May 2006 paper entitled “The Performance of Hedge Fund Strategies and the Asymmetry of Return Distributions”, Bill Ding and Hany Shawky examine returns for hedge funds in general. They also use four alternative models to investigate the performance distributions of several categories of equity hedge funds, comparing results with broad stock market indexes. Using monthly returns over the period 1990 (466 funds with $16 billion in assets) to 2003 (2,225 funds with $328 billion in assets), they find that: Keep Reading

Worldwide Equity Returns in the 21st Century

In his June 2006 article entitled “Investing in the 21st Century: With Occam’s Razor and Bogle’s Wit”, Javier Estrada evaluates the long-term forecasting abilities of two simple models over 10-year periods during 1973-2005. He then uses them to predict the returns for 12 country stock markets (Australia, Belgium, Canada, Denmark, France, Germany, Ireland, Japan, Netherlands, Switzerland, UK, USA) for 2006-2015. He finds that: Keep Reading

Testing the Indicators of Barchart.com

Barchart.com offers free short-term, intermediate-term and long-term technical assessments of stocks and exchange traded funds (ETF). Barchart.com, Inc. claims that their “market information is being used by millions of investors every month.” An obstacle to assessing the usefulness of their technical indicators is unavailability of historical data. To overcome this obstacle, we have recorded their average indicators for S&P 500 Depository Receipts (SPY) daily to assemble a statistically meaningful history for that ETF, which tracks the S&P 500 index. Whenever an indicator average is “Hold,” we assign a value of 0%. From the seven months of data collected, encompassing both market advances and declines, we conclude that: Keep Reading

Indicators of Persistent Fund Manager Outperformance

What makes some mutual fund managers better than others? A series of three recent papers triangulate on the answer to that question by investigating the importance of public and private information to fund managers. Using data for 1,700 equity mutual funds over the period 1993-2002, “Fund Manager Use of Public Information: New Evidence on Managerial Skills” by Marcin Kacperczyk and Amit Seru examines the responses of mutual fund managers to news (changes in public information). Using data for over 2,500 equity mutual funds over the period 1984-2003, “Unobserved Actions of Mutual Funds” by Marcin Kacperczyk, Clemens Sialm and Lu Zheng tests the impacts of unobserved (not immediately or precisely disclosed) mutual fund manager actions on fund returns. Using data for over 2,300 equity mutual funds over the period 1984-2003, “Industry Concentration and Mutual Fund Performance” by Marcin Kacperczyk, Clemens Sialm and Lu Zheng studies the relationship between industry concentration and fund returns. Respectively, these papers conclude that: Keep Reading

Hedge Fund Industry: Declining Performance and Increasing Risk?

Is the hedge fund industry an alpha-generating juggernaut? Does it even really offer a “hedge?” In their March 2006 paper entitled “Hedge Funds: Performance, Risk and Capital Formation”, William Fung, David Hsieh, Narayan Naik and Tarun Ramadorai investigate performance, risk and capital flows within the hedge fund industry over the past ten years. Using a comprehensive dataset of 1,603 Funds-of-Hedge-Funds (FoFs) covering the period 1995-2004, they find that: Keep Reading

Classic Research: Separating Cash Flow and Discount Rate Contributions to Stock Returns

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 August 2003 paper entitled “Bad Beta, Good Beta” (download count over 1,700) by John Campbell and Tuomo Vuolteenaho. In this research, the authors separate stock beta into two components, one reflecting news about cash flows and one reflecting news about discount rates. They apply this decomposition to explain the size effect and the value premium. They hypothesize that:

[Market] “value…may fall because investors receive bad news about future cash flows; but it may also fall because investors increase the discount rate…that they apply to these cash flows. In the first case, wealth decreases and investment opportunities are unchanged, while in the second case, wealth decreases but future investment opportunities improve. …[A]n investor may demand a higher premium to hold assets that covary with the market’s cash-flow news than to hold assets that covary with news about the market’s discount rates, for poor returns driven by increases in discount rates are partially compensated by improved prospects for future returns. …The required return on a stock is determined not by its overall beta with the market, but by its bad cash-flow beta and its good discount-rate beta. Of course, the good beta is good not in absolute terms, but in relation to the other type of beta.” [Underlining is ours.]

Using monthly returns from an early period (January 1929 through June 1963) and a modern period (July 1963 through December 2001) to test this idea, the authors conclude that: Keep Reading

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