O'Shaughnessy: Growth Screen
|O'Shaughnessy: Growth||S&P 500|
|Five Year Return:||14%||11.9%|
|Ten Year Return:||7.9%||5.6%|
One of the longest-running debates among stockpickers concerns whether selection criteria should focus on growth characteristics or value characteristics. Chasing hot growth stocks often leaves investors burned, while low-priced, "value" stocks often only become cheaper. Should you buy stocks for their superior growth potential, or because they are undervalued? Several years ago James O'Shaughnessy, president of the investment advisory firm O'Shaughnessy Capital Management, set out to try to settle the argument "once-and-for-all" by testing a series of growth and value screens on a sufficiently large database of stocks covering a long time period. His goal also was to determine which screens within each strategy produced the best returns.
The results of his analyses were first published in 1996 in his book, "What Works on Wall Street: A Guide to the Best-Performing Investment Strategies of All Time" (McGraw-Hill, $29.95). James O'Shaughnessy published a revised edition of the highly popular book in 1997, adding a more detailed breakdown of the performance of the strategies examined, providing data for an additional two years of testing and, most importantly, changing the criteria of one of his strategies. He has also started a number of mutual funds based on his approach.
In his book, O'Shaughnessy argues that the majority of investors fail to beat market averages because they do not follow a disciplined approach to investing. Instead, investors let the emotions surrounding the market overpower their judgment and push them off their planned investment course.
In this stock screen we will examine O'Shaughnessy's research and conclusions covering both single criteria testing as well as the cornerstone growth and value strategies. We will develop screens that follow the cornerstone value and growth strategies in light of the revisions he implemented and provide a look at the performance of these strategies during real-life testing.
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