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Using the classification and regression tree (CART) model for stock selection on the S&P 700
Traditionally, investment practitioners and academics alike have used stock fundamentals and a linear framework in order to predict future stock performance. This approach has been shown to have flaws as literature has shown that stock returns can exhibit non-linearity and involve complex relations...
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| Format: | Thesis |
| Language: | English |
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Department of Finance and Tax
2016
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| Summary: | Traditionally, investment practitioners and academics alike have used stock fundamentals and a linear framework in order to predict future stock performance. This approach has been shown to have flaws as literature has shown that stock returns can exhibit non-linearity and involve complex relations beyond that of a linear nature (Hsieh, 1991; Sarantis, 2001; Shively, 2003). These findings present an opportunity to investment practitioners who are better able to model these returns. This dissertation attempts to classify stocks on the S&P 700 index using a Classification and Regression Tree (CART) built during an in-sample period and then used for predicative purposes during an out-of-sample period deliberately comprising both a period of financial crisis and recovery. For these periods, various portfolios and performance measures are calculated in order to assess the models performance relative to the benchmark, the Standard and Poor (S&P) 700 index. |
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