Equity style timing using support vector regressions
The disappointing performance of value and small cap strategies shows that style consistency may not provide the long-term benefits often assumed in the literature. In this study it is examined whether the short-term variation in the US size and value premium is predictable. Style-timing strategies are documented based on technical and (macro-) economic predictors using a recently developed artificial intelligence tool called Support Vector Regressions (SVR). SVR are known for their ability to tackle the standard problem of overfitting, especially in multivariate settings. The findings indicate that both premiums are predictable under fair levels of transaction costs and various forecasting horizons.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 16 (2006)
Issue (Month): 15 ()
|Contact details of provider:|| Web page: http://www.tandfonline.com/RAFE20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/RAFE20|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Lo, Andrew W & MacKinlay, A Craig, 1990.
"Data-Snooping Biases in Tests of Financial Asset Pricing Models,"
Review of Financial Studies,
Society for Financial Studies, vol. 3(3), pages 431-467.
- Andrew W. Lo & A. Craig MacKinlay, 1989. "Data-Snooping Biases in Tests of Financial Asset Pricing Models," NBER Working Papers 3001, National Bureau of Economic Research, Inc.
- Lo, Andrew W. (Andrew Wen-Chuan) & MacKinlay, Archie Craig, 1955-, 1989. "Data-snooping biases in tests of financial asset pricing models," Working papers 3020-89., Massachusetts Institute of Technology (MIT), Sloan School of Management.
- T. C. Mills & J. V. Jordanov, 2003. "The size effect and the random walk hypothesis: evidence from the London Stock Exchange using Markov Chains," Applied Financial Economics, Taylor & Francis Journals, vol. 13(11), pages 807-815.
- Bossaerts, Peter & Hillion, Pierre, 1999. "Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?," Review of Financial Studies, Society for Financial Studies, vol. 12(2), pages 405-428.
- Johnathan Mun & Richard Kish & Geraldo Vasconcellos, 2001. "The contrarian investment strategy: additional evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 11(6), pages 619-640.
- Said Elfakhani, 2000. "Short positions, size effect, and the liquidity hypothesis: implications for stock performance," Applied Financial Economics, Taylor & Francis Journals, vol. 10(1), pages 105-116.
- Pesaran, M Hashem & Timmermann, Allan, 1995. " Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
- Bauer, Rob & Derwall, Jeroen & Molenaar, Roderick, 2004. "The real-time predictability of the size and value premium in Japan," Pacific-Basin Finance Journal, Elsevier, vol. 12(5), pages 503-523, November.
- Ki-Yeol Kwon & Richard Kish, 2002. "Technical trading strategies and return predictability: NYSE," Applied Financial Economics, Taylor & Francis Journals, vol. 12(9), pages 639-653.
- Lucas, Andre & van Dijk, Ronald & Kloek, Teun, 2002. "Stock selection, style rotation, and risk," Journal of Empirical Finance, Elsevier, vol. 9(1), pages 1-34, January.
When requesting a correction, please mention this item's handle: RePEc:taf:apfiec:v:16:y:2006:i:15:p:1095-1111. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.