Using Economic and Financial Information for Stock Selection
AbstractA major inconvenience of the traditional approach in portfolio choice, based upon historical information, is its inability to anticipate sudden changes of price tendencies. Introducing information about future behavior of the assets fundamentals may help to make more appropriate choices. However the specification and parameterization of a model linking this exogenous information to the asset prices is not straightforward. Classification trees can be used to construct partitions of assets of forecasted similar behavior. We analyze the performance of this approach and apply it to different sectors of the S&P500.
Download InfoIf 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.
Bibliographic InfoPaper provided by Swiss Finance Institute in its series Swiss Finance Institute Research Paper Series with number 06-21.
Length: 20 pages
Date of creation:
Date of revision:
Portfolio optimization; Decision trees; Factor models;
Other versions of this item:
- I. Roko & M. Gilli, 2008. "Using economic and financial information for stock selection," Computational Management Science, Springer, vol. 5(4), pages 317-335, October.
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
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.:
- Lehmann, Bruce N, 1990. "Fads, Martingales, and Market Efficiency," The Quarterly Journal of Economics, MIT Press, vol. 105(1), pages 1-28, February.
- Fama, Eugene F & French, Kenneth R, 1992. " The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-65, June.
- Chan, Louis K C & Jegadeesh, Narasimhan & Lakonishok, Josef, 1996. " Momentum Strategies," Journal of Finance, American Finance Association, vol. 51(5), pages 1681-1713, December.
- Bruce N. Lehmann, 1988. "Fads, Martingales, and Market Efficiency," NBER Working Papers 2533, National Bureau of Economic Research, Inc.
- Marina Velikova & Hennie Daniels, 2004. "Decision trees for monotone price models," Computational Management Science, Springer, vol. 1(3), pages 231-244, October.
- Narasimhan Jegadeesh, 2001. "Profitability of Momentum Strategies: An Evaluation of Alternative Explanations," Journal of Finance, American Finance Association, vol. 56(2), pages 699-720, 04.
- Björn Fastrich & Peter Winker, 2012.
"Robust portfolio optimization with a hybrid heuristic algorithm,"
Computational Management Science,
Springer, vol. 9(1), pages 63-88, February.
- Björn Fastrich & Peter Winker, 2010. "Robust Portfolio Optimization with a Hybrid Heuristic Algorithm," Working Papers 041, COMISEF.
- Peter Winker & Marianna Lyra & Chris Sharpe, 2011. "Least median of squares estimation by optimization heuristics with an application to the CAPM and a multi-factor model," Computational Management Science, Springer, vol. 8(1), pages 103-123, April.
- Piotr Arendarski, 2012. "Tactical allocation in falling stocks: Combining momentum and solvency ratio signals," Working Papers 2012-01, Faculty of Economic Sciences, University of Warsaw.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marilyn Barja).
If references are entirely missing, you can add them using this form.