Using machine learning algorithms to find patterns in stock prices
Pedro N. Rodríguez and
Simon Sosvilla-Rivero
No 2006-12, Working Papers from FEDEA
Abstract:
We use a machine learning algorithm called Adaboost to find direction-of-change patterns for the S&P 500 index using daily prices from 1962 to 2004. The patterns are able to identify periods to take long and short positions in the index. This result, however, can largely be explained by first-order serial correlation in stock index returns.
Date: 2006-06
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://documentos.fedea.net/pubs/dt/2006/dt-2006-12.pdf (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:fda:fdaddt:2006-12
Access Statistics for this paper
More papers in Working Papers from FEDEA
Bibliographic data for series maintained by Carmen Arias ().