The profitability of daily stock market indices trades based on neural network predictions: case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965-1999
A variety of new and powerful time series tools are available to test for predictive components in data which previously have been regarded as weak form efficient. The key issue is whether these new tools support profitable trading. A method is introduced based on univariate neural networks using untransformed data inputs to provide short-term predictions of the stock market indices returns. The profitability of trading signals generated from the out-of-sample short-term predictions for daily returns of S&P 500, DAX, TOPIX and FTSE stock market indices is evaluated over the period 1965-1999. The results provide strong evidence of high and consistent predictability contrasting the previous finding of weak form efficiency for index series and is notable because two of the series (S&P 500 and DAX) are confirmed as random using conventional tests. The out-of-sample prediction performance of neural networks is evaluated using RMSE, NMSE, MAE and sign and direction change statistics against a benchmark linear autoregressive model. Significant information advantage is confirmed by the Pesaran-Timmermann test. Finally, it is shown that buy and sell signals derived from neural network predictions are significantly different from unconditional one-day mean return and are likely to provide significant net profits for plausible decision rules and transaction cost assumptions.
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): 14 (2004)
Issue (Month): 4 ()
|Contact details of provider:|| Web page: http://www.tandfonline.com/RAFE20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/RAFE20|
When requesting a correction, please mention this item's handle: RePEc:taf:apfiec:v:14:y:2004:i:4:p:285-297. 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: (Chris Longhurst)
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.