## Abstract We examine the outโofโsample performance of monthly returns forecasts for the Dow Jones and the FT, using a linear and an artificial neural network (ANN) model. The comparison of outโofโsample forecasts is done on the basis of directional accuracy, using the Pesaran and Timmermann (1992
Comparing linear and nonlinear forecasts for stock returns
โ Scribed by Angelos Kanas; Andreas Yannopoulos
- Book ID
- 114344209
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 138 KB
- Volume
- 10
- Category
- Article
- ISSN
- 1059-0560
No coin nor oath required. For personal study only.
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## Abstract Following recent nonโlinear extensions of the presentโvalue model, this paper examines the outโofโsample forecast performance of two parametric and two nonโparametric nonlinear models of stock returns. The parametric models include the standard regime switching and the Markov regime swi
## ABSTRACT We studied the predictability of intraday stock market returns using both linear and nonlinear time series models. For the S&P 500 index we compared simple autoregressive and random walk linear models with a range of nonlinear models, including smooth transition, Markov switching, artif