Nonlinearities, cyclical behaviour and predictability in stock markets: international evidence

By Nicholas Sarantis. 2001

191 citations recorded by the source corpus.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/70b79693d6f0a7195e550e532ea493b20bd94aa8

How researchers see this paper

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In Economics and Econometrics

1. J. De Gooijer
2. Rob J Hyndman
3. Frank H. Westerhoff
4. Werya Aghamiri
5. Stefan Reitz
6. Akbar Esfahanipour
7. Alexei Botchkarev
8. Cars H. Hommes

Citations in the corpus

• Which Lag Length Selection Criteria Should We Employ (2006, unknown): “These criteria especially the AIC have been popularly adopted in economic studies, see for examples the works of Sarantis (1999, 2001) and Baum et al. (2001), Baharumshah et al. (2002), Ng (2002) and Tang (2003) who employed the AIC, Sarno and Taylor (1998) who employed the AIC and SIC, Ahmed (200”
• EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction (2019, background): “In the category of statistical approaches, there are autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH) volatility (Franses & Ghijsels, 1999), and the10 smooth transition autoregressive model (STAR) (Sarantis, 2001), just to name a few”
• 25 Years of IIF Time Series Forecasting: A Selective Review (2005, methodology): “The class of (self-exciting) threshold AR (SETAR) models was introduced by Tong (1978). These models, which are piecewise linear models in their most basic form, have attracted some attention in the IJF. Clements & Smith (1997) compared a number of methods for obtaining multistep-ahead forecasts for”
• Nonlinearities and Cyclical Behavior: The Role of Chartists and Fundamentalists (2003, background): “Cyclical motion is also detected on stock markets (Sarantis 2001, Nam et al. 2001, 2002).”
• Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis (2010, methodology): “The statistics school includes autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH) volatility (Franses & Ghijsels, 1999), and smooth transition autoregressive (STAR) (Sarantis, 2001).”
• 25 years of time series forecasting (2006, background): “Sarantis (2001) found evidence that STAR-type models can improve upon linear AR and random walk models in forecasting stock prices at both short term and medium term horizons. Interestingly, the recent study by Bradley & Jansen (2004) seems to refute Sarantis’ conclusion.”