Comparing linear and nonlinear forecasts for stock returns

By A. Kanas, A. Yannopoulos. 2001

78 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. Melike Bildirici
2. Kimon P. Valavanis
3. Elçin A. Alp
4. Yosi Keller
5. George S. Atsalakis
6. Ö. Ersin
7. A. Navon

In Management Science and Operations Research

1. George S. Atsalakis
2. Kimon P. Valavanis
3. Elçin A. Alp
4. Yosi Keller
5. Ö. Ersin
6. Melike Bildirici
7. A. Navon
8. Tugrul U. Daim

Citations in the corpus

• Financial Time Series Prediction Using Deep Learning (2017, background): “Kanas [10] showed that the non-linearity of the models being used for time series forecasting is of major importance.”
• Surveying stock market forecasting techniques - Part II: Soft computing methods (2009, methodology): “…Chaturvedi and Chandra (2004) who uses only 40 observations, Koulouriotis et al. (2005) who uses 2000 observations, Thawornwong and Enke (2004) who uses 24 years of data, Kanas and Yannopoulos (2001) who uses 21 years of data and Atsalakis and Valavanis (2006a) who uses 18 years of daily data.”
• TAR-cointegration neural network model: An empirical analysis of exchange rates and stock returns (2010, background): “Further, Black and McMillan (2004), Jasic and Wood (2004), Kanas (2001), Kanas and Yannopoulos (2001), Shively (2003) applied regarding the stock prices; whereas, Dunis and Huang (2002) and Hamid and Iqbal (2004) provide insightful applications regarding the stock volatility.”
• Referential kNN Regression for Financial Time Series Forecasting (2013, background): “Actually, recent scholars have revealed that the nonparametric non-linear models tend to outperform linear models in financial time series forecasting [2].”