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Stock market forecasting has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. ANN approaches have, however, suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning...
Stock index prediction seems to be a challenging task of the financial time series prediction process especially in emerging markets with their complex and inefficient structures. Multivariate adaptive regression splines (MARS) is a nonlinear and non-parametric regression methodology and has been successfully used in classification tasks. However, there are few applications using MARS in stock index...
The traditional BS evaluation model for options assumes volatility as a constant, and is unable to explain phenomena such as leptokurtic distribution and volatility clusters. In order to supplement this shortcoming, scholars have begun to use linear and non-linear GARCH models to estimate volatility. However, a consistent result has not been achieved with empirical analysis of various different volatility...
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