This paper presents a novel trend-based segmentation method TBSM and the support vector regression SVR for financial time series forecasting. The model is named as TBSM-SVR. Over the last decade, SVR has been a popular forecasting model for nonlinear time series problem. The general segmentation method, that is, the piecewise linear representation PLR , has been applied to locate a set of tradi...

Lettau and Ludvigson (2001) find that the consumption-wealth ratio (cay) constructed from revised data is a strong predictor of stock market returns. This paper shows that its out-ofsample forecasting power becomes substantially weaker if cay is estimated using information available at the time of forecast. The difference, which mainly reflects periodic revisions in consumption and labor income...

This paper introduces a method based on various linear and nonlinear state space models that are used to extract global stochastic financial trends (GST) out of non-synchronous financial data. More specifically, these models are constructed to take advantage of the intraday arrival of closing information coming from different international markets to improve volatility description and forecasti...

This paper presents a GARCH type volatility model with a time-varying unconditional volatility which is a function of macroeconomic information. It is an extension of the SPLINE GARCH model proposed by Engle and Rangel (2005). The advantage of the model proposed in this paper is that the macroeconomic information available (and/or forecasts) is used in the parameter estimation process. Based on...

Support vector machine (SVM) is a very speci1c type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of 1nancial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability o...

Volatility models and their forecasts are of interest to many types of economic agents, especially for financial risk management. Since 1982 when Engle proposed the Autoregressive Conditionally Heteroscedastic (ARCH) model, there have emerged numerous models for forecasting volatility. Given the vast number of models available, agents must decide which one to use. This paper explores a number o...

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Neural networks are an artificial intelligence method for modeling complex target functions. For certain types of problems, such as learning to interpret complex real-world sensor data, artificial neural networks (ANNs) are among the most effective learning methods. During the last decade, they have been widely applied to the domain of financial time series prediction, and their importance in t...

Volatility models and their forecasts are of interest to many types of economic agents, especially for financial risk management. Since 1982 when Engle proposed the Autoregressive Conditionally Heteroscedastic (ARCH) model, there have emerged numerous models for forecasting volatility. Given the vast number of models available, agents must decide which one to use. This paper explores a number o...

Statistical models have been widely used for the purpose of forecasting. However, it has some limitations regarding its performance, which prevents an automatic forecasting system development. In order to overcome such limitations, Artificial Neural Networks (ANNs), Evolutionary Algorithms (EAs) and Fuzzy Systems (FSs) based approaches have been proposed for nonlinear time series modeling. Howe...