In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficient ...

In today’s world, using quantitative methods are very important for financial markets forecast, improvement of decisions and investments. In recent years, various time series forecasting methods have been proposed for financial markets forecasting. In each case, the accuracy of time series methods fundamental to make decision and hence the research for improving the effectiveness of forecasting...

Computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting models propos...

Our review highlights some of the key challenges in financial forecasting problems along with opportunities arising from the unique features of financial data. We analyze the diffi culty of establishing predictability in an environment with a low signal-to-noise ratio, persistent predictors, and instability in predictive relations arising from competitive pressures and investors’ learning. We d...

In this paper, we present the advantages of Maximum overlapping Discrete Wavelet Transform (MODWT) in improving the forecasting accuracy financial time series data. Amman stock market (ASE) in Jordan was selected as a tool to show the ability of MODWT in forecasting financial time series, using Banking sector. Experimentally, this article suggests a novel technique for forecasting the banking d...

EDDIE is a genetic-programming based system for channelling expert knowledge into forecasting. FGP-2 is an implementation of EDDIE for financial forecasting. The novelty of FGP-2 is that, as a forecasting tool, it provides the user with a handle for tuning the precision against the rate of missing opportunities. This allows the user to pick investment opportunities with greater confidence.

This paper presents a study of neural network forecasting construction system. Forecasting, especially financial forecasting has been attracting researchers and practitioners for many years. Many experiments suggest that neural networks can outperform conventional models in most cases. However, due to the tedious and time consuming of neural networks training, some of models reported in literat...

Financial forecasting is an example of a signal processing problem w hich is challenging due to Small sizes, high noise, nonstationarity, and non-linearity,but fast forecasting of stock market price is very important for strategic business planning.Present study is aimed to develop a comparative predictive model w ith Feedforward Multilayer Artif icial Neural Netw ork & Recurrent Time Delay Neu...

Potential financial planning tools may include technical models for forecasting expenses, revenues, and risk. The Volpe Center conducted a literature review to assess the state of the financial forecasting practice and potentially to expand the body of knowledge concerning the development and use of these tools. The results of the literature scan, presented in the table below, were narrowed fro...