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...

One of the challenging problems in forecasting the conditional volatility of stock market returns is that general kernel functions in support vector machine (SVM) cannot capture the cluster feature of volatility accurately. While wavelet function yields features that describe of the volatility time series both at various locations and at varying time granularities, so this paper construct a mul...

This paper describes a heuristic forecasting model based on neural networks for stock decision-making. Some heuristic strategies are presented for enhancing the learning capability of neural networks and obtaining better trading performance. The China Shanghai Composite Index is used as case study. The forecasting model can forecast the buying and selling signs according to the result of neural...

Internet-based virtual futures markets (VFMs) have been used in predicting election results and movie ticket sales. We construct an Internet-based VFM to predict an underlying stock price. Results of Granger causality tests and tests of directional accuracy show that a VFM with only a small number of participants (75) is able to generate informative futures prices useful in the prediction of th...

Recruitment prediction is a key element for management decisions in many fisheries. A new approach using neural network is developed as a tool to produce a formula for forecasting fish stock recruitment. In order to deal with the local minimum problem in training neural network with back-propagation algorithm and to enhance forecasting precision, neural network’s weights are adjusted by optimiz...

We have introduced an early warning system for volatility regimes regarding Tehran Stock Exchange using Markov Switching GARCH approach. We have examined whether Tehran Stock Market has calmed down or more specifically, whether the surge in volatility during 2007-2010 global financial crises still affects stock return volatility in Iran.  Doing so, we have used a regime switching GARCH model.  ...

There has been an increasing attention on the influences online financial information has on the financial markets. In the meanwhile, the volatility of trading volumes, just as the volatility of stock returns, has an inseparable association with financial risks. It has been considered that there might exist some direct or indirect correlations between online financial information volumes and fi...

In this study, we explored data from StockTwits, a microblogging platform exclusively dedicated to the stock market. We produced several indicators and analyzed their value when predicting three market variables: returns, volatility and trading volume. For six major stocks, we measured posting volume and sentiment indicators. We advance on the previous studies on this subject by considering a l...

To forecast a complex and non-linear system, such as a stock market, advanced artificial intelligence algorithms, like neural networks (NNs) and genetic algorithms (GAs) have been proposed as new approaches. However, for the average stock investor, two major disadvantages are argued against these advanced algorithms: (1) the rules generated by NNs and GAs are difficult to apply in investment de...

State-space or latent-variable models for stock prices specify a process for expected returns and expected and unexpected dividend growth, and then derive dividend yields and returns from a present value relations. They are a useful structure for understanding and interpreting forecasting relations. In this note, I connect state-space representations with their observable counterparts, and VAR/...