Developing forecasting models for estimating the behavior of capital markets is one of the most challenging tasks in financial decision support system research. Besides time series models, artificial neural network approaches and genetic algorithms, text mining technologies represent a promising approach to support financial decision-making. In this paper, the authors address the problem field ...

Motivated by the increasing integration among electricity markets, in this paper we propose three different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of t...

This paper summarizes what we have learned about price stability since the C.D. Howe Institute and Bank of Canada conferences of 1996 and 1997.1 The range of issues that a central bank must consider in its evolving approach to price stability, and that these two conferences covered, is broad. It includes setting targets for the behaviour of the price level, developing techniques for forecasting...

Abstract. In this paper we propose a new forecasting methodology that comprises simultaneous level-wise modeling in the wavelet domain. The WAW methodology (short for wavelet-armax-winters) uses three modeling startegies: ARMAX models capable of incorporating external inputs and model feedbacks, trigonometric regressions sensitive to seasonality effects and Holt-Winters models describing trends...

Currently, stock price forecasting is carried out using either time series prediction methods or trend classifiers. The trend classifiers are designed to predict the behaviour of stock price’s movement. Recently, soft computing methods, like support vector machines, have shown promising results in the realization of this particular problem. In this paper, we apply several prototype generation c...

Financial forecasting is a difficult task due to the intrinsic complexity of the financial system. A simplified approach in forecasting is given by “black box” methods like neural networks that assume little about the structure of the economy. In the present paper we relate our experience using neural nets as financial time series forecast method. In particular we show that a neural net able to...

The use of decision rules and estimation techniques is increasingly common for decision making. In recent years studies were conducted which applies Genetic Programming (GP) to obtain rules to make predictions. A new branch in the area of Evolutionary Algorithms (EA) is Linear Genetic Programming (LGP). LGP evolves instructions sequences of an imperative programming language. This paper propose...

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...

A combined wavelet transform (WT) and multiple linear regression (MLR) based technique to forecast price profile in a single settlement real time electricity market has been presented. The historical price and load data has been decomposed into better-behaved wavelet domain constitutive subseries using WT and then combined with other time domain variables to form the set of input variables for ...

Financial forecasting is a di cult task due to the intrinsic com plexity of the nancial system A simpli ed approach in forecasting is given by black box methods like neural networks that assume little about the structure of the economy In the present paper we relate our experience using neural nets as nancial time series forecast method In particular we show that a neural net able to forecast t...