Stock data analysis for price forecasting and trend prediction has been a challenging problem that attracts researchers from different fields. Some use statistical methods, while others use neural network based approaches. This paper reports on a preliminary study on stock market data analysis using a hyperspace data mining approach that is built upon a projective geometrical method. Discussion...

This paper is mainly talking about several volatility models and its ability to predict and capture the distinctive characteristics of conditional variance about the empirical financial data. In my paper, I choose basic GARCH model and two important models of the GARCH family which are E-GARCH model and GJR-GARCH model to estimate. At the same time, in order to acquire the forecasting performan...

Stock prices as time series are, often, non-linear and non-stationary. This paper presents an ensemble forecasting model that integrates Empirical Mode Decomposition (EMD) and its variation Ensemble Empirical Mode Decomposition (EEMD) with Artificial Neural Network (ANN) for short-term forecasts of stock index. In first stage, the data is decomposed into a smaller set of Intrinsic Mode Function...

Our paper suggests a simple, recursive residuals (out-of-sample) graphical approach to evaluating the predictive power of popular equity premium and stock market timeseries forecasting regressions. When applied, we find that dividend ratios should have been known to have no predictive ability even prior to the 1990s, and that any seeming ability even then was driven by only two years, 1973 and ...

Generally, high oil prices slow economic growth, cause inflationary pressures and creates global imbalances. In addition, oil price volatility increase uncertainty and restrain the much-needed investment in the capital market. Thus, this paper applies the Augmented Dickey Fuller and Johansen Co-integration Tests in which the effect of oil price volatility, crude oil price and stock price is ana...

Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecastin...

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques and combining them to improve forecasting accuracy in different fields. Besides, stock market forecasting has always been a subject of interest for most investors and professional analysts. Stock market forecasting is a tough problem because of the uncertainties involved in the ...

-In the modern times of the information age, the magnitude of social media activity has reached unprecedented levels. Twitter is one such popular online social networking and micro-blogging service, which enables hundreds of millions of users share short messages in real time about events worth broad attention expressing public opinion. In this paper, we investigate the relationship between Twi...

Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing ...

This work examines recent publications in forecasting in various fields, these include: wind power forecasting; electricity load forecasting; crude oil price forecasting; gold price forecasting energy price forecasting etc. In this review, categorization of the processes involve in forecasting are divided into four major steps namely: input features selection; data pre-processing; forecast mode...