The Hurst exponent (H) is a statistical measure used to classify time series. H=0.5 indicates a random series while H>0.5 indicates a trend reinforcing series. The larger the H value is, the stronger trend. In this paper we investigate the use of the Hurst exponent to classify series of financial data representing different periods of time. Experiments with backpropagation Neural Networks show that series with large Hurst exponent can be predicted more accurately than those series with H value close to 0.50. Thus Hurst exponent provides a measure Read the rest of this entry »
In this study, a novel forecasting model based on Wavelet Neural Network (WNN) is proposed to predict monthly crude oil spot prices. In this new model, OECD industrial petroleum inventory levels are used as the independent variable, and Wavelet Neural Network (WNN) is used to model the nonlinear relationship between inventories and prices. For verification purpose, West Texas Intermediate (WTI) crude oil spot price is used. Experiment results reveal that WNN can model the nonlinear relationship between inventories and price very well. Furthermore, Read the rest of this entry »
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 multidimensional wavelet kernel function and prove it meeting the mercer condition to address this problem. The applicability and validity Read the rest of this entry »
The main theme of this article is the mathematical theory needed to develop a computer application that helps to predict economic data, which contain an element of time. This is the case of stock markets, currency exchange rates, inflation rates, etc. In the second part of this paper an example application and its forecasting results are described. The application was used to predict Read the rest of this entry »
In this paper, we propose an effective clustering method, HRK (Hierarchical agglomerative and Recursive K-means clustering), to predict the short-term stock price movements after the release of financial reports. The proposed method consists of three phases. First, we convert each financial report into a feature vector and use the hierarchical agglomerative clustering method to divide the converted feature vectors into clusters. Second, for each cluster, we recursively apply the K-means clustering method to partition each cluster into sub-clusters so that most feature vectors in each subcluster belong to the same class. Read the rest of this entry »
The present paper introduces the Particle Swarm Optimization (PSO) technique to develop an efficient forecasting model for predicion of various stock indices. The connecting weights of the adaptive linear combiner based model are optimized by the PSO so that its mean square error (MSE) is minimized. The short and long term prediction performance of the model is evaluated with test data and the results obtained are compared with those obtained from the multilayer peceptron (MLP) based model. Read the rest of this entry »
