Selected technical indicators and their formulas (Type 1).

<p>Selected technical indicators and their formulas (Type 1).</p

Comparison of the hit ratio between the two types of input variables.

<p>Comparison of the hit ratio between the two types of input variables.</p

Selected technical indicators and their formulas (Type 2).

<p>Selected technical indicators and their formulas (Type 2).</p

Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model

<div><p>In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.</p></div

Comparison of our study with prior research reports.

<p>Comparison of our study with prior research reports.</p

Process flow of the hybrid GA and BP algorithm.

<p>Process flow of the hybrid GA and BP algorithm.</p

Description of parameters that are used in the hybrid model.

<p>Description of parameters that are used in the hybrid model.</p

The architecture of the back propagation neural network.

<p>The architecture of the back propagation neural network.</p

Plots showing the daily Nikkei 225 closing prices from January 23, 2007 to December 30, 2013.

<p>Plots showing the daily Nikkei 225 closing prices from January 23, 2007 to December 30, 2013.</p

Distributed Statistical Process Monitoring Based on Four-Subspace Construction and Bayesian Inference

Multivariate statistical process monitoring (MSPM) can conduct dimensionality reduction on process variables and can obtain low-dimensional representations that capture most of the information in the original data space. However, most MSPM models are developed under unsupervised situations. Therefore, any abandoned information may deteriorate the process monitoring performance. To address both issues (i.e., dimension reduction and information preservation), this paper proposes a distributed statistical process monitoring scheme. The proposed method employs principal component analysis to derive four distinct and explicable subspaces from the original process variables according to their relevance or irrelevance to principal component subspace and residual subspace. Each subspace serves as a low-dimensional representation of the original data space, thereby preserving the information of the original data space without undergoing information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, was calculated directly in each subspace for fault detection. The Bayesian inference was then introduced as the decision fusion strategy to obtain a final and unique probability index. The feasibility and superiority of the proposed method was investigated by conducting a case study of the well-known Tennessee Eastman process