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New Covariance-Based Feature Extraction Methods For Classification And Prediction Of High-Dimensional Data, Mopelola Adediwura Sofolahan
New Covariance-Based Feature Extraction Methods For Classification And Prediction Of High-Dimensional Data, Mopelola Adediwura Sofolahan
Open Access Dissertations
When analyzing high dimensional data sets, it is often necessary to implement feature extraction methods in order to capture relevant discriminating information useful for the purposes of classification and prediction. The relevant information can typically be represented in lower-dimensional feature spaces, and a widely used approach for this is the principal component analysis (PCA) method. PCA efficiently compresses information into lower dimensions; however, studies indicate that it is not optimal for feature extraction especially when dealing with classification problems. Furthermore, for high-dimensional data having limited observations, as is typically the case with remote sensing data and nonstationary data such as …
Investor Sentiment In The Stock Market, Bayram Veli Salur
Investor Sentiment In The Stock Market, Bayram Veli Salur
Open Access Theses
Classical finance theories neglect the impact of investor sentiment on stock returns. These theories assume that investors are rational and make decisions in a way that maximizes their wealth. However, a vast amount of research shows that investors' decisions are affected by their psychological biases and feelings. These findings suggest that investor sentiment may have an impact on stock returns. This hypothesis is the main motivation of this study. First, this study examines whether there is correlation among investor sentiment indicators, and whether sentiment indicators have an impact on stock returns in the US and other countries. Second, this study …