Investor sentiment and statistical moments of the return distribution in the German stock market. A three stage empirical analysis.
type: CreativeWork
description: This dissertation contributes to an increasing body of literature through a holistic perspective in research and model development to investigate the relationships between investor sentiment and the lower- and higher-order statistics of the return distribution in the German stock market utilizing the market-wide CDAX stock index as an exemplary sample. Since empirical studies on investor sentiment are conducted mainly with USmarket-based data, comparatively few academic contributions are made to the German investor sentiment literature. Moreover, previous findings for other countries cannot necessarily be generalized to Germany, especially as Germany appears to be mainly influenced by global trends and investor sentiments owing to its high dependence on foreign trade. Consequently, the empirical evidence for Germany in this research domain, which includes both cross-sectional and longitudinal perspectives, is sparse. As various approaches exist to measure and assess the links between investor sentiment and capital market movements, a proprietarily defined investor sentiment categorization system is established, in which each investor sentiment indicator is assigned. This dissertation's underlying investor sentiment sample consists of all three categories of the dedicated categorization system for investor sentiment indicators and covers up to 20 years to 2021. With regard to the thesis structure, a comprehensive overview of the literature and current research on market efficiency and investor sentiment is initially elaborated before the applied methodology and the evaluation results are analyzed. Of particular note is the three-stage empirical analysis conducted in this thesis: First, a principal component analysis-based investor sentiment risk factor is established to improve model performance in traditional cross-sectional multifactor models as measured by the corrected coefficient of determination and additional metrics. Second, the application of Long Short-Term…
abstract: This dissertation contributes to an increasing body of literature through a holistic perspective in research and model development to investigate the relationships between investor sentiment and the lower- and higher-order statistics of the return distribution in the German stock market utilizing the market-wide CDAX stock index as an exemplary sample. Since empirical studies on investor sentiment are conducted mainly with USmarket-based data, comparatively few academic contributions are made to the German investor sentiment literature. Moreover, previous findings for other countries cannot necessarily be generalized to Germany, especially as Germany appears to be mainly influenced by global trends and investor sentiments owing to its high dependence on foreign trade. Consequently, the empirical evidence for Germany in this research domain, which includes both cross-sectional and longitudinal perspectives, is sparse. As various approaches exist to measure and assess the links between investor sentiment and capital market movements, a proprietarily defined investor sentiment categorization system is established, in which each investor sentiment indicator is assigned. This dissertation's underlying investor sentiment sample consists of all three categories of the dedicated categorization system for investor sentiment indicators and covers up to 20 years to 2021. With regard to the thesis structure, a comprehensive overview of the literature and current research on market efficiency and investor sentiment is initially elaborated before the applied methodology and the evaluation results are analyzed. Of particular note is the three-stage empirical analysis conducted in this thesis: First, a principal component analysis-based investor sentiment risk factor is established to improve model performance in traditional cross-sectional multifactor models as measured by the corrected coefficient of determination and additional metrics. Second, the application of Long Short-Term…
author: Hövel, Emile David
language: en
keywords: Distribución de la rentabilidad, Sentimiento de los inversores, Modelos multifactoriales, Análisis de componentes principales, Redes de gran memoria de corto plazo, Red neuronal recurrente artificial, Procesamiento de lenguajes naturales, Mercado de valores alemán
license: http://creativecommons.org/licenses/by-nc-nd/4.0/
date published: 2023
date modified: 2026-07-24T03:17:47Z
provider: Universidad Catolica de Murcia - Doctoral
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Universidad Catolica de Murcia - Doctoral

PropertyValue
property ID: Handle
value: http://hdl.handle.net/10952/6129