Interaction Network Inference and Stock Market Modelling - Elsegai, Heba
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The inference of causal relationships between stock markets constitutes a major research topic in the field of financial time series analysis. A successful reconstruction of the underlying causality structure represents an important step towards the overall aim of improving stock market price forecasting. For the identification of causal relationships, the concept of Granger-causality is utilised. However, there are several challenges associated with applying this concept to real world problems. The first part of the book devoted to inferring the underlying network structure from incomplete…mehr

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Produktbeschreibung
The inference of causal relationships between stock markets constitutes a major research topic in the field of financial time series analysis. A successful reconstruction of the underlying causality structure represents an important step towards the overall aim of improving stock market price forecasting. For the identification of causal relationships, the concept of Granger-causality is utilised. However, there are several challenges associated with applying this concept to real world problems. The first part of the book devoted to inferring the underlying network structure from incomplete time series data. The second part of the book deals with data-based modelling with application to stock market time series data. The methods developed throughout this book are valuable tools to gain a deep understanding of causal relationships especially in stock markets. The numerous breakthrough results in this book are expected to be of aid for investors for improving the process of decision making in portfolio selections. This allows them to reduce the risk exposure associated with their investments.
Autorenporträt
Dr.Heba Elsegai, Ph.D. in Mathematics at University of Aberdeen, UK. She is now a lecturer of Mathematics and Applied Statistics, department of Applied Statistics, Faculty of Commerce, Mansoura University, Egypt. She is interested in working on inference of causal interaction networks and time series data analysis.