Chuan Shi – författare
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15 produkter
15 produkter
Häftad, Engelska, 2024
527 kr
Skickas inom 10-15 vardagar
Bridging the gap between theoretical asset pricing and industry practices in factors and factor investing, Zhang et al. provides a comprehensive treatment of factors, along with industry insights on practical factor development.Chapters cover a wide array of topics, including the foundations of quantamentals, the intricacies of market beta, the significance of statistical moments, the principles of technical analysis, and the impact of market microstructure and liquidity on trading. Furthermore, it delves into the complexities of tail risk and behavioral finance, revealing how psychological factors affect market dynamics. The discussion extends to the sophisticated use of option trading data for predictive insights and the critical differentiation between outcome uncertainty and distribution uncertainty in financial decision-making. A standout feature of the book is its examination of machine learning's role in factor investing, detailing how it transforms data preprocessing, factor discovery, and model construction. Overall, this book provides a holistic view of contemporary financial markets, highlighting the challenges and opportunities in harnessing alternative data and machine learning to develop robust investment strategies.This book would appeal to investment management professionals and trainees. It will also be of use to graduate and upper undergraduate students in quantitative finance, factor investing, asset management and/or trading.
Inbunden, Engelska, 2024
2 423 kr
Skickas inom 10-15 vardagar
Bridging the gap between theoretical asset pricing and industry practices in factors and factor investing, Zhang et al. provides a comprehensive treatment of factors, along with industry insights on practical factor development.Chapters cover a wide array of topics, including the foundations of quantamentals, the intricacies of market beta, the significance of statistical moments, the principles of technical analysis, and the impact of market microstructure and liquidity on trading. Furthermore, it delves into the complexities of tail risk and behavioral finance, revealing how psychological factors affect market dynamics. The discussion extends to the sophisticated use of option trading data for predictive insights and the critical differentiation between outcome uncertainty and distribution uncertainty in financial decision-making. A standout feature of the book is its examination of machine learning's role in factor investing, detailing how it transforms data preprocessing, factor discovery, and model construction. Overall, this book provides a holistic view of contemporary financial markets, highlighting the challenges and opportunities in harnessing alternative data and machine learning to develop robust investment strategies.This book would appeal to investment management professionals and trainees. It will also be of use to graduate and upper undergraduate students in quantitative finance, factor investing, asset management and/or trading.
E-bok
PDF, Engelska, 2024622 kr
Läs direkt efter köp
Bridging the gap between theoretical asset pricing and industry practices in factors and factor investing, Zhang et al. provides a comprehensive treatment of factors, along with industry insights on practical factor development.
Chapters cover a wide array of topics, including the foundations of quantamentals, the intricacies of market beta, the significance of statistical moments, the principles of technical analysis, and the impact of market microstructure and liquidity on trading. Furthermore, it delves into the complexities of tail risk and behavioral finance, revealing how psychological factors affect market dynamics. The discussion extends to the sophisticated use of option trading data for predictive insights and the critical differentiation between outcome uncertainty and distribution uncertainty in financial decision-making. A standout feature of the book is its examination of machine learning''s role in factor investing, detailing how it transforms data preprocessing, factor discovery, and model construction. Overall, this book provides a holistic view of contemporary financial markets, highlighting the challenges and opportunities in harnessing alternative data and machine learning to develop robust investment strategies.
This book would appeal to investment management professionals and trainees. It will also be of use to graduate and upper undergraduate students in quantitative finance, factor investing, asset management and/or trading.
E-bok
Engelska, 2024622 kr
Läs direkt efter köp
Bridging the gap between theoretical asset pricing and industry practices in factors and factor investing, Zhang et al. provides a comprehensive treatment of factors, along with industry insights on practical factor development.
Chapters cover a wide array of topics, including the foundations of quantamentals, the intricacies of market beta, the significance of statistical moments, the principles of technical analysis, and the impact of market microstructure and liquidity on trading. Furthermore, it delves into the complexities of tail risk and behavioral finance, revealing how psychological factors affect market dynamics. The discussion extends to the sophisticated use of option trading data for predictive insights and the critical differentiation between outcome uncertainty and distribution uncertainty in financial decision-making. A standout feature of the book is its examination of machine learning''s role in factor investing, detailing how it transforms data preprocessing, factor discovery, and model construction. Overall, this book provides a holistic view of contemporary financial markets, highlighting the challenges and opportunities in harnessing alternative data and machine learning to develop robust investment strategies.
This book would appeal to investment management professionals and trainees. It will also be of use to graduate and upper undergraduate students in quantitative finance, factor investing, asset management and/or trading.
Häftad, Engelska, 2021
651 kr
Skickas inom 10-15 vardagar
heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.
E-bok
PDF, Engelska, 2022870 kr
Läs direkt efter köp
heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.
Inbunden, Engelska, 2022
651 kr
Skickas inom 10-15 vardagar
This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.
E-bok
Engelska, 2022840 kr
Läs direkt efter köp
This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.
Häftad, Engelska, 2023
651 kr
Skickas inom 10-15 vardagar
This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.
Inbunden, Engelska, 2017
1 570 kr
Skickas inom 10-15 vardagar
This book offers researchers an understanding of the fundamental issues and a good starting point to work on this rapidly expanding field. It provides a comprehensive survey of current developments of heterogeneous information network. It also presents the newest research in applications of heterogeneous information networks to similarity search, ranking, clustering, recommendation. This information will help researchers to understand how to analyze networked data with heterogeneous information networks. Common data mining tasks are explored, including similarity search, ranking, and recommendation. The book illustrates some prototypes which analyze networked data.Professionals and academics working in data analytics, networks, machine learning, and data mining will find this content valuable. It is also suitable for advanced-level students in computer science who are interested in networking orpattern recognition.
E-bok
Engelska, 20171 825 kr
Läs direkt efter köp
This book offers researchers an understanding of the fundamental issues and a good starting point to work on this rapidly expanding field. It provides a comprehensive survey of current developments of heterogeneous information network. It also presents the newest research in applications of heterogeneous information networks to similarity search, ranking, clustering, recommendation. This information will help researchers to understand how to analyze networked data with heterogeneous information networks. Common data mining tasks are explored, including similarity search, ranking, and recommendation. The book illustrates some prototypes which analyze networked data.Professionals and academics working in data analytics, networks, machine learning, and data mining will find this content valuable. It is also suitable for advanced-level students in computer science who are interested in networking orpattern recognition.
Häftad, Engelska, 2018
1 570 kr
Skickas inom 10-15 vardagar
This book offers researchers an understanding of the fundamental issues and a good starting point to work on this rapidly expanding field. It provides a comprehensive survey of current developments of heterogeneous information network. It also presents the newest research in applications of heterogeneous information networks to similarity search, ranking, clustering, recommendation. This information will help researchers to understand how to analyze networked data with heterogeneous information networks. Common data mining tasks are explored, including similarity search, ranking, and recommendation. The book illustrates some prototypes which analyze networked data.Professionals and academics working in data analytics, networks, machine learning, and data mining will find this content valuable. It is also suitable for advanced-level students in computer science who are interested in networking orpattern recognition.
Inbunden, Engelska, 2022
1 793 kr
Skickas inom 10-15 vardagar
Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node.In this book, we provide a comprehensive survey of current developments in HG representation learning.More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.
E-bok
Engelska, 20222 194 kr
Läs direkt efter köp
Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node.In this book, we provide a comprehensive survey of current developments in HG representation learning.More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.
Häftad, Engelska, 2023
1 793 kr
Skickas inom 10-15 vardagar
Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node.In this book, we provide a comprehensive survey of current developments in HG representation learning.More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.