Author: Yuxin Zhang (Ph. D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 362
Book Description
In my dissertation, I propose a general research framework of MAD---Monitoring, Analyzing, and Data Informed Decision-making---for financial decision-making. I present three essays which concentrate on two consequential aspects of decision-making for financial risk management. The first two essays focus on better monitoring and analyzing the risk, and the last one focuses on better data-informed decision-making based on the observation and analysis. In the first essay, I study the modeling of joint mortality for the practice of life insurance and annuity pricing. Specifically, I develop a new mathematical model to describe the joint mortality for coupled dependent lives. This model can be used to guide the risk management strategy and the pricing policy for insurance and annuity products. It is shown that it improves the current methods for modeling financial decision-making related to dependent life structures (such as joint life insurance, last survivor annuities, and defined benefit plans for married couples). In the second essay, I study the prediction of Bitcoin price movement and the relevant implications for business analytics. I exploit Bitcoin transaction networks and link network characteristics with the Bitcoin market exchange price. Based on this linkage and the data record, I construct predictive models for Bitcoin price movement. With the innovative use of Bitcoin transaction network data, the predictive models lead to more accurate results which outperform existing models. This methodological innovation also presents new managerial insights from network perspectives. In the third essay, I focus on data-driven decision-making in contexts of the allocation of disaster relief funds. Specifically, I tackle methodological challenges in disaster management when data are extremely sparse and insufficient in the beginning of the disaster evolution, and slowly become more available and reliable as time unfolds. Here I propose an iterative learning method within the general MAD framework to estimate disaster damage losses using very limited and slowly obtained data. Results show that this iterative learning method leads to highly accurate results with fast convergence of the estimation error to a very low level. The framework and results of this essay can be further used for disaster management and resource allocation in various scenarios
Three Essays on Business Analytics
Author: Yuxin Zhang (Ph. D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 362
Book Description
In my dissertation, I propose a general research framework of MAD---Monitoring, Analyzing, and Data Informed Decision-making---for financial decision-making. I present three essays which concentrate on two consequential aspects of decision-making for financial risk management. The first two essays focus on better monitoring and analyzing the risk, and the last one focuses on better data-informed decision-making based on the observation and analysis. In the first essay, I study the modeling of joint mortality for the practice of life insurance and annuity pricing. Specifically, I develop a new mathematical model to describe the joint mortality for coupled dependent lives. This model can be used to guide the risk management strategy and the pricing policy for insurance and annuity products. It is shown that it improves the current methods for modeling financial decision-making related to dependent life structures (such as joint life insurance, last survivor annuities, and defined benefit plans for married couples). In the second essay, I study the prediction of Bitcoin price movement and the relevant implications for business analytics. I exploit Bitcoin transaction networks and link network characteristics with the Bitcoin market exchange price. Based on this linkage and the data record, I construct predictive models for Bitcoin price movement. With the innovative use of Bitcoin transaction network data, the predictive models lead to more accurate results which outperform existing models. This methodological innovation also presents new managerial insights from network perspectives. In the third essay, I focus on data-driven decision-making in contexts of the allocation of disaster relief funds. Specifically, I tackle methodological challenges in disaster management when data are extremely sparse and insufficient in the beginning of the disaster evolution, and slowly become more available and reliable as time unfolds. Here I propose an iterative learning method within the general MAD framework to estimate disaster damage losses using very limited and slowly obtained data. Results show that this iterative learning method leads to highly accurate results with fast convergence of the estimation error to a very low level. The framework and results of this essay can be further used for disaster management and resource allocation in various scenarios
Publisher:
ISBN:
Category :
Languages : en
Pages : 362
Book Description
In my dissertation, I propose a general research framework of MAD---Monitoring, Analyzing, and Data Informed Decision-making---for financial decision-making. I present three essays which concentrate on two consequential aspects of decision-making for financial risk management. The first two essays focus on better monitoring and analyzing the risk, and the last one focuses on better data-informed decision-making based on the observation and analysis. In the first essay, I study the modeling of joint mortality for the practice of life insurance and annuity pricing. Specifically, I develop a new mathematical model to describe the joint mortality for coupled dependent lives. This model can be used to guide the risk management strategy and the pricing policy for insurance and annuity products. It is shown that it improves the current methods for modeling financial decision-making related to dependent life structures (such as joint life insurance, last survivor annuities, and defined benefit plans for married couples). In the second essay, I study the prediction of Bitcoin price movement and the relevant implications for business analytics. I exploit Bitcoin transaction networks and link network characteristics with the Bitcoin market exchange price. Based on this linkage and the data record, I construct predictive models for Bitcoin price movement. With the innovative use of Bitcoin transaction network data, the predictive models lead to more accurate results which outperform existing models. This methodological innovation also presents new managerial insights from network perspectives. In the third essay, I focus on data-driven decision-making in contexts of the allocation of disaster relief funds. Specifically, I tackle methodological challenges in disaster management when data are extremely sparse and insufficient in the beginning of the disaster evolution, and slowly become more available and reliable as time unfolds. Here I propose an iterative learning method within the general MAD framework to estimate disaster damage losses using very limited and slowly obtained data. Results show that this iterative learning method leads to highly accurate results with fast convergence of the estimation error to a very low level. The framework and results of this essay can be further used for disaster management and resource allocation in various scenarios
Essays in Business Analytics
Author: Feng Mai
Publisher:
ISBN:
Category :
Languages : en
Pages : 144
Book Description
The availability of structured and unstructured data, along with recent advancements in machine learning methods and tools, pose both challenges and opportunities for businesses. The three essays in this dissertation address important aspects of business such as marketing and operations using emerging business analytics methods. The essays are devoted to two topics in analytics: advances in unsupervised learning methods and analytics of unstructured, textual data. In Essay 1 we develop a business intelligence framework and advance market structure analysis by combining computational linguistics, machine learning, and relevant marketing theories to reveal consumer insights from free-form product reviews. Our text analytics method is able to create a hierarchy for product attributes, discover consumer sentiments, and construct market structure perceptual maps. In Essay 2, we use deep learning and evolutionary clustering to study the dynamics of market segmentation. We adopt the skip-gram model to learn computable, vectorized representation of product attributes. In addition, the evolutionary clustering model integrates a measure of temporal smoothness into the overall measure of clustering quality, and thus can be used as a method to study market structures over time. In Essay 3, we apply expectation-maximization (EM), a widely used method in statistical inference, to solve a discrete optimization problem that has many applications in operations management. We frame the optimization problem as a semi-supervised learning problem and develop a heuristic to solve a capacitated clustering problem and its stochastic variant.
Publisher:
ISBN:
Category :
Languages : en
Pages : 144
Book Description
The availability of structured and unstructured data, along with recent advancements in machine learning methods and tools, pose both challenges and opportunities for businesses. The three essays in this dissertation address important aspects of business such as marketing and operations using emerging business analytics methods. The essays are devoted to two topics in analytics: advances in unsupervised learning methods and analytics of unstructured, textual data. In Essay 1 we develop a business intelligence framework and advance market structure analysis by combining computational linguistics, machine learning, and relevant marketing theories to reveal consumer insights from free-form product reviews. Our text analytics method is able to create a hierarchy for product attributes, discover consumer sentiments, and construct market structure perceptual maps. In Essay 2, we use deep learning and evolutionary clustering to study the dynamics of market segmentation. We adopt the skip-gram model to learn computable, vectorized representation of product attributes. In addition, the evolutionary clustering model integrates a measure of temporal smoothness into the overall measure of clustering quality, and thus can be used as a method to study market structures over time. In Essay 3, we apply expectation-maximization (EM), a widely used method in statistical inference, to solve a discrete optimization problem that has many applications in operations management. We frame the optimization problem as a semi-supervised learning problem and develop a heuristic to solve a capacitated clustering problem and its stochastic variant.
Nature, the Utility of Religion, and Theism
Author: John Stuart Mill
Publisher:
ISBN:
Category : God
Languages : en
Pages : 284
Book Description
Publisher:
ISBN:
Category : God
Languages : en
Pages : 284
Book Description
Three Essays on Fiscal Policy and Business Cycle Analysis
Author: Christian Breuer
Publisher:
ISBN:
Category :
Languages : en
Pages : 123
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 123
Book Description
Three Essays in Monetary Theory
Author: Ludwig Van den Hauwe
Publisher: BoD – Books on Demand
ISBN: 2810602212
Category : Monetary policy
Languages : en
Pages : 188
Book Description
Recent events in international financial markets have revived the scientific interest in conceivable institutional alternatives to prevailing monetary arrangements. In the essays reprinted in this book, the author critically examines some of the more influential arguments which have been made in favour of decentralization in banking.
Publisher: BoD – Books on Demand
ISBN: 2810602212
Category : Monetary policy
Languages : en
Pages : 188
Book Description
Recent events in international financial markets have revived the scientific interest in conceivable institutional alternatives to prevailing monetary arrangements. In the essays reprinted in this book, the author critically examines some of the more influential arguments which have been made in favour of decentralization in banking.
Three Essays on
Author: Moonsuk Oh
Publisher:
ISBN:
Category :
Languages : en
Pages : 362
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 362
Book Description
Three Essays on Macroeconomics and Econometric Analysis of Business Cycle
Author: Yang Su Park
Publisher:
ISBN:
Category :
Languages : en
Pages : 176
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 176
Book Description
Three Essays on Regional Business Cycle Analysis
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Institute of Horticultural Engineering (ITT).
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 6
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 6
Book Description
Three Essays in Modern Data Analysis
Author: Wen Cao
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description