Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 176 for “"Financial Data"”.
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Modelling skewness in Financial data
… and its extensions have been applied in lots of financial applications. This thesis contributes to the recent development of the skew-normal distribution by, firstly, analyzing the the properties of annualization and time-scaling of the skew-normal distribution under heteroskedasticity which, in …
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Integrating financial data over the Internet
This thesis examines the issues and value-added, from both the technical and economic perspective, of solving the information integration problem in the retail banking industry. In addition, we report on an implementation of a prototype for the Universal Banking Application using currently …
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Essays on the Econometrics of Financial Data
This dissertation proposes a methodology for inference in the context of diffusion processes with jumps. There are many applications. For example, in finance, this methodology can be used to study asset pricing. My dissertation consists of two chapters which are closely related. They reveal the …
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Mean reversion in macroeconomic and financial data
… on the mean reversion in macroeconomic and financial data using nonparametric estimation method. The first essay studies the recent attempts to solve the second form of the Purchasing Power Parity (PPP) puzzle (usually expressed as a half-life of 3∼5 years), mostly by using non-linear …
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Insights from financial data - old and new
This thesis investigates financial data — the backbone of empirical research in finance — to provide insights into the importance of precisely interpreting existing data as well as the power of findings from new data. Specifically, the first study in this thesis examines the impact of event-day …
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Time Series Models for Analyzing Financial Data
… 1986 are now not only widely used in modeling financial data but also provide a benchmark to evaluate other available models in empirical studies. The classical feature of this class of models is that the conditional variance (second moment) is considered to be time varying. ARCH models are …
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NLP driven large scale financial data analysis
… In terms of information, traders would read financial reports released by companies, 8-K reports, mass but informal social media information like tweets, as well as financial topic or market related reports from professional news agencies such as Reuters, Bloomberg, Yahoo Finance etc. Among …
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Deep learning models for high-frequency financial data
The limit order book of a financial instrument represents its supply and demand at each point in time. The limit order book data can be used to predict the future price of the financial instrument. We develop deep learning models to capture the high dimensional data distributions (on R^d) of the …
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Data to information to text summaries of financial data
… for a system that can convert vast quantities of data generated by existing systems and data analytics techniques, into usable information and then into a format that is easy for someone not trained in data analytics to understand. This is possible through Natural Language Generation (NLG). The …
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Contributions to Semiparametric Inference to Biased-Sampled and Financial Data
… and methods for the analysis of life-time and financial data under the umbrella of semiparametric framework. The first part studies the use of empirical likelihood on Levy processes that are used to model the dynamics exhibited in the financial data. The second part is a study of inferential …
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Three Essays on High-Frequency and High-Dimensional Financial Data Analysis
<p>In recent decades, financial market data has become available with increasingly higher frequency and higher dimension. This rapidly growing amount of financial data has created many research opportunities and challenges. In this dissertation, I address several important issues in the areas of …
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Statistical self-similarity in time series from financial data & chaotic dynamical systems
In this paper, I am going to introduce statistical self-similarity for discrete time series. My thesis is divided into three parts:
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Using publicly available financial data to measure production depth in the automobile industry
… depth from a company's publicly available financial data. This study examines two different methods of estimating production depth using publicly available financial data.
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Detecting Errors in Financial Data: A Multi-Agent LLM and Synthetic Data Approach
With the high volume of activity flowing through financial institutions, detecting potential errors remains a critical challenge. This paper addresses two key areas where errors may occur: business name registrations and transactions within valid accounts. Traditional string-matching methods …
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Application of multi-state financial data for use with the USDA Agricultural Conservation Planning Framework
… goal of this research was to develop and test datasets and methodologies to be used with the ACPF to quantify costs and nitrate reduction outcomes associated with best management practices (BMPs) and conservation scenarios in agriculturally dominated watersheds across the US Corn Belt. The ACPF …
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Performance Improvement in an Accounting Firm: Comparing Operational and Financial Data Before and After Process Redesign
… the process improvement intervention had on key financial and operational measures. Results indicated that the tax returns prepared in the new process were faster, cheaper, and more profitable. This study indicates that organizations conducting process improvement interventions can beneficially …
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Generic Architecture for Predictive Computational Modelling with Application to Financial Data Analysis: Integration of Semantic Approach and Machine Learning
… analytical conclusions regarding quantitative data structured as a data frame. The model involves heterogeneous data mining based on a semantic approach, graph-based methods (ontology, knowledge graphs, graph databases) and advanced machine learning methods. The main focus of my research is …
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