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.
Results
Showing 1 to 20 of 34 for “"out-of-sample performance"”.
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Assessing out-of-sample hedging performance with commodity futures
This thesis investigates the out-of-sample performance of minimum-variance and unconditional hedging strategies in the corn futures market from 2002 to 2019. The out-of-sample performance is captured by new measures of hedging effectiveness that are fundamentally tied to basis and net price. The …
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Hedge Fund Performance and Derivative Hedging
<p>This dissertation is comprised of three essays which focus on hedge fund performance and derivative hedging. The first essay uses ETF returns as proxies for tradable risk factors in hedge fund performance evaluation and identifies contemporaneously relevant risk factors from the entire universe …
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Use of modern machine learning techniques to predict the occurrence and outcome of corporate takeover events
The objective of this project is to use machine learning to predict the occurrence of corporate takeovers. The findings show that random forest yields the best predictions out-of-sample based on the area under the curve (AUC) metric. As such, 8 independent variables are considered statistically …
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Word Sense Disambiguation in the domain of Sentiment Analysis through Deep Learning
Sentiment analysis forms part of a major component of Natural Language Processing (NLP), even though continuous improvements in NLP are being made, word disambiguation remains a complex problem within the domain of sentiment analysis (Navigli, 2009). Word Sense Disambiguation (WSD) is a problem …
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Forecasting the Yield Curve of Government Bonds: A Comparative Study
… the modeling and forecasting the term structure of interest rates. Despite its impressive performance in in-sample fitting yield curves, little research has focused on the out-of-sample forecast of yield curves using the Kalman filter. The goal of this thesis is to develop a unified dynamic model …
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NPL forecasting under a fourier residual modified model: An empirical analysis of an unsecured consumer credit provider in South Africa
… in accurately identifying the determinants of domestic NPLs has led to a review of time series forecasting techniques. This dissertation explores whether a forecasting model combining a traditional time series approach with a Fourier series residual modification technique performs well in …
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Estrategias de trading con Time Series Momentum
… involves the volatility-adjusted aggregation of univariate strategies and therefore relies heavily on the e ciency of the volatility estimator and on the quality of the momentum trading signal. Using a dataset with intra-day quotes of 18 assets from May 2017 to May 2019, we investigate these …
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Data-driven robust solution schemes for sequential decision making
… machine learning. Classical approaches such as sample average approximation—also referred to as empirical risk minimization in the machine learning literature—often suffer from poor out-of-sample performance when data is limited. To address this issue, the dissertation proposes a data-efficient …
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Deep neural networks for video classification in ecology
Analyzing large volumes of video data is a challenging and time-consuming task. Automating this process would very valuable, especially in ecological research where massive amounts of video can be used to unlock new avenues of ecological research into the behaviour of animals in their environments. …
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Decisionmetrices : dynamic structural estimation of shipping investment decisions
… that allow us to understand the dynamics of shipping investment decisions under uncertainty and test interrelated economic assertions with aggregate data. The main framework is a three-party model with a structural specification of the time charter rate process, based on market clearing …
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Optimal versus naive diversification : do different loss functions improve portfolio choice?
I estimate the out-of-sample performance of the equal weight, minimum variance and mean-variance model portfolios in different settings. In each setting, I vary the loss function used when estimating returns and covariances, length of the estimation window, and number of factors used in our …
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Systematic asset allocation using flexible views for South African markets
… probabilities to historical observations of asset class returns. This is achieved using relative entropy to find estimates with the least distortion to the prior distribution. Here, we use the HS-FP framework on South African financial market data for asset allocation purposes; by …
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A robust optimization approach to statistical estimation problems by Apostolos G. Fertis.
… and support vector machines. In the first part of the thesis, we formalize these connections using robust optimization. Specifically (a) We show that in classical regression, regularized estimators like lasso can be derived by applying robust optimization to the classical least squares problem. …
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Three essays on the predictive content and predictability of the nominal exchange rates in a changing world
… the predictive power and the predictability of the nominal USD/GBP exchange rate changes in a world with structural instabilities. In Chapter 2 we mainly focus on the predictive content of the exchange rates in an attempt to forecast the Taylor rule fundamentals, such as the output gap, the …
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Robust optimization for portfolio risk : a ravisit of worst-case risk management procedures after Basel III award.
The main purpose of this thesis is to develop methodological and practical improvements on robust portfolio optimization procedures. Firstly, the thesis discusses the drawbacks of classical mean-variance optimization models, and examines robust portfolio optimization procedures with CVaR and …
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Performance modeling of human-machine interfaces using machine learning
As the popularity of online retail expands, world-class electronic commerce (e-commerce) businesses are increasingly adopting collaborative robotics and Internet of Things (IoT) technologies to enhance fulfillment efficiency and operational advantage. E-commerce giants like Alibaba and Amazon are …
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Interpretable machine learning methods with applications to health care
… is a severe need for several areas of applications, like health care or business. Doctors or managers often need to understand how models make predictions, in order to make their final decisions. In this thesis, we improve and propose some interpretable machine learning methods by …
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A Statistical Approach to Modeling Wheel-Rail Contact Dynamics
… contact mechanics and dynamics that are of great importance to the railroad industry are evaluated by applying statistical methods to the large volume of data that is collected on the VT-FRA state-of-the-art roller rig. The intent is to use the statistical principles to highlight the …
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