Essays On Machine Learning In Empirical Finance And Accounting Research
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Essays On Machine Learning In Empirical Finance And Accounting Research
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Author : Daniel Marcel Metko
language : en
Publisher:
Release Date : 2022
Essays On Machine Learning In Empirical Finance And Accounting Research written by Daniel Marcel Metko and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Three Essays On Machine Learning In Empirical Finance
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Author : Jinhua Wang
language : en
Publisher:
Release Date : 2022
Three Essays On Machine Learning In Empirical Finance written by Jinhua Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Essays On The Application Of Machine Learning Techniques In The Empirical Asset Pricing Research
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Author : Tizian Otto
language : en
Publisher:
Release Date : 2022
Essays On The Application Of Machine Learning Techniques In The Empirical Asset Pricing Research written by Tizian Otto and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Three Essays On Empirical Finance
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Author : Tse-Chun Lin
language : en
Publisher: Rozenberg Publishers
Release Date : 2009
Three Essays On Empirical Finance written by Tse-Chun Lin and has been published by Rozenberg Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Essays On Conditional Asset Pricing And Machine Learning In Finance
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Author : Stephen Owen
language : en
Publisher:
Release Date : 2021
Essays On Conditional Asset Pricing And Machine Learning In Finance written by Stephen Owen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
In recent years there has been wide-scale access to improved statistical estimation techniques and the implementation of such techniques in financial economics. In this dissertation, I provide two brief overviews of the evolution of linear factor models in asset pricing and machine learning in finance. I then provide four research essays that implement machine learning in financial economic research settings. The first essay revisits tests of the conditional Capital Asset Pricing Model in an international context using multivariate generalized autoregressive conditional heteroskedasticity techniques. The second essay studies the use of hierarchical clustering in mean-variance optimal portfolio management. The third essay proposes a novel paragraph embedding technique that leverages the question-and-answer structure of earnings announcement calls to model the similarity between documents. The fourth and final essay studies the impact that dodgy managers have on idiosyncratic security performance.
Essays In Machine Learning In Finance
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Author : Ye Ye
language : en
Publisher:
Release Date : 2022
Essays In Machine Learning In Finance written by Ye Ye and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
The bond market is one of the largest financial markets, with $52.9 trillion of debt outstanding for the US market as of 2021. The implied interest rate for borrowing at different horizons is the fundamental object for this market. However, a complete set of interest is not observed and must be estimated from the noisy market data. In two papers, we develop machine learning methods to precisely estimate the term structure of interest rates and to understand and manage interest-rate related risks. In the first paper, we introduce a robust, flexible and easy-to-implement method for estimating the yield curve from Treasury securities. This method is non-parametric and optimally learns basis functions in reproducing Hilbert spaces with an economically motivated smoothness reward. We provide a closed-form solution of our machine learning estimator as a simple kernel ridge regression, which is straightforward and fast to implement. We show in an extensive empirical study on U.S. Treasury securities, that our method strongly dominates all parametric and non-parametric benchmarks, which positions our method as the new standard for yield curve estimation. In the second paper, we develop a sparse factor model for bond returns, that unifies non- parametric term structure estimation with cross-sectional factor modeling. Building on the modeling framework of the first paper, we estimate an optimal set of sparse basis functions, which maps into a cross-sectional conditional factor model. Our estimated factors are investable portfolios of traded assets, that replicate the full term structure and are sufficient to hedge against interest rate changes. In an extensive empirical study on U.S. Treasury securities, we show that the term structure of excess returns is well explained by four factors. We introduce a new measure for the time-varying complexity of bond markets based on the exposure to higher-order factors.
The Essentials Of Machine Learning In Finance And Accounting
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Author : Mohammad Zoynul Abedin
language : en
Publisher: Routledge
Release Date : 2021-06-20
The Essentials Of Machine Learning In Finance And Accounting written by Mohammad Zoynul Abedin and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-20 with Business & Economics categories.
• A useful guide to financial product modeling and to minimizing business risk and uncertainty • Looks at wide range of financial assets and markets and correlates them with enterprises’ profitability • Introduces advanced and novel machine learning techniques in finance such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches and applies them to analyze finance data sets • Real world applicable examples to further understanding
Novel Financial Applications Of Machine Learning And Deep Learning
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Author : Mohammad Zoynul Abedin
language : en
Publisher: Springer Nature
Release Date : 2023-03-01
Novel Financial Applications Of Machine Learning And Deep Learning written by Mohammad Zoynul Abedin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-01 with Business & Economics categories.
This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
Machine Learning In Asset Pricing
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Author : Stefan Nagel
language : en
Publisher: Princeton University Press
Release Date : 2021-05-11
Machine Learning In Asset Pricing written by Stefan Nagel and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-11 with Business & Economics categories.
A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.
Machine Learning For Economics And Finance In Tensorflow 2
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Author : Isaiah Hull
language : en
Publisher: Apress
Release Date : 2020-11-26
Machine Learning For Economics And Finance In Tensorflow 2 written by Isaiah Hull and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-26 with Computers categories.
Work on economic problems and solutions with tools from machine learning. ML has taken time to move into the space of academic economics. This is because empirical work in economics is concentrated on the identification of causal relationships in parsimonious statistical models; whereas machine learning is oriented towards prediction and is generally uninterested in either causality or parsimony. That leaves a gap for both students and professionals in the economics industry without a standard reference. This book focuses on economic problems with an empirical dimension, where machine learning methods may offer something of value. This includes coverage of a variety of discriminative deep learning models (DNNs, CNNs, RNNs, LSTMs, the Transformer Model, etc.), generative machine learning models, random forests, gradient boosting, clustering, and feature extraction. You'll also learn about the intersection of empirical methods in economics and machine learning, including regression analysis, text analysis, and dimensionality reduction methods, such as principal components analysis. TensorFlow offers a toolset that can be used to setup and solve any mathematical model, including those commonly used in economics. This book is structured to teach through a sequence of complete examples, each framed in terms of a specific economic problem of interest or topic. Otherwise complicated content is then distilled into accessible examples, so you can use TensorFlow to solve workhorse models in economics and finance. What You'll Learn Define, train, and evaluate machine learning models in TensorFlow 2 Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems Solve workhorse models in economics and finance Who This Book Is For Students and data scientists working in the economics industry. Academic economists and social scientists who have an interest in machine learning are also likely to find this book useful.