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Three Essays On Machine Learning In Empirical Finance

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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 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 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.

Three Essays In Empirical Finance

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Author : Jordy Rillaerts
language : en
Publisher:
Release Date : 2024

Three Essays In Empirical Finance written by Jordy Rillaerts and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.

Three Essays On Empirical Finance

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Author : Chao Wang
language : en
Publisher:
Release Date : 2018

Three Essays On Empirical Finance written by Chao Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Banks and banking, Central categories.

Three Essays In Finance

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Author : Alex John Fabisiak
language : en
Publisher:
Release Date : 2019

Three Essays In Finance written by Alex John Fabisiak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

In the first chapter, I apply machine learning techniques to numerically solve high-dimensional continuous time models in finance. Traditional methods rely on finite difference schemes for solutions to partial differential equations. By approximating the solution with a deep neural network, I am able to leverage the computational efficiency of neural networks and batch gradient descent to accurately compute solutions involving many state variables. I demonstrate the accuracy and efficiency of this method for Black-Scholes options pricing problems and dynamic programming problems in up to 50 spatial dimensions, far beyond the capability of grid methods. I also develop a solution method to mean field game type problems, where both a value function and a distribution function must solve a system of differential equations, utilizing mixture density networks. In the second chapter (with Ivo Welch), we develop a model where buyers prefer local over lower-cost vendors even in the absence of direct preferences, taxes, subsidies, contracts, sanctions, information asymmetries, audits, etc. Instead, they prefer locals because they internalize the fact that local agents will in turn be more likely to buy from them in the future. Local sellers understand that buyers' preferences give them limited local market power, and therefore raise their prices and earn surplus in equilibrium. Our model can explain how voluntary reciprocity among subsets of identical agents can sustain itself, and how ex-ante identical goods from ex-ante identical sellers can acquire and maintain sustainably differentiated prices. In the third chapter (with Antonio Bernardo and Ivo Welch), we develop a model where firms with lower leverage are not only less likely to experience financial distress but are also better positioned to acquire assets from other distressed firms. With endogenous asset sales and values, each firm's debt choice then depends on the choices of its industry peers. With indivisible assets, otherwise identical firms may adopt different debt policies---some choosing highly levered operations (to take advantage of ongoing debt benefits), others choosing more conservative policies to wait for acquisition opportunities. Our key empirical implication is that the acquisition channel can induce firms to reduce debt when assets become more redeployable. This article has been accepted for publication and is forthcoming in the Journal of Financial and Quantitative Analysis.