Sparse machine learning methods for financial signal processing

Ever since stock trading came into force, financial economists are keen on identifying optimal methods that track stock movements and make a prediction on future prices with a high degree of accuracy. One such research problem is portfolio optimization. Ever since then an extensive research has bee...

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Main Author: Pucha Srinivasa Sai Chakravarthy
Other Authors: Justin Dauwels
Format: Theses and Dissertations
Language:English
Published: 2017
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Online Access:http://hdl.handle.net/10356/72617
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-726172023-07-04T15:05:23Z Sparse machine learning methods for financial signal processing Pucha Srinivasa Sai Chakravarthy Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Ever since stock trading came into force, financial economists are keen on identifying optimal methods that track stock movements and make a prediction on future prices with a high degree of accuracy. One such research problem is portfolio optimization. Ever since then an extensive research has been carried on in the space of portfolio optimization in financial engineering. A several of signal processing techniques like Time Series analysis, Regression analysis and Bayesian inferences have found their deep inroads into this particular area of research. Investors are keen on holding portfolios that maximize the returns and minimize the risk. And research of portfolio theory was primarily driven as a mean – variance optimization problem where in mean mimicked the returns and variance the risk. Current active research in portfolio optimization is for finding efficient mathematical techniques that solve mean – variance optimization problem efficiently and accurately with less computation burden. The new edition to current research in portfolio theory is sparsity. Sparsity explores the new space of achieving mean – variance optimization with minimum possible asset combinations. The prime motivation of this thesis is to find applications of decision sciences and machine learning techniques to solve the mean – variance optimization problem to construct portfolios that are optimal and simple with the aid of less computation intensive methods. Current thesis work aims to address the above problem from decision theory perspective. It employs a widely acknowledged approximation techniques of Bayesian inference like variational methods and exploits the advantages of such methods in constructing portfolios that are optimal and simple. This research shall find wide applications in financial industry from risk – management perspective. Sparsity which implies minimal combination of assets to form a portfolio significantly lessens the operational costs and transaction costs involved in holding a bulky portfolio in open markets. This shall be an advantage to end investors and enables to better manage the risk of a portfolio with minimum assets in place. Master of Science (Signal Processing) 2017-08-30T08:02:36Z 2017-08-30T08:02:36Z 2017 Thesis http://hdl.handle.net/10356/72617 en 79 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Pucha Srinivasa Sai Chakravarthy
Sparse machine learning methods for financial signal processing
description Ever since stock trading came into force, financial economists are keen on identifying optimal methods that track stock movements and make a prediction on future prices with a high degree of accuracy. One such research problem is portfolio optimization. Ever since then an extensive research has been carried on in the space of portfolio optimization in financial engineering. A several of signal processing techniques like Time Series analysis, Regression analysis and Bayesian inferences have found their deep inroads into this particular area of research. Investors are keen on holding portfolios that maximize the returns and minimize the risk. And research of portfolio theory was primarily driven as a mean – variance optimization problem where in mean mimicked the returns and variance the risk. Current active research in portfolio optimization is for finding efficient mathematical techniques that solve mean – variance optimization problem efficiently and accurately with less computation burden. The new edition to current research in portfolio theory is sparsity. Sparsity explores the new space of achieving mean – variance optimization with minimum possible asset combinations. The prime motivation of this thesis is to find applications of decision sciences and machine learning techniques to solve the mean – variance optimization problem to construct portfolios that are optimal and simple with the aid of less computation intensive methods. Current thesis work aims to address the above problem from decision theory perspective. It employs a widely acknowledged approximation techniques of Bayesian inference like variational methods and exploits the advantages of such methods in constructing portfolios that are optimal and simple. This research shall find wide applications in financial industry from risk – management perspective. Sparsity which implies minimal combination of assets to form a portfolio significantly lessens the operational costs and transaction costs involved in holding a bulky portfolio in open markets. This shall be an advantage to end investors and enables to better manage the risk of a portfolio with minimum assets in place.
author2 Justin Dauwels
author_facet Justin Dauwels
Pucha Srinivasa Sai Chakravarthy
format Theses and Dissertations
author Pucha Srinivasa Sai Chakravarthy
author_sort Pucha Srinivasa Sai Chakravarthy
title Sparse machine learning methods for financial signal processing
title_short Sparse machine learning methods for financial signal processing
title_full Sparse machine learning methods for financial signal processing
title_fullStr Sparse machine learning methods for financial signal processing
title_full_unstemmed Sparse machine learning methods for financial signal processing
title_sort sparse machine learning methods for financial signal processing
publishDate 2017
url http://hdl.handle.net/10356/72617
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