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Building an Optimized Stock Portfolio Using Machine Learning Models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Building an Optimized Stock Portfolio Using Machine Learning Models.
Author:	Jones, Kayla Michelle.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2023
Description:	86 p.
Notes:	Source: Masters Abstracts International, Volume: 84-11.
Notes:	Advisor: Chowdhury, Abhinandan;Dolo, Samuel.
Contained By:	Masters Abstracts International84-11.
Subject:	Applied mathematics.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30426086
ISBN:	9798379513153

Building an Optimized Stock Portfolio Using Machine Learning Models.
Jones, Kayla Michelle.

Building an Optimized Stock Portfolio Using Machine Learning Models. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 86 p.

Source: Masters Abstracts International, Volume: 84-11.

Thesis (M.S.Math)--Savannah State University, 2023.

This item must not be sold to any third party vendors.

This research aims to analyze more than 500 public stock market companies and their prices to identify the most profitable sectors from three major stock market indices, Nasdaq-100, Dow Jones, and the S&P 500. We developed four regression models and trained them to predict the price of a stock, forecast the future price, and generate optimized stock portfolios based on one's budget. We then measured the performance and accuracy of each prediction and forecast by calculating . To measure the profitabilityof each portfolio, we calculated the expected return, volatility, and Sharpe ratio to determine if they would outperform the S&P 500 Index over a 10-year period. Our best performing model belongs to the Polynomial Regression Model which has an expected portfolio return of 22.9%, volatility of 14.27%, and Sharpe ratio of 1.069. Lastly, this paper analyzes which sector is the most profitable based on our machine learning models. 

ISBN: 9798379513153Subjects--Topical Terms:

377601
Applied mathematics.
Subjects--Index Terms:

Deep learning

Building an Optimized Stock Portfolio Using Machine Learning Models.
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245 1 0 $a Building an Optimized Stock Portfolio Using Machine Learning Models.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 86 p.
500 $a Source: Masters Abstracts International, Volume: 84-11.
500 $a Advisor: Chowdhury, Abhinandan;Dolo, Samuel.
502 $a Thesis (M.S.Math)--Savannah State University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a This research aims to analyze more than 500 public stock market companies and their prices to identify the most profitable sectors from three major stock market indices, Nasdaq-100, Dow Jones, and the S&P 500. We developed four regression models and trained them to predict the price of a stock, forecast the future price, and generate optimized stock portfolios based on one's budget. We then measured the performance and accuracy of each prediction and forecast by calculating . To measure the profitabilityof each portfolio, we calculated the expected return, volatility, and Sharpe ratio to determine if they would outperform the S&P 500 Index over a 10-year period. Our best performing model belongs to the Polynomial Regression Model which has an expected portfolio return of 22.9%, volatility of 14.27%, and Sharpe ratio of 1.069. Lastly, this paper analyzes which sector is the most profitable based on our machine learning models. 
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650 4 $a Computer science. $3 199325
653 $a Deep learning
653 $a Machine learning
653 $a Regression models
653 $a Portfolio optimization
653 $a Stocks
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710 2 $a Savannah State University. $b Mathematics. $3 966826
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