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Building an Optimized Stock Portfolio Using Machine Learning Models.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Building an Optimized Stock Portfolio Using Machine Learning Models.
作者:	Jones, Kayla Michelle.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2023
面頁冊數:	86 p.
附註:	Source: Masters Abstracts International, Volume: 84-11.
附註:	Advisor: Chowdhury, Abhinandan;Dolo, Samuel.
Contained By:	Masters Abstracts International84-11.
標題:	Applied mathematics.
電子資源:	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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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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653 $a Stocks
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