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Building an Optimized Stock Portfolio Using Machine Learning Models.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Building an Optimized Stock Portfolio Using Machine Learning Models./
Author:	Jones, Kayla Michelle.
Description:	1 online resource (86 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
Subject:	Applied mathematics. -
Online resource:	click for full text (PQDT)
ISBN:	9798379513153

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

Building an Optimized Stock Portfolio Using Machine Learning Models. - 1 online resource (86 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379513153Subjects--Topical Terms:

1069907
Applied mathematics.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

554714
Electronic books.

Building an Optimized Stock Portfolio Using Machine Learning Models.
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502 $a Thesis (M.S.Math)--Savannah State University, 2023.
504 $a Includes bibliographical references
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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538 $a Mode of access: World Wide Web
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650 4 $a Finance. $3 559073
650 4 $a Computer science. $3 573171
653 $a Deep learning
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653 $a Regression models
653 $a Portfolio optimization
653 $a Stocks
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