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Forecasting the Stock Prices Using Machine Learning, Deep Learning, and Reinforcement Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Forecasting the Stock Prices Using Machine Learning, Deep Learning, and Reinforcement Learning.
Author:	Etukuru, Raghurami Reddy.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2023
Description:	184 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Notes:	Advisor: Dhou, Khaldoon.
Contained By:	Dissertations Abstracts International85-01B.
Subject:	Computer science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30525121
ISBN:	9798379911935

Forecasting the Stock Prices Using Machine Learning, Deep Learning, and Reinforcement Learning.
Etukuru, Raghurami Reddy.

Forecasting the Stock Prices Using Machine Learning, Deep Learning, and Reinforcement Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 184 p.

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--Northcentral University, 2023.

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

This research investigates the effectiveness of machine learning, deep learning, and reinforcement learning techniques for predicting stock prices. The study explored how to predict stock prices accurately when the time series data exhibits the characteristics of non-stationarity, non-linearity, long-term memory, and asymmetry. The stock prices were forecasted using the historical observations of stock prices using artificial intelligence so that investors could make the right investment decisions. This dissertation explored machine learning, deep learning, and reinforcement learning techniques for predicting stock prices when the time series exhibits the characteristics of non-stationarity, non-linearity, long-term memory, and asymmetry. The study employed a combination of data analysis, model development, and model training to examine the performance of various forecasting models. The research methodology used in this dissertation is a quantitative approach based on historical stock prices. The data were analyzed using statistical methods, and various forecasting models were developed and evaluated. The study also explored the impact of different factors, such as the hyperparameter fine-tuning, on the forecasting models' accuracy. The key findings of this research show that deep learning and reinforcement learning techniques can effectively predict stock prices to achieve higher accuracy and profitability. The study demonstrates that using a combination of techniques can improve the accuracy of the forecasts. The research also demonstrates the importance of feature and model selection for accurate stock price prediction. The study contributes to the existing literature by providing empirical evidence of these techniques' effectiveness and insights for future research in this area.

ISBN: 9798379911935Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

Machine learning

Forecasting the Stock Prices Using Machine Learning, Deep Learning, and Reinforcement Learning.
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500 $a Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
500 $a Advisor: Dhou, Khaldoon.
502 $a Thesis (Ph.D.)--Northcentral University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a This research investigates the effectiveness of machine learning, deep learning, and reinforcement learning techniques for predicting stock prices. The study explored how to predict stock prices accurately when the time series data exhibits the characteristics of non-stationarity, non-linearity, long-term memory, and asymmetry. The stock prices were forecasted using the historical observations of stock prices using artificial intelligence so that investors could make the right investment decisions. This dissertation explored machine learning, deep learning, and reinforcement learning techniques for predicting stock prices when the time series exhibits the characteristics of non-stationarity, non-linearity, long-term memory, and asymmetry. The study employed a combination of data analysis, model development, and model training to examine the performance of various forecasting models. The research methodology used in this dissertation is a quantitative approach based on historical stock prices. The data were analyzed using statistical methods, and various forecasting models were developed and evaluated. The study also explored the impact of different factors, such as the hyperparameter fine-tuning, on the forecasting models' accuracy. The key findings of this research show that deep learning and reinforcement learning techniques can effectively predict stock prices to achieve higher accuracy and profitability. The study demonstrates that using a combination of techniques can improve the accuracy of the forecasts. The research also demonstrates the importance of feature and model selection for accurate stock price prediction. The study contributes to the existing literature by providing empirical evidence of these techniques' effectiveness and insights for future research in this area.
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