Comparing Deep Learning and Machine Learning for Stock Price Forecasting

Adrekar, Gulamali Isa (2024) Comparing Deep Learning and Machine Learning for Stock Price Forecasting. Masters thesis, Dublin, National College of Ireland.

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Abstract

This paper studies the performance comparison of ML and DL models for predicting stock prices, especially Apple’s historical data. The study uses models like Logistic Regression, Support Vector Machine (SVM), Long Short-Term Memory(LSTM), and Convolutional Neural Networks(CNN) for performance evaluation under metrics of accuracy, computational efficiency as well as resource utilization. This research approach includes data collection from Yahoo Finance, model formulation, and training with fine-tuning along with extensive evaluations via accuracy and mean squared error. Generally, results indicate that traditional ML models are efficient and easier to interpret compared to DL models like LSTM and CNN in the financial data context; however, using DL has better forecast accuracy when dealing with more complex structured forms of financial time-series. This work provides a range of insights into the real-world application of these algorithms to financial forecasting, enabling improved investment strategies and risk management.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Byrne, Brian UNSPECIFIED
Uncontrolled Keywords:	Stock Price Prediction; Machine Learning; Deep Learning; Support Vector Machine; Long Short-Term Memory
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management H Social Sciences > HG Finance > Fintech T Technology > T Technology (General) > Information Technology > Fintech Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HG Finance > Investment > Stock Exchange
Divisions:	School of Computing > Master of Science in FinTech
Depositing User:	Ciara O'Brien
Date Deposited:	02 Aug 2025 11:51
Last Modified:	02 Aug 2025 11:51
URI:	https://norma.ncirl.ie/id/eprint/8409

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