Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157648
Title: AI-driven stock market prediction
Authors: Chong, Noel Zhenjie
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Chong, N. Z. (2022). AI-driven stock market prediction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157648
Project: P3052-202
Abstract: The accuracy of deep learning techniques used for prediction has always been deemed superior as compared to regression techniques. In this report, deep learning techniques such as Long Short-Term Memory, Recurrent Neural Network, Multi-Layer Perceptron and Gated Recurrent Unit will be used in a comparison with regression techniques such as Gradient Boosting Regressor and Support Vector Regressor to forecast the Straits Times Index (STI). The data sourced will also be non-linear and will be used as inputs into the algorithms to generate the results. The results will be compared using Fundamental Analysis and Technical Analysis. This experiment shows that the results from deep learning techniques does not generally mean that it is more accurate as compared to regression techniques.
URI: https://hdl.handle.net/10356/157648
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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