AI for Finance

The rise in prominence of cryptocurrencies have led to increased volatility and trading in cryptocurrency exchanges. Financial institutions are now embracing the use of alternative data especially towards social media commentary to increase their investment returns. The rationale is based on beha...

Full description

Saved in:
Bibliographic Details
Main Author: Phoe, Chuan Bin
Other Authors: Erik Cambria
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/158241
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-158241
record_format dspace
spelling sg-ntu-dr.10356-1582412022-06-02T01:10:35Z AI for Finance Phoe, Chuan Bin Erik Cambria School of Computer Science and Engineering cambria@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Document and text processing The rise in prominence of cryptocurrencies have led to increased volatility and trading in cryptocurrency exchanges. Financial institutions are now embracing the use of alternative data especially towards social media commentary to increase their investment returns. The rationale is based on behavioural finance which proved that financial decisions are significantly driven by emotion and mood. As such, sentiment analysis of financial microblogs have been getting increased attention. In this project, I will be leveraging on the use of Text Mining and NLP techniques to better predict the financial sentiment of social media cryptocurrency content. We will take both Symbolic and Sub Symbolic approaches in tackling this problem using lexicons and learningbased language models respectively. Our results show that the proposed final hybrid architecture outperforms individual lexicons in the current literature and state-of-the-art deep learning methods for this sentiment classification problem. Bachelor of Engineering (Computer Science) 2022-06-02T01:10:35Z 2022-06-02T01:10:35Z 2022 Final Year Project (FYP) Phoe, C. B. (2022). AI for Finance. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158241 https://hdl.handle.net/10356/158241 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Document and text processing
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Document and text processing
Phoe, Chuan Bin
AI for Finance
description The rise in prominence of cryptocurrencies have led to increased volatility and trading in cryptocurrency exchanges. Financial institutions are now embracing the use of alternative data especially towards social media commentary to increase their investment returns. The rationale is based on behavioural finance which proved that financial decisions are significantly driven by emotion and mood. As such, sentiment analysis of financial microblogs have been getting increased attention. In this project, I will be leveraging on the use of Text Mining and NLP techniques to better predict the financial sentiment of social media cryptocurrency content. We will take both Symbolic and Sub Symbolic approaches in tackling this problem using lexicons and learningbased language models respectively. Our results show that the proposed final hybrid architecture outperforms individual lexicons in the current literature and state-of-the-art deep learning methods for this sentiment classification problem.
author2 Erik Cambria
author_facet Erik Cambria
Phoe, Chuan Bin
format Final Year Project
author Phoe, Chuan Bin
author_sort Phoe, Chuan Bin
title AI for Finance
title_short AI for Finance
title_full AI for Finance
title_fullStr AI for Finance
title_full_unstemmed AI for Finance
title_sort ai for finance
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/158241
_version_ 1735491160513708032