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Title: Artificial intelligence/machine learning for wealth management
Authors: Teo, Wee Ren
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Teo, W. R. (2022). Artificial intelligence/machine learning for wealth management. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: SCSE21-0083
Abstract: Over the years, machine learning and Artificial intelligence have been making exponential advancements due to improvements in technology and computational power. They are rapidly transforming industries and societies in the world. Machine learning and Artificial intelligence are now available to everyone that is connected to the internet. It is no longer a concept that huge organizations can only implement. The paper proposes to capitalize on the available machine learning libraries and build a web application around them to provide users with information and knowledge to invest in the stock market. The web application aims to give recommendations and guidance on the stock they are interested in. The application's front end is created using a popular JavaScript framework called React. The recommendations which will be shown on the web application are generated through the various implementations of machine learning models such as Logistic Regression, Support Vector Machine, Long Short-Term Memory (LSTM), XG Boost, and Random Forest. The models were trained and tested using time series data obtained from the web. A Sentiment Analysis will be conducted to determine the sentiment of a company so a user can be better informed to decide. Results showed that the models can predict the signals reasonably well and will be able to help users make informed decisions. The backend is implemented entirely in Python and a web framework called FastAPI. A non-relational database called MongoDb will store the required data for the web application.
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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