App for predicting stock price fluctuations with neural network

• المؤلف الرئيسي: Andrew Tatang
• مؤلفون آخرون: Wong Liang Jie
• التنسيق: Final Year Project
• اللغة: English
• منشور في: Nanyang Technological University 2023
• الموضوعات: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/172765

Stock market investment has become one of the most popular ways for people to invest their money in hoping to get great return in the future. How the stock price fluctuates however often is not predictable. It can be affected by a lot of factors, especially external events that can greatly shifts the fluctuations. Hence it possesses a great challenge to predict stock price fluctuations. As machine learning and artificial intelligence has been greatly improved and curated, it has become one of the available algorithms to predict the stock price fluctuations with great accuracy. Long Short-Term Memory is one of the neural network models in deep learning that is capable of doing so. Incorporating a LSTM model into a mobile application is the aim of this final year project to help user make informed financial decisions based on a highly curated mathematical model calculation of stock price data.