Deep Learning and Natural Language Processing Based FinTech prediction
Financial markets including stocks and forex constantly fluctuate. Apart from individual company performances, various external factors such as human psychology of the masses, local and international news contribute to the dynamics of the markets. Forecasting the level of the impact of these factors on financial markets is a dream of many decision makers. Markets usually exit patterns which could be used to predict future movement of the markets, however, the manual analysis of such patterns is very laborious and subjective. The development of an artificial intelligent (AI) agent based on technical, fundamental and sentimental indicators can make this dream a reality.
AI has been shown to successfully unveil the hidden patterns in data. AI implementation to financial data can help to identify the social mood of the masses and thus the movement in financial markets, which in turn can lead to stabilising of the socionomics of the society.
Therefore, in this project, we anticipate studying the socionomics patterns by digging deep into the historical data and market reaction to predict the future direction of the markets. We will employ machine/deep learning to correctly identify the wave trends such as Elliott waves. We will also investigate the correlation and the level of the impact of news, human sentiments or company performances on a sector.
Find out more here.
How to apply
If you are interested in applying for the above PhD topic please follow the steps below:
- Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
- Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
- Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.
This is a self funded topic
Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.
Meet the Supervisor(s)
- Matloob has over 2 decades of industry and academic experience. He completed his PhD in AI and Data Science from the University of Sydney, Australia in which he developed novel algorithms for understanding big genomic data. Dr. Khushi has earned various awards for his research achievements and has authored more than 70 research papers. During his postdoc (2014-2017) at the Children’s Medical Research Institute, Australia, he developed automated AI-based algorithms for expediting the drug discovery process. He has also developed solutions for the financial industry such as portfolio management, prediction of stock, forex and commodities markets. Dr. Khushi has supervised more than 100 research theses in various domains of AI and data mining and is always willing to supervise very talented PhD candidates.
Related Research Group(s)
Intelligent Data Analysis - Concerned with effective analysis of data involving artificial intelligence, dynamic systems, image and signal processing, optimisation, pattern recognition, statistics and visualisation.