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Explainable machine learning for exploiting technical and fundamental indicators in stock trading

   Cardiff School of Computer Science & Informatics

This project is no longer listed on FindAPhD.com and may not be available.

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  Dr Yuhua Li, Dr Qingwei Wang  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

The aim of this project is to use machine learning to combine technical analysis and fundamental analysis in stock trading. Technical analysis and fundamental analysis are two commonly employed investment tools for timing the market and select stocks. Technical analysis often relies on daily price or trading volume, and is usually applied for short-term trading, while fundamental analysis focuses on financial ratios and more suitable for long-term investing. Hedge fund managers have argued that, to achieve best trading performance, one should combine both tools.

Yet an important but difficult problem remains -- there are a large number of technical and financial indicators. While each of them can be used for predicting future returns, it is ex-ante unclear which indicators should be used, and how to combine them to improve trading performance. The high-dimensional nature of machine learning methods is well suited for such a challenging problem. Existing literature has used machine learning for combining different technical indicators or fundamental ratios. Much less is explored to combine both methods. This project attempts to fill the void. It may also shed some lights on the questions such as when and why ML-combined strategy works and for what types of stocks it works better.

Keywords: explainable machine learning, stock trading, technical indicators, fundamental indicators.

Contact Yuhua Li ([Email Address Removed]) or Qingwei Wang ([Email Address Removed]) for information on the project.

Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas.

Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.

How to apply:

Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below

Please submit your application before the application deadline 29th April 2022 via Computer Science and Informatics - Study - Cardiff University

In order to be considered candidates must submit the following information: 

  • Supporting statement 
  • CV 
  • In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD
  • In the funding field of your application, insert “I am applying for 2022 PhD Scholarship in Computer Science and Informatics”, and specify the project title and supervisors of this project in the text box provided.
  • Qualification certificates and Transcripts
  • References x 2 
  • Proof of English language (if applicable)

Interview - If the application meets the entrance requirements, you will be invited to an interview

If you have any questions or need more information, please contact [Email Address Removed]

Funding Notes

A School-Funded PhD Scholarship is available for entry 2022/23.
In the Funding field of your application, insert "I am applying for 2022 PhD Scholarship" and specify the project title and supervisor of this project in the fields provided.
This project is also open to Self-Funded students worldwide. If you are interested in applying for a Self-Funded PhD, please search FindAPhD for this specific project title, supervisor or School within its Scholarships category.


Brogaard & Zareei (2021) “Machine Learning and the Stock Market” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3233119.
Chen et.al. (2022) “Predicting Direction of Future Earnings Changes Using Machine Learning and Detailed Financial Data” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3741015.
Beyaz et.al. (2018) “Comparing Technical and Fundamental Indicators in Stock Price Forecasting” https://ieeexplore.ieee.org/abstract/document/8623000.
Cao & You (2021) “Fundamental Analysis Via Machine Learning” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3706532.

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