Deep Learning for Financial Market

Novel Deep Learning Models for Financial Market Predictions

Degree type


Closing date

10 October 2022



Citizenship requirement


About the research project

Regardless of people's investment strategy, fluctuations are expected in the financial market. Despite this variance, professional investors try to estimate their overall returns. Risks and returns differ based on investment types and other factors impacting stability and volatility. In financial markets, volatility captures the amount of fluctuation in prices. High volatility is associated with periods of market turbulence and large price swings, while low volatility describes more calm and quiet markets. In reality, trading for profit has always been a difficult problem to solve, even more so in today's fast-moving and complex financial markets, where electronic trading allows for thousands of transactions to occur within a fraction of a second, resulting in nearly unlimited opportunities to potentially find and take advantage of price differences in real-time. To attempt to predict returns, there are some computer-based Deep Learning algorithms and models for financial market trading. Yet, with new techniques and approaches, improved Deep Learning models could improve our ability to forecast an investment's return.

This project involves developing new Deep Learning models for improving machine learning's performance in financial market prediction. In the current era of big data, Deep Learning for predicting financial market trends has become even more popular than before. While Deep Learning has successfully brought profits to some investment operators, issues related to optimal model architecture, ensemble approach, and prediction accuracy remain. Additionally, while traditional approaches have mainly focussed on employing numerical financial data to build machine learning modes, it has been suggested that such models should also incorporate news and reviews relevant to financial markets for making investment decisions. This would involve adding computer-based natural language processing and knowledge representation to the improved models. In this project, you are expected to develop new Deep Learning algorithms to overcome these (and possibly other) issues so that financial market prediction can be significantly improved.

Primary Supervisor

Meet Dr Shuxiang Xu


Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:

  • a living allowance stipend of $28,854 per annum (2022 rate, indexed annually) for 3.5 years
  • a relocation allowance of up to $2,000
  • a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)

If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.

As part of the application process you may indicate if you do not wish to be considered for scholarship funding.


Applicants should review the Higher Degree by Research minimum entry requirements.

Additional eligibility criteria specific to this project/scholarship:

  • Applications are open to Domestic/ International/ Onshore applicants
  • Applicants must be able to undertake the project on-campus

Selection Criteria

The project is competitively assessed and awarded.  Selection is based on academic merit and suitability to the project as determined by the College.

Application process

There is a three-step application process:

  1. Select your project, and check you meet the eligibility and selection criteria;
  2. Contact the Primary Supervisor, Dr Shuxiang Xu to discuss your suitability and the project's requirements; and
  3. Submit an application by the closing date listed above.
    • Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
    • As part of your application, you will be required to submit a covering letter, a CV including 2 x referees and your project research proposal.

Following the application closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.

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