Machine Learning on Financial Time Series
Engineering, IT, Mathematics and Statistics
- This internship is able to cover project costs for domestic students only.
- If your skillset is aligned with this internship and you are located remotely, please enquire with the Internship Contact to discuss possible arrangements.
ABOUT THE INDUSTRY PARTNER
Tibra is a world-class diversified trading firm operating in the world’s most advanced financial markets with offices in Austinmer and Sydney in Australia, and London in the UK. At Tibra, no day is the same. Their work is challenging and dynamic, which means that their working days can vary in all aspects. They reward insight and celebrate success. Tibra’s culture is supportive and collaborative. You will be challenged and stretched to bring your ideas to the table and apply them to the markets.
Tibra’s people are our greatest asset, which is why they don’t compromise on quality: we are looking for the best. We are looking for smart, curious people with a scientific mind and a fascination for finance and capital markets.
WHAT’S IN IT FOR YOU?
- The opportunity to gain experience within a global quantitative trading firm
- The opportunity to collaborate with industry professionals on blue-sky research
- Collaboration with other researchers
- You will be able to use your research expertise on real world data and problems
RESEARCH TO BE CONDUCTED
Financial data is notoriously vast and noisy. The key goal of a quantitative trading firm is to discover structure and dependencies in data to construct predictive signals in financials markets. This will involve extraction, preparation, cleaning, transforming before any analysis is carried out.
This goal of this specific project is to make use of statistical to find relationship in large datasets that form the dynamics of the modern exchange. Categorical and regression based methods can both be used with the intention of finding a solution that can be proven to statistically outperform the classical methods as well as performing better in back test scenarios.
SKILLS WISH LIST
If you’re a PhD student and meet some or all the below we want to hear from you. We strongly encourage women, indigenous and disadvantaged candidates to apply:
- Strong statistical modelling skills
- Strong programming ability in either C++ or Python.
Please note: research outcomes are to be negotiated and agreed upon with the selected candidate during the project plan stage.
The aim is to conduct research into applications of machine learning techniques into market data prediction. There will be support and collaboration with our Tibra’s researchers.
- Analysis of ML models for prediction in financial data sets including documentation for use within Tibra.
- Development of software for the above analysis.
The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.
It is expected that the intern will primarily undertake this research project during regular business hours and maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.
The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.
CONNECT WITH APR.INTERN