Causal Inference and Prediction of Asset Returns
Engineering, IT, Mathematics and Statistics
- This internship is able to cover project costs for domestic students only.
- The Industry Partner has implemented appropriate preparations to comply with Federal and State Government requirements regarding COVID-19 safety. Due to remote arrangements, this internship is now accepting applications from eligible PhD students nationwide.
ABOUT THE INDUSTRY PARTNER
Tibra is a world-class diversified trading firm operating in the world’s most advanced financial markets with offices in Sydney and Austinmer in Australia, Hong Kong and London. At Tibra, no day is the same. They reward insight and celebrate success. Their 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 their greatest asset, which is why they don’t compromise on quality: they are looking for the best. They 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 methods to find robust causal drivers of traded financial instruments that can be supported by fundamental economic theories. The methods to be used here could include (but are not limited to); Bayesian inference, Granger causality, signal processing, cointegration, ensemble methods and dimensionality reduction.
The research should be expressed in a software package that abstracts the methodologies used and may be used by other quant researchers within Tibra for ongoing testing and research.
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 skill.s
- Strong programming ability. Python is the preference. Working knowledge of Spark/Scala for distributed computing is a plus, as is being comfortable in a Unix environment.
- An interest in quantitative finance – a knowledge of finance is not necessary.
The aim is to conduct research into causal drivers of asset prices. There will be ongoing support and collaboration with quant researchers from within Tibra.
The final product should be:
- Identification of the appropriate numerical tools for predicting causal effects of asset returns
- Production of efficient and fast software to run such methods
- Documented research for use within Tibra
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.
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