Semi-metric machine learning techniques to investigate trend of COVID 19 spread and impact on stock market

Summary

The spread of COVID 19 pandemic appears to occur in several phrases and each phrase has possibly different impacts on the economy as reflected in the stock market. This project aims to detect and compare the impact of COVID 19 spread patterns across countries during different phrases on stock market through some measures of market properties. The COVID 19 spread can be measured by time series daily infected and death cases whereas market conditions can be measured through market indices returns, volatilities and their persistence etc. Some financial time series models can be applied to extract other market feature information. These information can be combined in various ways for each country and various semi-metrics can be used to measure differences across countries in the form of distance matrices. Change point detection, clustering and other machine learning techniques can be applied to summarise these distance information and identify association between COVID 19 spread patterns and stock market conditions in different phrases. There are many potential area to explore in each stage of the analyses.

Supervisor(s)

Associate Professor Jennifer Chan

Research Location

School of Mathematics and Statistics

Program Type

PHD

Synopsis

Machine learning methods

Additional Information

HDR Inherent Requirements

In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:

- Confidential disclosure and registration of a disability that may hinder your performance in your degree;
- Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.);
- Ability to perform independently and/or with minimal supervision;
- Ability to undertake certain physical tasks (e.g. heavy lifting);
- Ability to undertake observatory, sensory and communication tasks;
- Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden);
- Ability to work in confined spaces or at heights;
- Ability to operate heavy machinery (e.g. farming equipment);
- Hold or acquire an Australian driver’s licence;
- Hold a current scuba diving license;
- Hold a current Working with Children Check;
- Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.)

You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application.

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Keywords

Semi-metrics, Machine learning, change point detection, clustering, returns and volatility models, COVID 19 pandemic

Opportunity ID

The opportunity ID for this research opportunity is: 2878

Other opportunities with Associate Professor Jennifer Chan