Industry project examples – Master of Applied Finance

Examples of past finance projects that our student teams have worked on with local organisations.

Te Taiwhenua o Te Whanganui ā Orotu – Financial Analysis and Peer Comparison

Te Taiwhenua o Te Whanganui ā Orotu supports its marae and hapū in areas including health, education, employment, housing and the environment, working in partnership with central and local government. As it continues to grow, Te Taiwhenua was interested in reviewing its financial situation and exploring opportunities to expand and diversify its revenue streams. A MAppFin student team assisted Te Taiwhenua with this work. 

The student team analysed various aspects of Te Taiwhenua's financial information and carried out a peer organisation comparison. Students also conducted research into alternative revenue opportunities and produced a template for presenting financial and other information related to the organisation.    

Auckland Council - Sustainable Finance Framework Assessment

Auckland Council is responsible for operating one of New Zealand’s largest portfolios of infrastructure and other public assets. The council has in place a Sustainable Finance Framework to support it in raising sustainable debt.

The council was interested in comparing its own Sustainable Finance Framework with other international sustainable finance criteria and principles. A student team carried out research to perform this comparison, identify areas of difference and make recommendations based on their findings. Students also evaluated the suitability of potential projects for funding according to sustainable finance criteria and conducted research comparing sustainable debt instruments with other debt instruments.

New Zealand Green Investment Finance – Analysis of NZ Emissions Landscape

New Zealand Green Investment Finance (NZGIF) invests to facilitate New Zealand’s decarbonisation. NZGIF was interested in analysing the commercial emissions landscape in New Zealand to identify key emitters, solution providers and investment opportunities to reduce emissions.

A MAppFin student team assisted NZGIF with this project by carrying out a survey of New Zealand's emissions landscape, summarising emissions by sector and subsector, identifying recent changes and future trends, and reporting the economic materiality of each sector. The team also analysed key entities within important sectors and identified potential offshore solution providers where gaps exist in New Zealand.

NZ Super Fund – NZ Director Network Analysis

The NZ Super Fund is New Zealand’s sovereign wealth fund and invests in a global portfolio of assets to contribute to the cost of paying for future New Zealand superannuation. The NZ Super Fund was interested in creating a network model to explore relationships between New Zealand entities and directors and their implications for a range of business problems and questions.

A MAppFin student team developed a python-based model to support the NZ Super Fund with this project. The model functionality included automatic collection of director and entity data from publicly available sources as well as construction of the network. Students performed analysis on the collected data, exploring implications for market outcomes.

ThatDay – Savings Outcomes Simulation

ThatDay is a registered charity that has developed a free-to-use web app designed to encourage positive saving behaviour among New Zealanders. The app simulates different financial outcomes under a range of inputs and assumptions. ThatDay was interested in assessing the assumptions used by the app and evaluating outcomes under a range of scenarios.

A MAppFin student team assisted ThatDay with this work. The team created an Excel-based model which emulated the code-based model used by the app. The model allowed the impact of different inputs, assumptions and scenarios on financial outcomes to be easily compared. The team further evaluated the assumptions used by the app by comparing them with those applied by other financial practitioners, as well as historical observed values.