Quantitative Finance: An Alternative Intraday Liquidity Risk Model; Anti-Poaching in Africa: Sociotechnical Factors of Influence
Zhang, Rachel, School of Engineering and Applied Science, University of Virginia
Seabrook, Bryn, Engineering and Society, University of Virginia
Graham, Daniel, Computer Science, University of Virginia
Vrugtman, Rosanne, Computer Science, University of Virginia
As an investment bank with over a trillion dollars in assets, Bank A (pseudonym) must ensure its funds are managed properly (Leon, 2015). One aspect of portfolio management is ensuring that all debts are paid on time to counteract intraday liquidity risk, the risk that the bank fails to pay its debts on time (Bech, 2008). To mitigate the risk of defaulting on its debts, Bank A maintains a reserve fund to cover its debts in case there is a liquidity shortage during the day. Funding the reserve is a dynamic process that requires an algorithm that accurately predicts how much money is needed in the reserve without setting aside so much liquidity that the money cannot be used elsewhere. This portfolio documents the optimization of this prediction algorithm, with the product of the technical research topic being a reduction in the amount of money predicted while still being able to cover the bank’s debts.
Billions of dollars also move under the table every year as part of the illicit wildlife trade. In order to support what has become the fourth most lucrative global crime, poaching in sub-Saharan Africa increases every year and has a devastating effect on ecosystems (Lunstrum & Givá, 2020). Though some anti-poaching techniques such as the World Wildlife Fund’s Google Glass initiative have been proven to curb poaching numbers significantly, these methods may be economically infeasible for the local community to implement. The portfolio documents the analysis of the development of these techniques under the lens of socioeconomic context. By discovering the sociotechnical aspects of conservation technologies that make them most successful, the STS research paper informs the development of a more sustainable anti-poaching system.
At Bank A, an American investment bank and financial services company, around $4 billion was stagnating daily based on outdated model predictions. The technical research project produced an algorithm to reduce the amount of money that needed to be set aside according to the current intraday liquidity risk model. This algorithm uses Fourier transforms to predict the reserve amount differently and its effectiveness was compared to that of the original model by evaluating the amount of money that could be saved while still being able to pay off everything on the due date. This new intraday liquidity risk model minimized costs by roughly 4 billion dollars and was valuable because it saved the firm a significant amount of money, which can be invested or repurposed elsewhere.
Poaching, the illegal capture of wild animals, is the fourth most lucrative global crime and has a devastating effect on the ecosystems of sub-Saharan Africa (Lunstrum & Givá, 2020). By illegally hunting and harvesting native animals, poachers threaten the natural resource supply and the tourism it brings, a source of income for many local communities. As the illicit African wildlife trade expands, it has become imperative for the three stakeholders — governments, non-governmental organizations, and local communities — to develop an effective anti-poaching system to protect endangered species. This STS paper investigates how community involvement and layered approaches impact the effectiveness of anti-poaching methods used in sub-Saharan Africa, and how collaboration between the three stakeholders may be used to address the poaching epidemic. Three case studies are used to frame the development of existing anti-poaching techniques in terms of the technological fix concept, demonstrating the ineffectiveness of solely institutional, social, or economic approaches. This research is analyzed using the wicked problem theory to elucidate the underlying economic imbalance that has not been addressed by prior conservation techniques. The resulting STS paper informs the development of a more sustainable anti-poaching system by integrating sociotechnical aspects of different stakeholder efforts that have been successful in the past.
The portfolio’s dual focus on reserve funding and anti-poaching tactics investigates both topics from the perspective of resource management. The portfolio’s two research papers provide analyses that contrast the optimization of a legal financial market with an illicit commodity trade. The investigation of American financial markets in the technical research paper elucidates the need to consider the effective use of the resources being conserved, while the STS paper elucidates the need to conserve resources effectively. Though the technical and STS research projects were not conducted concurrently, completing the technical research portion of the portfolio prior to completing the STS research portion strengthened the analysis of the underlying economic issues in the STS research topic’s wicked problem by providing economic context.
BS (Bachelor of Science)
quantitative finance, wicked problem, algorithmic trading, poaching
School of Engineering and Applied Science
Bachelor of Science in Computer Science
STS Advisor: Bryn Seabrook
Technical Team Members: Rachel Zhang
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