School of Business and Management Department of Finance 203 Risk-Management Duel: Financial vs Operational Risk Management Supervisor: Peter MACKAY / FINA Student: LAI Yuen Ying / ECOF LEUNG Hiu Hin / ECOF WANG Zhengjia / FINA XIA Fan / ECOF Course: UROP 3100, Summer UROP 2100, Summer UROP 2100, Summer UROP 3100, Summer Companies often mitigate risk through financial and operational risk management. This project aims to analyse the relation between these risk-management methods, as observed in precious-metal mining companies. At the current stage of the project, the research focuses on financial risk management by studying how companies adjust their hedging strategies and intensities in response to changes in metal prices. Our research team collected data on hedging-related items, including the scope of selective hedging, use of derivative instruments, hedge accounting, and the scope of their hedging activities concerning commodity prices, foreign exchange, interest rates, and energy costs. This report summarises the data collection and data audit processes conducted in summer 2025, highlighting specific findings and the challenges encountered during the collection and audit phases. Risk-Management Duel: Financial vs Operational Risk Management Supervisor: Peter MACKAY / FINA Student: UTAMA Alicia Destiny / GBUS ZHANG Xuanrui / MGMT Course: UROP 1000, Summer UROP 1100, Summer This project focuses on analyzing financial and operational risk management activities of gold-mining companies. In the current stage of the project, a comprehensive database containing over 20 years of production data from 31 gold-mining companies has been established. An auditing team, consisting of two UROP participants, has been tasked with identifying discrepancies and areas for improvement within the database to enhance its accuracy for future analyses. This report summarizes the team’s auditing efforts, highlighting the issues encountered during the process and the proposed solutions to address these issues. The findings aim to ensure the reliability of the database, ultimately supporting more effective risk management assessments in gold-mining companies. Applications of Machine Learning to Financial Data Supervisor: NOH Don / FINA Student: BI Zihao / ECOF XIE Hangcen / MATH-FAM Course: UROP 1100, Fall This undergraduate research project replicates the study of overnight-intraday return gaps in the Korean stock market. Our panel regression analysis shows that a 10 percentage point increase in Retail Trading Proportion (RTP) corresponds to a 6.69 basis point rise in the return gap. Using log shares outstanding as an instrument, our 2SLS analysis establishes a causal relationship between RTP and returns, with effects stronger than OLS estimates, suggesting potential downward bias due to arbitrage activities. We document distinct trading patterns, including U-shaped volume distribution and systematic differences in buying behavior between market open and close. Building on these findings, we propose future research incorporating LSTM neural networks to enhance trading strategy development.
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