School of Business and Management Department of Economics 175 Market Expectations and RMB Exchange Rate Policies Supervisor: LU Yang / ECON Student: PAN Xilei / ECOF TANG Yuqian / ECOF XIAN Huiwen / QFIN ZHOU Shihong / ECOF Course: UROP 1000, Summer UROP 1000, Summer UROP 1000, Summer UROP 1000, Summer We investigated the correlation between Chinese monetary policy changes and market expectations on the USDCNY exchange rate. We used CPRS to represent policy changes and survey data, as well as USDCNY option prices, to represent market expectations. We found that the magnitude of change in market expectation and Chinese monetary policy is positively correlated. However, in survey data, there isn’t evidence for significant correlation between the directions of market expectation and CPRS. We put forward three hypotheses (cross-section effect, cross-time effect or cross-feature effect) to test if there’s potential correlation between the directions. Through investigation, we found that the cross-feature effect might hold. In the option market, CPRS is significantly correlated with expectation change, while such correlation shows differently under different conditions. Understanding and De-biasing User Media Consumption Supervisor: LU Yang / ECON Co-supervisor: HAGMANN David / MGMT Student: AI Yuhan / ECOF CHUN Tin Wai / ECOF GAO Yifan / MAEC TANG Yuqian / ECOF Course: UROP 2100, Fall UROP 2100, Fall UROP 1100, Fall UROP 2100, Fall Building upon the conclusion of the summer 2023 UROP project, professors launched the experiment platform on the DeBubble platform and commenced data acquisition throughout the semester. Five students helped to examine and analyse the data obtained from the trial experiment. This involved performing data cleansing, analyzing and visualizing the data, and facilitating effective communication. Additionally, to prepare the system for a larger cohort of participants, we carried out a traffic test. Looking ahead, professors will engage a greater number of participants and conduct multiple iterations of testing. The insights gleaned from this endeavour will prove valuable in comprehending readers' perceptions of bias, as well as predicting, categorizing, and mitigating bias.
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