Financial Analyst Data

Kai “Shawn” Xu

240-***-**** ● **** Stream Bank Lane., Room 1-4 Greenbelt MD 20770

● ***.**@*************.***.***● www.linkedin.com/in/kaixusmith EDUCATION

Robert H. Smith School of Business, University of Maryland College Park, MD Master of Quantitative Finance

• Expected Graduation Date: May,2021

University of Science and Technology of China Hefei, China Bachelor of Science, Major: Statistics June 2019

• University of Science and Technology Scholarship 2000 RMB 2015,2016,2019

• Relevant coursework: Probability Theory, Regression Analysis, Multivariate Statistical Analysis, Statistical Software, Mathematical Modeling, Data Structure and Database RELEVANT PROJECTS

Forecast of Stock Return:

• Dealt with a database with 500 thousand observations and applied several data manipulation methods like moving average, polynomial transformation and shifting back to set up.

• Adopted several factor selection methods like principle component analysis and random forest to choose the most significant variables which will affect the results.

• Applied machine learning methods like neural network, elastic net and random forest to forecast the return. Risk Analysis of Healthcare Industry:

• Accomplished data extraction and cleaning process by R and Python, thus excluding the potential risk factors that will make our fitting results volatile.

• Adopted Binary Logistic Model to study the relationship between osteoarthritis and heart disease in Canada and analyzed the interactive effects between osteoarthritis and other risk factors like gender, age and region.

• Realized data visualization using Tableau and Python, including scattering pattern and correlation heatmap.

• Made marketing schemes for healthcare companies in Canada about how to take steps to decrease the risk of heart disease among osteoarthritis patients.

EXPERIENCE

ESG Experiential Learning Program with Hydrus.AI San Francisco, CL Data & Financial Analyst July - August 2020

• Studied ESG factors in environmental, social and governmental dimensions and constructed a dynamic ESG monitoring systems. Used text mining techniques on AWS JUPYTER notebook to extract 10-K files data from online Edger database to distinguish real ESG companies from greenwashing companies.

• Applied logistic regression and PCA techniques in Python to study how ESG factors affect financial performance in the short term and long term. Applied cluster analysis to select industry-related and industry-unrelated factors. SEMA Software Internship Baltimore, MD

Business Analyst July - August 2020

• Made characteristics segmentation model using Python and customer data. The whole target is to categorize different developers based on their geographic and psychographic characteristics, making it more effective to evaluate and hire software developers.

• Used companies’ Jira data set to construct a Regression Forest “Story Point” model, whose function is to supervise each project’s progress through estimating the whole work load and comparing it to the actual time spent. Credit Risk Experiential Learning Program with Fannie Mae Washington, DC Data Analyst September - October 2020

• Extracted data from Fannie Mae’s public website and established logit model for credit risk. In the process, both delinquency and prepayment rate are considered and spline regression is used to improve prediction accuracy.

• Machine learning methods like random forest and XG Boosted are used to better imply the credit risk for mortgage loans. Also, cluster analysis is used to study different effect from categorical features like property type. DISTINCTIONS

• Technical: Microsoft Office; Python; C; MATLAB; R; MySQL; ESG investment; Segmentation Analysis; Text Mining.

• Languages: Native in Mandarin; Fluent in English

Contact this candidate