Structured finance modeling in Python & Excel

Quantitative risk management

• Modeled and evaluated the insurance risk of specific truck features via LR/Ridge/KNN/SVM/AdaBoost/Gradient Boosting/Random Forests/Neural Network; profiled the risk structure of a truck pool and calculated the insurance portfolio risk• Cleaned and selected features from data sets of pilots, drones, policies and sensors, assessed the impact and importance of different characteristics of drones and pilots on the severity & frequency of drone claims via data visualization and linear regression

• Researched domestic & foreign investment advisory market and wrote 30 pages’ report, analyzed and compared the performance of 47 competing as well as company's internal fund portfolios of various risk level on different investment platforms• Created a heat index based on the issuance of equity funds and slant model funds to portray the monthly market setiment, back-tested the market timing strategy with long/short signals of the heat index from 2012 to 2020 and achieved a 93% win rate • Computed/tested quantitative indicators in Python to assist fund managers in adjusting their TAA model and making investment decisions• Tracked the equity market and wrote weekly reports in terms of market status, valuation, growth, sentiment, capital supply & demand

• Conducted research on Changshu Automotive Trim(603035.SH): reviewed company and industry history, investigated supply chain & industry competitive landscape, identified Co’s unique moat, projected 5-year future earnings and built valuation models• Forecasted Auto industry post-epidemic comeback by tracking weekly and monthly auto sales & inventories, and recommended Huamao Technology and Changshu Automotive Trim, which achieved 67% and 30% return respectively• Reviewed market performance and monthly trend of tradition auto OEMs, Electrical Vehicle and Luxury auto brand

• Established the Evaluation Index System for 2019 Informatization Assessment of the Chinese Academy of Sciences (CAS) for dimensions of elementary environment, application of informatization and network security• Updated Mysql under Linux system, massively speeded up data collection and processing by Python, and visualized results via Echarts• Applied machine learning and text mining to the classification of scientific research papers and forecast of interdisciplinary development

• Built evaluation frameworks for the macroeconomic, industry development and investment environment of 150 countries by collecting gap index data (including railways, oil & gas, TMT, education) and analyzing 100+ economic indicators• Classified countries’ financial structure (market/bank based) by considering market capitalization/domestic assets of deposit money banks, trading vs. banks, trading vs. overhead costs and other indicators and conducting correlation analysis and PCA to obtain the structure index• Utilized programming in R to convert datasets from different databases to final analysis outputs in batches, and improved the team’s working efficiency by reducing a week’ s work to 10 minutes every time databases update

• Assisted analysts in collating data and industry policies, tracking key changes and building financial analysis models of listed companies covering food and beverage, tourism, transport, computer and public utilities industries• Wrote over 80 stock reviews(including Zhongqing Tourism, Zhongke Shuguang, Daqin Railways, Guizhou Maotai, etc.) to analyze companies’ performance and future development according to their financial reports and industry research reports