Developed mean reversion trading strategy for stocks by ARIMA and ML algorithm to identify profitable opportunities through robustness and sensitivity analysis, leveraging statistical arbitrage to increase portfolio returns by 9%Built a Fama-French model based on factor model to forecast the performance of target stocks, adjusting portfolio returns with value factors, size factors and market risk premiumConducted 10 years of backtesting and 5 years of forward testing using historical and real-time data, ensuring statistical robustness. Achieved an 8% annualized return with a Sharpe ratio of 1.5 through a long-short equity strategyConstructed machine learning models for market prediction and regime detection, including Bayesian models by using historical data to predict return distribution, enhancing portfolio performance with sentiment analysis, turnover ratesCollaborated to optimize execution algorithms, reducing trading costs by 20% and improving market impact management

Sole valuated 100+ assets including housing and office building, matching investors to issue ABS and loans to $2 millionPrepared comprehensive pitchbooks and client presentations, providing industry insights, financial projectionsConducted due diligence on real estate investments, including industry, equity research, cash-flow health assessmentLed property acquisitions and financing projects, handling DCF, MBS, generating $500,000 in management feesDeveloped long-term investment strategies, optimizing portfolio diversification achieving $1.2 million in profits

•Analyzed and predicted financial performance with trend analysis, common-size, WACC and DCF•Assisted with annual budgeting and monthly forecasting process

•Built and evaluated asset allocation techniques including Mean-Variance, Monte Carlo simulation, Black-Litterman model• Performed outlier analysis, correlation analysis and WOE calculation of credit scores•Built logistic regression model for credit default risk •Balanced cash flow statement, balance sheet and income statement•Constructed lender model to benchmark the pro forma against the status quo

•Valued 12 pharmaceutical companies, 8 innovative medical device companies using DCF, PS, and PE methods•Identified investment risks of innovator medical devices and pharmaceuticals•Retrieved six-year financial reports, prospectuses, and announcements to establish valuation models and peer comparisons •Employed relative valuation methods, extract data on competitor company attributes to identify competitive advantages•Estimated Random Forest and gradient-boosted decision trees to predict own and peer firm performance metrics

•Conducted analysis on 100+ construction projects, producing a 96-page rail transit industry trends report•Utilized PivotTable, V-Lookup for data analysis of 400+ meetings, including case studies on Canary Wharf and Oculus•Applied analogy to cut costs by 200 million RMB, outlining construction and operation plans and project framework•Analyzed 30+ third-party payment policies for One Wallet project, assessed market trends and competitive strategies•Developed web crawler with BS4 and Grequest for data collection•Used R for customer profiling and predictive modeling, supporting a detailed report on digital transformation

•Analyzed gold and gold stock prices using Generalized Linear Regression •Tested stationarity and cointegration, using ECM and VAR for future price prediction •Performed time series analysis on client stock prices using an ARIMA model with exponential smoothing•Integrated monetary policy and inflation in multifactor stock price model to estimate 95% confidence intervals •Refined and enhanced predictions using Random Forest and Neural Network approaches