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Yinan Value Investing & Financial Modeling Externship

Gain insider experience in value investing by analyzing leading stocks alongside seasoned investor Yinan Zhao. Learn how professionals leverage financial modeling in Excel, interpret alternative data, evaluate market trends, and craft persuasive investment recommendations. Build real-world skills essential for careers in finance, equity research, and investment banking.

Predictive Consumer Analytics & Dashboarding

Created a predictive model in R for consumer behavior trends of a retail business, boosting baseline demand forecast accuracy by up to 50%. Designed interactive Tableau executive dashboards to translate complex statistical trends into intuitive visual narratives for non-technical proprietors, directly driving strategies that increased projected monthly customer conversion by 20%.

Solar Activity Data Analysis

Processed 33,000+ unstructured data points, identifying an 11-year sunspot cycle using Python (NumPy/Matplotlib). Reduced pattern detection RMSE by 35% by implementing polynomial regression and neural networks (TensorFlow). Captured nonlinear patterns linking sunspot activity to geomagnetic indices by extracting highly accurate predictive signals (±1% uncertainty) from a noisy, unstructured dataset.

Enterprise Database Refinement

Extracted and manipulated an unorganized dataset containing a catalog of 10,000+ consumer products using SQL environment. Engineered algorithm enhancements across enterprise databases systematically reducing catalog query execution times from approximately 5.0 seconds to 0.01 seconds. Revised the back-end search algorithm achieving a 95% precision in expected sorted-by-relevance query results.

Solving Black-Scholes Model to Simulate European Options

Discretized the Black-Scholes PDE with 4th-order-accurate finite differences in price and integrated in time using a self-implemented adaptive RK45 (Cash-Karp) solver. Achieved mean relative error of 0.01% (max 0.17%, R² > 0.999) against the closed-form Black-Scholes formula across a 100-point price grid.

Northern Lights Forecast Model

Processed 33,000+ unstructured data points, identifying an 11-year sunspot cycle using Python (NumPy/Matplotlib). Extrapolated non-linear relationships between sunspot activity and geomagnetic indices, cutting pattern detection RMSE by 35% using polynomial regression to forecast auroral activity with high predictive precision.