Project Experience is a very important part of our student training. Our students works full time during the summer on industry-originated projects under the supervision of industry mentors. In addition to that, our students work on varies course projects during regular semesters.

The purpose of our projects is to give students some first hand working experience on solve real problems with the technical skills they have learned in classrooms. Most projects involve real time data, and students can practice and improve their skills on data cleaning, data analysis, model developing and model validations. Students use varies programming languages such as Python, SAS, R, etc.

Summer projects and course project are usually followed by a closing workshop at which students present their final results. Besides project industry mentors, professionals from industry are invited to the closing workshops and they give evaluations and advice to our students on their results as well as their presentation and communication skills.

Here are some selected student projects in recent years.

  1. Summer projects (11 weeks, full time)
    • Interest rate and Prepayment models for Mortgage-Backed-Securities
    • Hazard Rate PD Models and LGD Models on Fannie Mae Mortgage Loans
    • PD Models using Transition Matrix on Fannie Mae Mortgage Loans
    • Pair Trading with the OU Process and Algorithmic Trading
    • Optimal Hedge Ratio for Mortgage Portfolio
    • Natural Language Processing – Sentiment Analysis
    • Algorithmic Trading and the Durability of Published Financial Modeling
    • Consumer and Small Business Credit Risk Modeling Mortgages
    • Interest-Rate Derivatives Models: Design, Development, and Validation
    • Quarterly Model to Forecast VIX Index

  2. Semester Course Projects
    • PD and LGD Modeling under the CECL Framework
    • Stock Price Forecasting using Machine Learning, time series and deep learning techniques
    • Neural Network and Its Application to Financial Data Analysis
    • Portfolio Investments/ Algorithmic Trading with Jump Diffusions
    • Bitcoin Futures and Volatility Surface
    • Sentiment Analysis for Event Driven Stock Price Prediction
    • Portfolio Management and Investor Intuition: The Black-Litterman Model
    • Stock Price Prediction Using Deep Learning
    • Identify Optimal Delta Hedging Interval for a Structured Notes’ Options Portfolio
    • Credit Event Models on Single Family Mortgage Loans
    • VaR models: A Comparative Study
    • Impact of Disaster Events on Investments via Financial Contagion
    • Probability of Default Model with Transition Matrix
    • Interest Rate Model and Fixed Income Product Pricing
    • Estimating Loss Given Default (LGD) with a Two-Stage Model
    • Loss Severity Analysis on Single Family Mortgage Loans