Seiji Sasaki

For this project, I worked with a graduate student at the Duke Law School to perform exploratory analysis on loan backed fine art. As a topic that has minimal prior research, this project was particularly interesting as we had to create and collect out own datasets and provided original insight in the topic.  Through our analysis, we were able to find that collateralized works tend to sell for a higher hammer price and that the volume of art backed loans negatively correlates to the housing market. This project demonstrates the following technical skills: - Collecting and cleaning data - Advanced statistical modeling - STATA - Excel - Working with financial time-series data

In this project, I worked with a group of three other students with the goal to find arbitrage opportunities between the New York, London, and Paris art markets. We developed two models; a multivariate regression model to predict the hammer price and a logistic regression model to predict the likelihood of a sale.  This project demonstrates the following technical skills: - Collecting and cleaning data - Advanced statistical modeling - Testing statistical models - Machine Learning - R - STATA - Excel

For this project, I set out to understand how changes in Facebook's advertisement algorithm impacts facebook's stock prices. In addition to Facebook, four other stock ticker and the S&P index were used to tease out any larger market and industry price changes. Using a RDD model, I was able to find two significant algorithmic/acquisitional updates that impacted facebooks stock prices in a statistically significant manner.  This project demonstrates the following technical skills: - Collecting and cleaning data - Advanced statistical modeling - MATLAB - Excel - Working with financial time-series data please click here or the below link for the full Google Drive Slides https://docs.google.com/presentation/d/1YYNv3wCx7a-qx0ruth1E_LsoAwWioZqY/edit?usp=sharing&ouid=107570216653474841263&rtpof=true&sd=true