Skip to main content

About Me

Seiji Sasaki is a recent Duke University graduate (class of 2020) with a B.S. in Economics with a concentration in Finance. He is currently a full time high net worth property and casualty insurance Underwriter working for a multi-national company out in Chicago, IL. 

During his time at Duke, by taking on classes such as Econometrics, Forecasting Financial Markets, Intermediate Statistics, Linear Algebra, and Multivariable Calculus, he has gained a strong understanding of math and statistics. Through his prior work experience as a data intern at StrongKey and at Variant Perception, he has gained the technical expertise to leverage tools such as R and Tableau to visualize data in an understandable and actionable manner.

Comments

Popular posts from this blog

RDD analysis on time-series financial data

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

Linear and logistic regression analysis to find arbitrage opportunities in art markets

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

Classifying profitable soccer players

Overview In this project I set out to create a classification model in order to predict key characteristics where soccer players are expected to show significant market value growth in the short to mid-term future. I relied on data available on Transfermarkt.com, a European soccer player market valuation database/website used by soccer clubs around the world.  Data Collection and Cleaning In order to collect the relevant data, I developed a scrapping program using Beautiful Soup on Python. After scraping all of the necessary data, I used python to remove any null values and standardized all data types. After collecting and cleaning the data through Python. I was finally ready to perform preliminary data exploration to find any key trends of themes in the dataset.  Exploratory Data Analysis Size = Change in market value, X = age, Y = Market Value From an initial review of the dataset, I found that there was most likely a strong relationship between the age of the player and expected mar