Kai Lun Ho
Class of 2023
Cracking the financial markets is a puzzle that has captivated me for nearly a decade. Since my father introduced me to trading, I have been fascinated with finding a systematic way to beat the market. Over the years, I experimented with a multitude of approaches, from rules based on “technical indicators” to systematizing the Benjamin Graham style of fundamental value investing. When I first came across the field of Machine Learning and Data Science, I immediately saw its tremendous potential to develop an edge from troves of financial data. That sparked my interest and desire to develop my skillsets in this field, which I have applied in internships and projects.
Over the summer, I interned as a Quantitative Research Intern in Worldquant, focused on building Alpha signals for Statistical Arbitrage. I worked on projects involving automated factor mining, deep learning and natural language processing, yielding Alpha signals that we successfully productionized. I also have internship experience with Machine Learning on large datasets, building data and recommender pipelines for Smule, the biggest social network for music. Most recently, I came in 2nd in the 2021 Citadel Data Open APAC, where I led my team to develop a betting strategy for soccer matches.
I am pursuing a Masters of Science in Computational Finance at Carnegie Mellon University to further hone my Data Science and Quantitative Finance skillsets. Before MSCF, I graduated with highest distinction from the National University of Singapore with a Bachelor’s in Industrial and Systems Engineering and a Minor in Computer Science. After graduation, I plan to pursue a career in quantitative research and trading.