Hi, Welcome to my website
Hope you enjoy your time visiting my works
and be my guests to share your thoughts via my contact
About Me
👋 Hello there! I'm Trang Ton
I recently completed my Master's degree in Economics and Institutions with a focus on Finance and Accounting at Marburg University. I have a growing interest in data-driven research, especially in areas like behavioral economics, financial risk, and macroeconomic analysis. My academic experience includes working with tools like Stata, R, and Python at an intermediate level, and I enjoy learning by doing, particularly through research projects, data exploration, and practical applications.
I created this website not only as a portfolio to document my research and projects but also as a space to share useful coding resources, research insights, and learning materials that others may find helpful. It’s also a way for me to keep developing my skills over time. Please note, I’m not a professional programmer, everything here is based on what I’ve learned through study and personal exploration. I welcome constructive feedback, thoughtful comments, and open discussions. I believe we can always learn more together.So grab a cup of coffee, dive in, and let's embark on an exciting journey of data-driven discoveries together!
My Toolbox
Here is an overview of the tools and concepts I’m currently learning and those I’ve worked with during my academic and project experiences. This section reflects an ongoing journey—some areas I’m still exploring, others I’ve applied in research settings.
Currently Exploring ...
Previouly Applied ...
Stata & Python – data processing and regression analysis
Econometric Modeling – empirical models and interpretation
Projects
This section features a range of Quantitative Research projects conducted primarily during my Master's studies, alongside ongoing personal work that will be updated over time. The projects are grouped into four themes: Behavioral Finance, Behavioral Economics, Financial Risk, and Other Topics. Some were developed in collaboration with fellow students, and most are summarized on GitHub.
Due to data confidentiality, detailed datasets cannot always be shared, but key insights and results are provided where possible.