My name is Pontus and I'm a 1st year MSc-student in Financial Mathematics at KTH - Royal Institute of Technology with previous exchange studies at National University of Singapore (NUS) and a completed BSc in Industrial Engineering & Management from KTH. Selected coursework include Portfolio Theory & Risk Management, Quantitative Finance, Machine Learning, Language Engineering, Probability Theory, Calculus, Applied Computer Science, Regression Analysis, Game Theory and Markov Processes.

Previous professional experience includes Visiting Associate @ BCG Platinion (tech/IT-strategy consulting internship), Junior Quantitative Analyst @ Söderberg & Partners building and maintaining quantitative models for money market, fixed income and equity funds using Python (pandas, numpy, scikit-learn), SQL and VBA. I've also worked as Intern/business developer @ If P&C Insurance in commerical pricing where I analyzed claims data to assess, model and price commerical property risk using SQL & SAS as well as building dashboards for portfolio follow-up in PowerBI using DAX and PowerQuery.

• Programming experience in Python, SQL, VBA and R with some experience in MATLAB, JavaScript and C/C++.
• My main areas of interest are 📊Quantitative Finance, 💻Software Engineering, ⚙️Statistics as well as 🤖Machine Learning & AI.

💡 Click the links to explore my previous projects within different areas and coding languages!

Bachelor Thesis in Mathematical Statistics - KTH Royal Institute of Technology
Authors: Pontus Hovberger & Hugo Brunlid

In this thesis we explored the kurtosis of momentum, and 'momentum crashes' both from 2008 as discussed in previous research but also in the light of recent covid-19 stock crash. This is done both using a singlefactor model split by Morningstar Category (based on style and size orienation) and also a multifactor model that in addition to momentum (excess past returns) incorporates size orientation, style orientation and management fee are used to predict excess future returns.

Left picture: Average yearly excess returns for each decile (2000-2023)
Right picture: Average yearly excess returns for each decile (1962-1987) (Carhart, 1997)

Findings of predictive power and longevity of momentum by Carhart are still true for US Equity funds 2000-2023 although impact of size & style factors have significantly changed since Carhart Four-Factor model was first presented in 1997.

• 💻 Languages: Python (pandas, numpy, scikit-learn, jupyter notebook)
• 🔑 Keywords: Momentum, Carhart Four-Factor Model, Multifactor Model, Momentum Crashes

In this project I have implemented different algorithms (bruteforce, backtracking, candidate checking, placefinding, and Crook's algorithm) for solving sudokus to test both their speed, accuracy, and solving ability. This is done both in Python and C to build the most efficient and quickest algorithms. While bruteforce and backtracking algorithms are fast and reliable to solve all sudokus, they have lower accuracy (a lot of wrong tries before reaching the correct solution). Instead, more human-like algorithms such as the pen-and-paper based Crook's Algorithm can perform on similar level in terms of speed and solving ability but with perfect accuracy (only inputting a number if it is correct).

• 💻 Languages: Python, C
• 🔑 Keywords: Bruteforce, backtracking, candidate checking, placefinding, and Crook's algorithm

• 📝 CV template CV template in LaTeX to create well-formatted and modular CV (add and order experiences without need for reformatting)