Author: <span>stem</span>

Reinforcement Learning for Bond Allocation in Global Pension Fund Portfolios

In this project, supervised by Tredje AP-fonden (AP3), the analysts Edvin Gunnarsson, Victor Mikkelsen and Carolina Oker-Blom explore whether Reinforcement Learning (RL) can optimize global bond allocations by dynamically balancing duration and credit risk. Using the Proximal Policy Optimization (PPO) algorithm combined with PCA-based dimensionality reduction, they develop an agent designed to adapt to shifting market regimes and complex macroeconomic conditions.

What price will Bitcoin hit in 2025?

In this project, analysts Carl Nordahl, Alexander Ihrfelt and Alfred Hultgren investigate whether crypto prediction market contracts can be valued using traditional derivatives pricing methods. Focusing on Polymarket contracts tied to the question “What price will Bitcoin hit in 2025?“, they model the contracts as one-touch barrier options and compare a closed-form approximation with a Monte Carlo framework using EWMA volatility and jump-diffusion dynamics.

Portfolio Optimization and Shrinkage

In this project, supervised by AP3, analysts Alexander Nilsson, Peder Persson, and Samuel Eriksson investigate whether Mean-Variance Optimization (MVO) can be enhanced by shrinking the off-diagonal elements of the covariance matrix. Utilizing a 20-year dataset, they backtest models incorporating spectral filtering and shrinkage techniques to assess the robustness and practical feasibility of the optimization process.

Regime-Dependent Macro Exposures in Emerging Market Bond ETFs

In this project, supervised by OQAM, analysts Vilhelm Hilding, Jacob Fransson and Dag Vallien investigate how global macro conditions drive the returns of two emerging market bond ETFs: EMB (USD-denominated) and EMLC (local-currency). Using a rolling linear factor model, they estimate time-varying exposures to equity markets, US dollar strength, interest rates, credit sentiment, commodities, and volatility across a 10-year period.