LINC

In this project supervised by OQAM, the analysts Olivia Lahtinen, Behdad Nikfarjam, and Douglas Eklund explore whether machine learning techniques can uncover pricing inefficiencies in the Nordic covered bond market. Using a Random Forest model, they aim to predict movements in five-year Swedish covered bond yields.

In this report, analysts Lisa Landin, Samuel Lerjestad and Angelo Sun investigate whether there are ways for companies to optimise their foreign-currency management using FX swaps. Using FX swap pricing data, interest rates for each currency and synthetic account balances, they simulate the benefits of FX swaps in different scenarios and determine optimal transaction decisions.

In this project supervised by OQAM, the analysts Dag Palmstierna, Denis Zernov and Magnus Drøyvold explore the existence and predictability of overnight reversals in the European high-yield bond market. By using a Random Forest machine learning model, they were able to predict significant share of overnight reversals in the European high-yield bond market. This supports the hypothesis that such reversals are not only present in equities, but also extend to credit markets.