In this investigation conducted with OQAM, the analysts Jaroslavs Grigoluns, Merriea Mathew, and Elliot Koujaharov Sjögren, analysed which factors drive the spread differences of bonds issued in both EUR and SEK. By examining a large number of both company-specific and macroeconomic features with different machine learning methods, the project distilled the most influential contributions to the historical spreads.
In this project supervised by SEB, the analysts Markus Lexander, Paulina Ibek, and Philip Mattisson investigate factors that predict the exchange rate of the Norwegian Krone, EURNOK. By employing ML techniques such as feature importance and random forest in conjunction, the investigation focused on the predictive power of variables such as the price of the Brent oil, EURSEK, the DAX, among others, on the future price of NOK on various time horizons.
The results indicate that different combinations of the selected features have the best predictive power on different time scales. The Martingale model theory of price movements was found to hold better on short (minutes) timescales, while Brent-price driven trends dominated on longer (days) time horizons.
The developed model showed promising results and predictive abilities regarding a company’s credit risk. However, the limitation of the dataset to the Nordic market significantly impacted the predictive power of the model. While the hypothesis remained unchallenged by the investigation, the need for a wider scope of considered companies is evident.
In this report supervised by OQAM, the analysts Niklas Gälldin, Pontus Neumann, and Oscar Näslund Cuesta explore the possibility of predicting public Nordic companies’ credit ratings using machine learning models. By utilizing historical quarterly financial data, stock data, and Moody’s historical ratings, the hypothesis is that changes in credit ratings can be predicted ahead of their publication, providing a competitive edge.
The developed model showed promising results and predictive abilities regarding a company’s credit risk. However, the limitation of the dataset to the Nordic market significantly impacted the predictive power of the model. While the hypothesis remained unchallenged by the investigation, the need for a wider scope of considered companies is evident.
In this report, quantitative analysts Victor Ritseson, Balasurya Sivakumar, and Théodore Zitouni used machine learning and traditional trend strategies with sentiment analysis to predict price movements.
In this report, analysts Jaroslavs Grigoluns, Marko Malling, Noah Åkesson will build on a preceding R&A report and further explore the relationship between share turnover and momentum for the S&P 500 universe. This will be done through the implementation of additional strategies and the incorporation of portfolio optimisation practices.
Quantitative analysts Duy Pham Nguyen, Niclas Wölner-Hanssen, and Hannes Brinklert investigate texts from the social media platforms Reddit and Twitter if they could generate trading signals that result in successful trades
In this report, quantitative analysts Emil Damirov, Ian Wallgren, and Tilde Vidman investigated intraday momentum effects surrounding interest rate announcements by the Federal Open Market Committee (FOMC). The analysis was performed on S&P 500 Contracts For Difference(CFDs) data ranging from 2012 up till 2020 with a fifteen-minute resolution.
In this report, quantitative analysts Victor Ritseson, Balasurya Sivakumar, and Binh Minh Tran researched and compared traditional trend strategies to machine learning when trading future contacts on commodities.
In this paper, analysts Emily Sundqvist, Doris Rivadeneira, and Lazarin Lashkov provide an extended analysis of skewness strategy, applied to commodity future prices.
In this report, quantitative analysts Michael Barasciutti, Veronica Larsson, Marko Malling and Noah Åkesson explore the idea that a positive relationship can be located between share turnover and momentum.
