Recommender systems are apparent in our lives through multiple different ways, such asrecommending what items to purchase when online shopping, recommending movies towatch and recommending restaurants in your area. This thesis aims to apply the sametechniques of recommender systems on a new area, namely stock recommendations basedon your current portfolio. The data used was collected from a social media platform forinvestments, Shareville, and contained multiple users portfolios. The implicit data wasthen used to train matrix factorization models, and the state-of-the-art LightGCN model.Experiments regarding different data splits was also conducted. Results indicate that rec-ommender systems techniques can be applied successfully to generate stock recommen-dations. Also, that the relative performance of the models on this dataset are in line withprevious research. LightGCN greatly outperforms matrix factorization models on this pro-posed dataset. The results also show that different data splits also greatly impact the re-sults, which is discussed in further detail in this thesis.