Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Diversification-Enhancing Contrastive Learning

arXiv:2404.07223v1 Announce Type: cross
Abstract: In complex financial markets, recommender systems can play a crucial role in empowering individuals to make informed decisions. Existing studies predominantly focus on price prediction, but even the most sophisticated models cannot accurately predict stock prices. Also, many studies show that most individual investors do not follow established investment theories because they have their own preferences. Hence, the tricky point in stock recommendation is that recommendations should give good investment performance but also should not …

arxiv cs.ai cs.lg diversification graph individual investors investors network q-fin.st recommendations stock temporal type