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This paper presents a benchmark for predicting institutional equity holdings using temporal graph machine learning, framing it as node affinity prediction on a bipartite graph. The proposed NAVIS model achieves state-of-the-art NDCG of 0.9127 on a dataset from SEC Form 13F filings.
A tweet highlights Stanford professor Stephen Boyd's free convex optimization course and textbook, noting that Citadel pays $400K for this skill. The course teaches optimal portfolio allocation, but emphasizes that the optimizer only works with a genuine edge in signals.