Causal inference from financial factors: Continuous variable based local structure learning algorithm

For identifying the interrelationships of financial factors, we present a local structure learning based framework for Bayesian networks (BN) discovery from a large amount of continuous financial data without making parametric assumption. First, the skeleton of BN structure is learned by finding the parent and child set of each variable. Second, to direct the edges, the v-structures are learned by finding the spouse set of each node. To make the algorithm more useful to practitioners, our previously developed two-step accelerated method is incorporated into each step of local learning. Empirical studies on 56 US financial factors show both the efficiency and the effectiveness of our method.