Bayesian Networks (BNs) are a useful graphical probabilistic structure for visualizing and understanding the dependencies of random variables. In this study, July 15 coup attempts' effects on Turkish Financial Market are analyzed with the BN approach. To this end, 31 Istanbul Stock Exchange (BIST) return indexes and seven foreign exchange rates (CNY, EUR, GBP, JPY, SAR, RUB, and USD) from year-to-September 30th of 2016 are examined. BN structure is learned (predict) via Greedy Thick Thinning algorithm with K2 prior from the dataset and is expertized. BN model is validated and trained from real dataset instead of generated data from the established model. The BN is called Trained Bayesian Network (TBN) model. TBN is validated and the beliefs of TBN are updated again by dataset via learning parameters with Expectation Maximization (EM) algorithm. BNs have not before been used to relate the presence/absence of BIST return indexes with foreign exchange rates. Accuracy rate (AUC) of the TBN model to generating the real data is calculated as 85.5% percent. TBN model has simplified the Market relations with conditional probability.