The stock market has been extensively studied as one of the important research areas of economics and finance. In particular, research on analyzing and predicting stock markets based on stock price and rate of change data is the most active topic in the financial sector, and specifically, predicting stock prices and markets as a whole is one of the important factors for investors to establish optimal investment strategies. In this study, a causal network was constructed based on the information flow of major financial market indices using the concept of transfer entropy. In addition, the financial market was analyzed using the configured network, and the predictive power of KOSPI's fluctuation could be almost maintained using a cluster-based smaller number of whole data when predicting the global financial market index based on the fluctuation of information flows. To conduct this experiment, transfer entropy, which measures the amount of reduction in information uncertainty, was used as an indicator of measuring causal relationships. Specifically, an information flow network was constructed using efficient transfer entropy, an effective indicator in adjusting the finite size effect that may occur when measuring transfer entropy. In other words, we analyzed the causal relationships between global financial indices and predicted KOSPI fluctuation using effective transfer entropy. As a result, it was confirmed that the financial market index could be analyzed using a causal network using efficient transfer entropy, and the improved prediction results could be confirmed using fewer data columns in predicting fluctuations in the domestic financial market using the configured network.