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An enormous ripple effect can occur in financial data mining if it accurately predicts stock prices. However, predicting stock prices using only stock price data is difficult because of the random nature of stock price data. This paper attempts to fuse data to solve the stock price prediction problem. The following data affecting the stock price are added to the proposed method as an additional modality: macroeconomic indicators and the months and day of the week. The multimodal early fusion method is used, which learns the intermodality correlation of features. The proposed model in this paper outperformed the comparison models and achieved statistically significant results. Specifically, 27 out of 50 stocks achieved higher classification accuracy than the comparative model. In addition, the in-depth analysis indicates that the early fusion strategy achieved better classification accuracy in 30 of 50 datasets than the late fusion strategy for stock price prediction.

Effective Exploitation of Macroeconomic Indicators for Stock Direction Classification using the Multimodal Fusion Transformer