[2F4-GS-10h] AI応用：経営と経済

[2F4-GS-10h-05] Forecast of Economic Indicators using Causal Information

〇Kiyoshi Izumi¹, Hiroki Sakaji¹, Hitomi Sano¹ (1. The University of Tokyo)

Keywords:Natural Language Proccesing, Economic Analysis, Causal Information, Text mining

In this paper, we propose a method that uses causal information extracted from economic texts to predict numerical indicators related to economic and financial fields, such as macroeconomic indicators and stock prices. The proposed method automatically detects whether each sentence in the economic text contains causal information or not, and if it does, it identifies the cause and effect expressions and stores them in our economic causality database. Furthermore, the proposed method calculates the similarity between the result expression of causal information contained in the economic causal database and the causal expression of another causal information, and generates causal chains from the given text data. Causal chains are used to predict how the numerical values of economic indicators will change in the future due to spillover effects.