Speaker Classification Model on BOJ's Summary of Opinions for Forecasting Their Policy Change

Abstract

In this paper, we introduce several types of text classification model that predict the speakers on BOJ's "Summary of Opinions". Those documents summarize BOJ's Monetary Policy Meetings and the major part of the contents consist of the committee members' opinions, but those comments are kept anonymous. Regarding this issue, the commentator prediction model should be a great help for focusing the next decision of BOJ. Our models are trained with the past public speech texts of BOJ committees. In order to correct the bias of datasets, we tried some data pre-processing before model fitting. The proposal models are Random Forest, LSTM and Bidirectional LSTM with attention mechanism. As a result, we achieved over 90% accuracy for the best. To applying those models to the analysis of BOJ's "Summary of Opinions", we focus on the relationship between the ratio of presumed speakers in the documents and past monetary policy changes. The analysis revealed that the higher ratio of "Reflationist's" assertion raises the chance of monetary easing occurrence.