Construction of Macroeconomic Uncertainty Indices for Financial Market Analysis Using a Supervised Topic Model

Kyoto Yono, Hiroki Sakaji, Hiroyasu Matsushima, Takashi Shimada and Kiyoshi Izumi
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Kyoto Yono: School of Engineering, The University of Tokyo, Tokyo 113-8654, Japan
Hiroki Sakaji: School of Engineering, The University of Tokyo, Tokyo 113-8654, Japan
Hiroyasu Matsushima: School of Engineering, The University of Tokyo, Tokyo 113-8654, Japan
Takashi Shimada: School of Engineering, The University of Tokyo, Tokyo 113-8654, Japan
Kiyoshi Izumi: School of Engineering, The University of Tokyo, Tokyo 113-8654, Japan

JRFM, 2020, vol. 13, issue 4, 1-18

Abstract: The uncertainty in the financial market, whether the US—China trade war will slow down the global economy or not, Federal Reserve Board (FRB) policy to increase the interest rates, or other similar macroeconomic events can have a crucial impact on the purchase or sale of financial assets. In this study, we aim to build a model for measuring the macroeconomic uncertainty based on the news text. Further, we proposed an extended topic model that uses not only news text data but also numeric data as a supervised signal for each news article. Subsequently, we used our proposed model to construct macroeconomic uncertainty indices. All these indices were similar to those observed in the historical macroeconomic events. The correlation was higher between the volatility of the market and uncertainty indices with larger expected supervised signal compared to uncertainty indices with the smaller expected supervised signal. We also applied the impulse response function to analyze the impact of the uncertainty indices on financial markets.

Keywords: uncertainty; economic policy; text mining; topic model (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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