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

School of Engineering, The University of Tokyo, Tokyo 113-8654, Japan
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the 7th International Conference on Smart Computing and Artificial Intelligence, SCAI 2019, Toyama, Japan, 7–12 July 2019.
J. Risk Financial Manag. 2020, 13(4), 79;
Received: 9 January 2020 / Revised: 11 April 2020 / Accepted: 15 April 2020 / Published: 19 April 2020
(This article belongs to the Special Issue AI and Financial Markets)
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. View Full-Text
Keywords: uncertainty; economic policy; text mining; topic model uncertainty; economic policy; text mining; topic model
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Yono, K.; Sakaji, H.; Matsushima, H.; Shimada, T.; Izumi, K. Construction of Macroeconomic Uncertainty Indices for Financial Market Analysis Using a Supervised Topic Model. J. Risk Financial Manag. 2020, 13, 79.

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