A Twitter-Based Economic Policy Uncertainty Index: Expert Opinion and Financial Market Dynamics in an Emerging Market Economy

Yeşiltaş, Sevcan; Arslan, Beyza; Altuğ, Sumru

doi:10.3389/fphy.2022.864207

Cited by 5 publications

(2 citation statements)

References 56 publications

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“…The core strategy is to filter out articles containing the keywords "economics", "policy", and "uncertainty" from selected newspapers using computer text mining technology, and then calculate the EPU index through normalization processing. This index, originally calculated for the United States and 11 other countries by Barker, has been widely accepted and subsequently generalized to different economies by various researchers [3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning

Rao

Wei

et al. 2023

Mathematics

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In this work, we integrate the conventional unsupervised machine learning algorithm—the Principal Component Analysis (PCA) with the Random Matrix Theory to propose a novel global economic policy uncertainty (GPEU) index that accommodates global economic policy fluctuations. An application of the Random Matrix Analysis illustrates the majority of the PCA components of EPU’s mirror random patterns that lack substantial economic information, while the only exception—the dominant component—is non-random and serves as a fitting candidate for the GEPU index. Compared to the prevalent GEPU index, which amalgamates each economy’s EPU weighted by its GDP value, the new index works equally well in identifying typical global events. Most notably, the new index eliminates the requirement of extra economic data, thereby avoiding potential endogeneity in empirical studies. To demonstrate this, we study the correlation between gold future volatility and GEPU using the GARCH-MIDAS model, and show that the newly proposed GEPU index outperforms the previous version. Additionally, we employ complex network methodologies to present a topological characterization of the GEPU indices. This research not only contributes to the advancement of unsupervised machine learning algorithms in the economic field but also proposes a robust and effective GEPU index that outperforms existing models.

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Section: Introductionmentioning

confidence: 99%

A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning

Rao

Wei

et al. 2023

Mathematics

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“…These rumors spread rapidly through the Internet social media, leading to weakened investor confidence and collective position reduction, financial risks, and even social unrest. Therefore, in the era of big data, it has become an important and urgent challenge in today's world to study the mechanism of the inherent influence of rumors on stock market volatility, capitalizing on the massive information on the Internet to ensure the sound operation of financial systems [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Effect of social media rumors on stock market volatility: A case of data mining in China

et al. 2022

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The Stock Market is a typical complex network composed of investors, stocks, and market information. The abnormal fluctuation of the Stock Market has a strong effect on the economy of a country and even that of the world. Fueled by the herd effect of the increasingly abundant social media, Internet rumors, as an important source of market information and an exogenous financial risk, can lead to the collapse of investor confidence and the further propagation of financial risks, which can damage the financial system and even lead to social unrest. With additional availability of computing techniques, we attempt to uncover the media information effects in the stock market and seek to provide researchers with 1) a theoretical reference for a comprehensive understanding of such a complex network, 2) accurate prediction of future data, and 3) design of efficient and reliable risk intervention models. Based on the data of China’s Stock Market, this study uses machine learning to investigate social media rumors to reveal the interplay of social media rumors and stock market volatility. In this work, we find patterns from social media rumors from financial forums using machine learning, quantify social media rumors based on statistics, and analyze the mechanism of propagation and influence of social media rumors on stock market volatility using econometric models. The empirical results show that rumors play an important information transmission effect on stock market volatility and the constructed Internet Financial Forum Rumor Index is helpful to sense the potential impact of rumors, i.e., a significant lagged negative effect. These findings are of guidance for the optimization of the information environment, and can serve to promote the healthy and stable development of the stock market.

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Twitter policy uncertainty and stock returns in South Africa: Evidence from time‐varying Granger causality

Nyakurukwa,

Seetharam

2024

Journal of Forecasting

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The study uses time‐varying Granger causality models that incorporate two proxies for Twitter policy uncertainty and South African returns stock returns to investigate the causal relationship between Twitter uncertainty and South African stock returns for the period between 2017 and 2023. The findings demonstrate that Twitter Market Uncertainty and Twitter Economic Uncertainty mostly lead JSE returns around the start of the COVID‐19 pandemic and the Russia‐Ukranainan war respectively. The findings also show significant out‐of‐sample forecasts using uncertainty indexes from Twitter.

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A Twitter-Based Economic Policy Uncertainty Index: Expert Opinion and Financial Market Dynamics in an Emerging Market Economy

Cited by 5 publications

References 56 publications

A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning

A Normalized Global Economic Policy Uncertainty Index from Unsupervised Machine Learning

Effect of social media rumors on stock market volatility: A case of data mining in China

Twitter policy uncertainty and stock returns in South Africa: Evidence from time‐varying Granger causality

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