Monetary policy on twitter and asset prices: Evidence from computational text analysis |
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| Institution: | 1. Research Center of the Central China for Economic and Social Development, Nanchang University, 330031 Nanchang, PR China;2. School of Economics and Management, Nanchang University, 330031 Nanchang, PR China;3. Business School, Hunan University, 410082 Changsha, PR China;1. Département des sciences administratives, Université du Québec (Outaouais), Campus St. Jérôme, 5 rue St Joseph, St Jérôme, Québec J7Z 0B7, Canada;2. Université du Québec (Montréal), École des sciences de la gestion, 315 Ste.-Catherine est, R-2915, Montréal, Québec H2X 3X2, Canada;3. Chaire d’information financière et organisationnelle (Université du Québec à Montréal), and Université du Québec en Outaouais, Canada;1. Department of Business Administration, Fu Jen Catholic University, No. 510, Zhongzheng Rd., Xinzhuang Dist., New Taipei City 24205, Taiwan;2. Department of Business Administration, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan;1. Department of Economics, University of the West Indies, Mona Campus, Kingston 7, Jamaica;2. Department of Economics and Finance, Southern Illinois University Edwardsville, 3146 Alumni Hall, Edwardsville, IL 62025, United States;1. George Washington University, Washington DC, USA;2. Barnard College, NY, USA;1. Monetary Policy Department, Reserve Bank of India, Mumbai 400001, India;2. University of Mumbai, Mumbai 400098, India |
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| Abstract: | In this paper, we dissect the Twitter debate about the future course of monetary policy and trace the effects of selected topics of this discourse on U.S. asset prices. We focus on the “taper tantrum” episode in 2013, a period with large revisions in expectations about future Fed policy. Based on a novel data set of 90,000 Twitter messages (“tweets”) covering the debate of Fed tapering on Twitter, we use Latent Dirichlet Allocation, a computational text analysis tool, to quantify the content of the discussion. Several estimated topic frequencies are then included in a VAR model to estimate the effects of topic shocks on asset prices. We find that the discussion about Fed policy on social media contains price-relevant information. Shocks to the discussion about the timing of the tapering, about the broader economic policy context and worrying investors are shown to lead to significant asset price changes. We also show that the effects are mostly due to changes in the term premium of yields consistent with the portfolio balance channel of unconventional monetary policy. |
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| Keywords: | Monetary policy Fed Latent Dirichlet Allocation Text analysis VAR E32 E44 E52 |
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