Summary:
At the beginning of 2020, the COVID-19 pandemic had a massive impact on the Chinese economy, leading to debates about whether consumption would slow down after recovery from the pandemic. It is crucial to achieve real-time forecasting consumption growth, which is important for government policy implementation. Moreover, accurately predicting the changes in consumption growth can provide timely warnings about downward pressures on consumption, allowing sufficient time for stimulus policy plans. Consumers' expectations of future economic conditions, personal incomes, price levels, and macro policies, among other factors, affect their investment and consumption decisions. Narrative economics suggests that consumer expectations are largely guided by media narratives. Therefore, consumption-related media coverage can be used to reflect changes in consumer expectations and predict future consumption activities. This article constructs a consumption-related news sentiment index (CNSI) based on close to 600,000 news articles from five mainstream media outlets in China from 2007 to 2020. We evaluate the performance of the news-based CNSI and the survey-based consumer confidence index (CCI) in real-time consumption projections. We study the time-series characteristics of consumption growth and find an obvious trend disconnection between consumption growth and consumer sentiment in China, especially after the advent of the “new normal” period, with a decline in consumption growth and an increase in consumer sentiment. This trend disconnection is an important feature that is ignored in the literature. By using wavelet decomposition, or detrending processing, we find a positive and significant correlation between the CNSI and the short-term cyclical component of consumption growth, whereas the CCI does not identify this correlation. Correspondingly, our CNSI can considerably improve the out-of-sample forecasting of short-term consumption, and it can be applied to nowcasting and mixed-frequency forecasting. We further explore the content structures of news texts and find that the “current status” content of news performs better for real-time nowcasting, whereas the “foresight analysis” content is more effective in predicting future consumption growth. Furthermore, more objective and emotionally neutral (non-seditious) media texts perform better in nowcasting and forecasting. Compared with the Internet-based CCI, the construction of CNSI is simpler and more transparent, which results in outstanding advantages in consumption projections. This article makes the following contributions to the literature. First, to the best of our knowledge, this study is the first to focus on the trend disconnection between consumption growth and consumer sentiment in China. We address this issue by identifying the long-term trend and short-term cyclical components of consumption growth through cycle decomposition methods, and find that their performance differs. This indicates that economic forecasting studies cannot ignore the cyclical changes in China's economy, and future research must consider cycle decomposition. Second, this article combines consumption projections with high-frequency and real-time textual data. Compared with the official CCI, the weekly news-based CNSI is timelier and more responsive to economic changes. The mix-frequency prediction results indicate that high-frequency data can further improve the accuracy of forecasting consumption growth, which can be an appropriate direction for further research. Third, this article examines multi-dimensional text features, such as “current status vs. foresight analysis”, and “seditious vs. non-seditious” features. This not only supports the theoretical conclusion that consumer confidence and expectations are driven by information on economic fundamentals but also provides a new perspective on the application of textual data in economic research.
张一帆, 林建浩, 樊嘉诚. 新闻文本大数据与消费增速实时预测——基于叙事经济学的视角[J]. 金融研究, 2023, 515(5): 152-169.
ZHANG Yifan, LIN Jianhao, FAN Jiacheng. News Data and Real-Time Consumption Growth Projections: A View from Narrative Economics. Journal of Financial Research, 2023, 515(5): 152-169.
[1]洪永淼、刘俸奇和薛涧坡,2023,《政府与市场心理因素的经济影响及其测度》,《管理世界》第3期,第30～50页。 [2]姜富伟、孟令超和唐国豪,2021,《媒体文本情绪与股票回报预测》,《经济学(季刊)》第4期,第1323～1344页。 [3]林建浩、陈良源、罗子豪和张一帆,2021,《央行沟通有助于改善宏观经济预测吗？——基于文本数据的高维稀疏建模》,《经济研究》第3期,第48～64页。 [4]沈艳、陈赟和黄卓,2019,《文本大数据分析在经济学和金融学中的应用:一个文献综述》,《经济学(季刊)》第4期,第1153～1186页。 [5]孙毅、吕本富、陈航和薛添,2014,《基于网络搜索行为的消费者信心指数构建及应用研究》,《管理评论》第10期,第117～125页。 [6]王频和侯成琪,2017,《预期冲击、房价波动与经济波动》,《经济研究》第4期,第48～63页。 [7]王霞、司诺和宋涛,2021,《中国季度GDP的即时预测与混频分析》,《金融研究》第8期,第22～41页。 [8]杨天宇和朱光,2020,《劳动报酬上涨与中国国民储蓄率的演变趋势》,《金融研究》第11期,第21～39页。 [9]臧旭恒、陈斌开、尹志超、汪伟和易行健,2020,《“新冠肺炎疫情与消费”专家笔谈》,《消费经济》第3期,第3～12页。 [10]张晓晶,2022,《经济新常态》,《经济研究》第7期,第4～11页。 [11]郑挺国、靳炜、方匡南和林洪伟,2023,《媒体信息、预期冲击与经济周期波动——基于中文财经类报刊数据》,《数量经济技术经济研究》第2期,第202～220页。 [12]庄子罐、崔小勇、龚六堂和邹恒甫,2012,《预期与经济波动——预期冲击是驱动中国经济波动的主要力量吗？》,《经济研究》第6期,第46～59页。 [13]Aguiar-Conraria, L., and M. Joana Soares. 2011. “Business Cycle Synchronization and the Euro: A Wavelet Analysis”, Journal of Macroeconomics, 33(3):477～489. [14]Armantier, O., G. Topa, W. Van der Klaauw, and B. Zafar. 2017. “An Overview of the Survey of Consumer Expectations”, Economic Policy Review, 23(2):51～72. [15]Barsky, R.B., and E.R. Sims. 2012. “Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence”, American Economic Review, 102(4):1343～1377. [16]Beaudry, P., and F. Portier. 2014. “News Driven Business Cycles: Insights and Challenges”, Journal of Economic Literature, 52(4):993～1074. [17]Bok, B., D. Caratelli, D. Giannone, A.M. Sbordone, and A. Tambalotti. 2018. “Macroeconomic Nowcasting and Forecasting with Big Data”, Annual Review of Economics, 10(1):615～643. [18]Bybee, L., B.T. Kelly, A. Manela, and D. Xiu. 2021. “Business News and Business Cycles”, National Bureau of Economic Research Working Paper Series, No. 29344. [19]Carroll, C.D., J. Slacalek, and M. Sommer. 2011. “International Evidence on Sticky Consumption Growth”, Review of Economics and Statistics, 93(4):1135～1145. [20]Chang, C., K. Chen, D.F. Waggoner, and T. Zha. 2016. “Trends and Cycles in China's Macroeconomy”, NBER Macroeconomics Annual, 30:1～84. [21]Christiansen, C., J.N. Eriksen, and S.V. Møller. 2014. “Forecasting US Recessions: The Role of Sentiment”, Journal of Banking & Finance, 49:459～468. [22]Crowley, P.M. 2007. “ A Guide to Wavelets for Economists”, Journal of Economic Surveys, 21(2): 207～267. [23]Diebold, F.X., and R.S. Mariano. 1995. “Comparing Predictive Accuracy”, Journal of Business & Economic Statistics, 13(3):253～263. [24]Dominitz, J., and C.F. Manski. 2004. “How Should We Measure Consumer Confidence？”, Journal of Economic Perspectives, 18(2):51～66. [25]Hansen, P.R., and A. Timmermann. 2015. “Comment on: Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests”, Journal of Business & Economic Statistics, 33:17～21. [26]Kelly, B., A. Manela, and A. Moreira. 2021. “Text Selection”, Journal of Business & Economic Statistics, 39(4): 1～21. [27]Lahiri, K., G. Monokroussos, and Y. Zhao. 2015. “Forecasting Consumption: The Role of Consumer Confidence in Real Time with Many Predictors”, Journal of Applied Econometrics, 31(7):1254～1275. [28] Larsen, V.H., and L.A. Thorsrud., 2019. “The Value of News for Economic Developments”, Journal of Econometrics, 210(1):203～218. [29]Nimark, K.P., and S. Pitschner. 2019. “News Media and Delegated Information Choice”, Journal of Economic Theory, 181:160～196. [30]Pennebaker, J.W., M.E. Francis, and R.J. Booth. 2001. “Linguistic Inquiry and Word Count: LIWC 2001”, Mahway: Lawrence Erlbaum Associates, 71. [31]Shapiro, A.H., M.Sudhof, and D.J.Wilson. 2022. “Measuring News Sentiment”, Journal of Econometrics, 228(2):221～243. [32]Shiller, R.J. 2019. Narrative、 Economics:“How Stories Go Viral and Drive Major Economic Events”, Princeton University Press. [33]Song, M., and K. Shin. 2019. “Forecasting Economic Indicators Using a Consumer Sentiment Index: Survey-based versus Text-based Data”, Journal of Forecasting, 38(6):504～518. [34]Thorsrud, L.A.. “Words are the New Numbers: A Newsy Coincident Index of the Business Cycle”, Journal of Business & Economic Statistics, 38:393～409. [35]Wang, P., X. Li, X. Zhan, Y. Zhang, Y. Yan, and W. Meng. 2019. “Building Consumer Confidence Index based on Social Media Big Data”, Human Behavior and Emerging Technologies, 1(3):261～268. [36]Yogo, M. 2008. “Measuring Business Cycles: A Wavelet Analysis of Economic Time Series”, Economics Letters, 100(2):208～212.
