Summary:
Enhancing expectation management is key to refining the modern monetary policy framework with Chinese characteristics. The Third Plenary Session of the 20th CPC Central Committee explicitly emphasized the need to “improve the expectation management mechanism”. Inflation expectations exert a profound influence on the decision-making of economic agents and the dynamics of inflation, playing a pivotal role in the central bank's efforts to guide market expectations through policy communication. Enhancing the central bank's ability to manage inflation expectations is of great significance for achieving price stability and economic growth. However, existing indexes of inflation expectations often suffer from low frequency and limited sample sizes, restricting their ability to provide granular assessments or facilitate heterogeneous analysis. These limitations hinder the identification of critical areas for improving expectation management effectiveness. This paper employs theoretical modeling, text analysis, and empirical analysis to investigate the effectiveness of the People's Bank of China (PBC) in guiding market inflation expectations and the factors influencing this process. First, this paper constructs a theoretical model incorporating central bank inflation expectations and the formation of market inflation expectations, demonstrating the guiding role of the central bank in the formation of market inflation expectations and the factors influencing the effectiveness of the central bank's inflation expectation management. Next, based on over 60,000 Chinese macroeconomic research reports, this paper employs a combination of machine learning and text analysis to construct a weekly market inflation expectation index. Similarly, it uses central bank communication texts to construct a central bank inflation expectation index. Finally, this paper conducts empirical analysis using central bank inflation expectation indexes and market inflation expectation indexes to explore the guiding effect of central bank inflation expectations on market inflation expectations and tests whether the factors identified in the theoretical model influence the effectiveness of the People's Bank of China's inflation expectation management. The main findings of this paper are as follows. First, the People's Bank of China's inflation expectation management can guide market inflation expectations, and this conclusion holds under various endogeneity tests and robustness tests. Second, policy space, uncertainty, and market forecasting ability all significantly influence the effectiveness of the People's Bank of China's inflation expectation management, and this conclusion is supported by both the theoretical model and empirical analysis. Specifically, the effectiveness of the central bank's inflation expectation management is better when policy space is limited, uncertainty is high, and market forecasting ability is weak. This is because, under such circumstances, the market has a higher demand for central bank information and relies more heavily on the central bank's inflation expectations. This study provides important policy implications for the central bank to establish a sound expectation management mechanism and implement effective expectation management. First, fully leverage the coordinated role of expectation management and traditional monetary policy to enhance policy effectiveness. Second, closely align expectation management with macroeconomic and market conditions, implementing appropriate discretionary measures. Third, improve market expectation monitoring and feedback mechanisms to enhance the effectiveness of expectation management. This paper contributes to the literature in two ways. First, by utilizing textual data and machine learning techniques, it constructs a novel index for measuring inflation expectations in China. This index, derived from macroeconomic research reports, provides a high-frequency, information-rich, and continuously updated measure, addressing the limitations of existing low-frequency and small-sample indexes. As one of the first attempts to develop a market inflation expectation index through textual big data, this study offers a crucial foundation for comprehensive and systematic assessments of the central bank's expectation management. Second, this paper extends the understanding of inflation expectation management by identifying key factors that influence its effectiveness, both theoretically and empirically. While previous studies have often been constrained by limited data availability, making it difficult to explore the drivers of effective expectation management, this research overcomes these challenges through high-frequency indexes and extensive heterogeneous analysis. The findings highlight the significant role of policy space, uncertainty, and market forecasting capabilities, offering valuable insights for enhancing the design and implementation of expectation management strategies within the broader monetary policy framework.
郭豫媚, 王航, 郭田勇, 郭俊杰. 中央银行通货膨胀预期管理有效性评估——基于文本通货膨胀预期指数的研究[J]. 金融研究, 2025, 538(4): 1-20.
GUO Yumei, WANG Hang, GUO Tianyong, GUO Junjie. The Assessment of Central Bank's Inflation Expectation Management Effectiveness: A Study Based on Textual Inflation Expectation Indexes. Journal of Financial Research, 2025, 538(4): 1-20.
Ang, A., G. Bekaert and M. Wei, 2008, “The Term Structure of Real Rates and Expected Inflation”, The Journal of Finance, 63(2), pp. 797～849.
[21]
Angelico, C., J. Marcucci, M. Miccoli and F. Quarta, 2022, “Can We Measure Inflation Expectations Using Twitter？”, Journal of Econometrics, 228(2), pp. 259～277.
[22]
Denes, J., A. Lestrade and L. Richardet, 2022, “Using Twitter Data to Measure Inflation Perception”, A chapter in Machine learning in central banking, Vol. 57 from Bank for International Settlements.
[23]
Goy, G., C. Hommes and K. Mavromatis, 2022, “Forward Guidance and the Role of Central Bank Credibility under Heterogeneous Beliefs”, Journal of Economic Behavior ＆ Organization, 200, pp. 1240～1274.
[24]
Hansen, S., M. McMahon and A. Prat, 2018, “Transparency and Deliberation within the FOMC: A Computational Linguistics Approach”, The Quarterly Journal of Economics, 133(2), pp. 801～870.
[25]
Huang, Y. and P. Luk ,2020, “Measuring Economic Policy Uncertainty in China”, China Economic Review, 59, 101367.
[26]
Morris, S. and H. S. Shin, 2002, “Social Value of Public Information”, American Economic Review, 92(5), pp. 1521～1534.
