Summary:
In order to accelerate the construction of a strong financial country and promote high-quality financial development, it is increasingly important to build a scientific and prudent monetary policy regulation system. In recent years, with the increasing demand for market expectation management and the maintenance of financial market stability, China has paid more and more attention to the monetary policy tools represented by central bank communication to manage market expectations. Therefore, it is of great significance to study the impact of central bank communication on asset prices in financial markets. Though there is abundant research on the impact of central bank communication on the financial market, relatively few studies are based on the perspective of unexpected monetary policy information. According to the rational expectations hypothesis, only unanticipated monetary policy information can drive changes in asset prices in financial markets. Therefore, measuring unanticipated monetary policy information in central bank communication has become a key issue. In fact, the information contained in central bank communication is particularly complex, showing multidimensional, multi-level, multi-structure and other characteristics. The traditional method of measuring unanticipated monetary policy information is to select a proxy indicator of monetary policy, and then to measure unanticipated monetary policy information, which is called quantifiable unanticipated monetary policy information (referred to as “quantifiable information”). However, the traditional method will inevitably be affected by factors such as subjectivity, representativeness, finiteness and model limitations of proxy indicators, resulting in the unexpected monetary policy information in central bank communication being only partially measured. In this paper, the unquantified part of the unanticipated monetary policy information is called potential unanticipated monetary policy information (referred to as “potential information”). In addition, to investigate the impact of monetary policy information on asset price changes, it is also necessary to pay attention to the endogeneity of monetary policy and the financial market, which have a two-way influence. The study of high-frequency financial events can effectively avoid endogeneity and simultaneity problems. This paper manually collects communication events of the People's Bank of China. It then uses daily financial market data and the high-frequency event research method to analyze the impact of unexpected monetary policy information in the communication of the People's Bank of China on asset prices. The main work and features of this paper are as follows: firstly, for the first time, dividing the unanticipated monetary policy information of the central bank communication into quantifiable information and potential information, using Treasury bond yield, shibor or M2 as proxy variables of monetary policy, and calculate quantifiable information based on Treasury bond futures, fixed float bonds and media survey data. Then, a potential factor is introduced into the state space model to identify the potential information, and Kalman filter method is used to achieve parameter estimation. Secondly, based on China's stock market, this paper examines the impact of quantifiable information and potential information in central bank communication on stock returns. In order to explain the difference between the latent factor model and the traditional event method, this paper compares the regression results of the two methods. Thirdly, this paper discusses the heterogeneous impacts of different types and different policy directions. On the one hand, the sample is divided into reports and meeting minutes for comparative study. On the other hand, the samples are divided into loose and tight information for comparative study. Finally, to verify the robustness of the event study method with potential factors and relevant conclusions, this paper examines the impact of central bank communication on the bond market, replaces the variable of quantifiable information, divides the different stages of the economic cycle and the verbal communication of the central bank. The data used in this paper are mainly the return of the Shanghai Stock Composite Index and Shenzhen Stock Exchange component index, the forward yield of Treasury bond futures, the 3-month interest rate of Shanghai interbank offered rate, the 1-year maturity yield of policy financial bonds, the floating interest rate of policy financial bonds, M2 and the consistent forecast data of M2. The sample period is from January 2007 to December 2022, and the data comes from the Wind, the People's Bank of China and Baidu search. The main research conclusions are as follows. First, quantifiable information measured by a single proxy indicator will underestimate the impact of unexpected monetary policy information in central bank communication on financial markets. Secondly, after introducing potential factors to measure potential unanticipated monetary policy information, more than 96% of stock price changes in the event window can be explained by unanticipated monetary policy information. Third, in terms of communication types, whether it is meeting minutes or reports, the expected management of central bank communication has a significant impact on the stock markets, but the impact of reports is greater. In terms of policy direction, central bank communication has an asymmetric impact on the stock market. Easing unanticipated monetary policy information has a significant positive impact on stock prices, while tightening unanticipated monetary policy information has a significant negative impact on stock prices, and investors are more sensitive to tightening information. To sum up, in order to improve the modernization of the central bank's monetary policy regulation and strengthen expectation management, the central bank should build an unexpected monetary policy information index based on multi-dimensional monetary policy anchor indicators. Secondly, optimize communication methods and improve communication content to enhance communication effectiveness. Thirdly, differentiated regulation policies should be formulated for different economic cycles. Finally, optimize the intermediate variable of monetary policy and straighten out the transmission mechanism of the central bank's policy interest rate. In addition, future studies are still needed to explore whether the impact of central bank communication on asset prices in financial markets has non-linear characteristics, and how to fully mine text information with big data methods.
董青马, 张皓越, 马剑文, 尚玉皇. 央行沟通与资产价格——识别“潜在”未预期货币政策信息[J]. 金融研究, 2024, 528(6): 40-59.
Dong Qingma, Zhang Haoyue, Ma Jianwen, Shang Yuhuang. Central Bank Communication and Asset Prices —— A Method for Identifying Potential Information of Unexpected Monetary Policy. Journal of Financial Research, 2024, 528(6): 40-59.
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