Summary:
A key role of financial institutions is to serve the real economy. However, in practice, loose monetary policies have failed to translate into effective credit easing. After the central bank injected liquidity into the banking system, due to the constraints of both the supply side and the demand side, the liquidity was hoarded in the banking system. A major cause of this liquidity hoarding lies in public pessimistic expectations, making expectation management crucial for addressing this issue. This leads to a critical practical question: Can central bank expectations management effectively alleviate bank liquidity hoarding？ To answer the question, this paper employs machine learning methods to construct a sentiment index for the forward-looking content of the China Monetary Policy Report, which serves as a measure of central bank's expected management policy. Meanwhile, this paper uses unbalanced panel data of banks from 2009 to 2022 to empirically test whether the central bank's expectation management can alleviate banks' liquidity hoarding. This paper also conducts a series of robustness tests, including the instrumental variable method, text content splitting, alternative sentiment measurement method, and alternative sample. The empirical results show: First, proactive forward-looking text sentiment alleviates liquidity hoarding among banks by reducing credit yield uncertainty and enhancing risk tolerance. Second, this sentiment significantly suppresses liquidity hoarding in banks with moderate liquidity levels (35%-75% percentile interval), while showing limited effect on those hoarding high or low liquidity. Third, the suppressive impact varies across bank sizes: it is notably effective for city commercial banks and rural commercial banks, but less pronounced in state-owned banks, joint-stock banks, and small rural financial institutions. Fourth, banks' innovation capacity enhances the suppressive effect, which becomes more pronounced when regulatory measures and innovation efforts reinforce each other. Finally, forward-looking text sentiment demonstrates stronger liquidity hoarding suppressive effects during economic upturns. Based on the empirical results, this paper proposes the following policy recommendations. First, optimize the forward guidance tone of the central bank to alleviate liquidity hoarding in the banking system. China is in the stage of gradual economic recovery, but the expectations of market entities such as banks remain weak. It is recommended that the central bank should emphasize confidence in achieving potential growth targets when releasing the Monetary Policy Report. Simultaneously, it should clearly communicate its commitment to maintaining reasonable liquidity through observable indicators such as target ranges for market interest rates. Second, implement differentiated expectation management strategies. Strengthen the forward guidance of small and medium-sized banks, including differentiated rediscount quotas and assessment standards. Promoting these banks to release liquidity can accurately meet the financing needs of small, medium and micro enterprises. Third, establish an expectation management mechanism aligned with economic cycles. During downturns, the emotional tone conveyed in the Monetary Policy Report has limited effectiveness in curbing liquidity hoarding. It is recommended that the central bank further strengthen expectation management through other means, such as increasing the frequency of briefings and press conferences, and setting up a policy feedback section on the official website of the People's Bank of China. Fourth, banks should be encouraged to enhance their innovation capabilities. In order to give full play to the expected management effect of the central bank, banks need to strengthen their innovation capabilities. Only when banks can carry out credit supply and post-loan management more accurately can they realize the incentive compatibility between high-quality development of banks and the expected management goals of the central bank. Finally, regulatory policies should be coordinated with expected management policies. Regulators should establish a more comprehensive information disclosure mechanism to enhance market transparency and banks' trust in forward-looking guidance.
蒋海, 孙娜, 李欣明. 央行预期管理能缓解银行流动性囤积吗？——基于前瞻性文本情绪指数的实证检验[J]. 金融研究, 2025, 540(6): 1-20.
JIANG Hai, SUN Na, LI Xinming. Can Central Bank Expectation Management Alleviate Bank Liquidity Hoarding？ An Empirical Test Based on Forward-looking Textual Sentiment Index. Journal of Financial Research, 2025, 540(6): 1-20.
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