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| Can Central Bank Expectation Management Alleviate Bank Liquidity Hoarding？ An Empirical Test Based on Forward-looking Textual Sentiment Index |
| JIANG Hai, SUN Na, LI Xinming
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School of Economics/Research Institute of Finance, Jinan University;
School of Finance, Nankai University |
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Abstract 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.
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Received: 15 February 2023
Published: 02 July 2025
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| [1] |
陈良源和林建浩,2024,《货币政策预期管理的策略选择:模糊沟通与精确沟通》,《管理世界》第12期,第63～78+94+79～83页。
|
| [2] |
方意、和文佳和王琦,2024,《非核心负债业务、流动性渠道和银行业系统性风险:理论模型与经验分析》,《金融研究》第3期,第20～37页。
|
| [3] |
顾海峰和于家珺,2019,《中国经济政策不确定性与银行风险承担》,《世界经济》第11期,第148～171页。
|
| [4] |
郭豫媚、陈伟泽和陈彦斌,2016,《中国货币政策有效性下降与预期管理研究》,《经济研究》第1期,第28~41+83页。
|
| [5] |
郭豫媚和郭俊杰,2024,《不确定性会影响央行沟通效果吗》,《财贸经济》第6期,第88～104页。
|
| [6] |
姜富伟、胡逸驰和黄楠,2021,《央行货币政策报告文本信息、宏观经济与股票市场》,《金融研究》第6期,第95～113页。
|
| [7] |
姜富伟、李梦如和孟令超,2024,《调预期、防风险:央行金融稳定沟通效果研究——基于文本分析的视角》,《经济学(季刊)》第6期,第2025~2040页。
|
| [8] |
吕之安、刘莉亚和谭姝颖等,2024,《央行沟通“简明—专业”权衡与公众行动》,《经济研究》第11期,第21～37页。
|
| [9] |
宋科、徐蕾和李振等,2022,《ESG投资能够促进银行创造流动性吗？——兼论经济政策不确定性的调节效应》,《金融研究》第2期,第61～79页。
|
| [10] |
万志宏和曾刚,2012,《后危机时代美国银行体系的流动性囤积与货币政策传导》,《国际金融研究》第10期,第21～28页。
|
| [11] |
王辉、朱家雲和胡诣聪,2023,《央行预期引导可以降低银行系统性金融风险吗？——基于市场解读偏离的视角》,《金融研究》第9期,第1～19页。
|
| [12] |
王宇伟和刘宏雅,2023,《央行言辞沟通与银行信贷投放:总量与结构效应》,《金融研究》第11期,第78～96页。
|
| [13] |
魏旭和周伊敏,2022,《流动性监管、系统性风险与社会福利——一个理论分析框架》,《经济学(季刊)》第5期,第1619～1638页。
|
| [14] |
项后军、高鹏飞和曾琪,2023,《银行风险承担渠道、流动性囤积与货币政策传导的“梗阻效应”研究》,《国际金融研究》第1期,第74～84页。
|
| [15] |
许文彬、赵霖和李志文,2019,《金融监管与金融创新的共同演化分析——一个基于非线性动力学的金融监管分析框架》,《经济研究》第5期,第81～97页。
|
| [16] |
尹龙,2005,《金融创新理论的发展与金融监管体制演进》,《金融研究》第3期,第7～15页。
|
| [17] |
张成思和计兴辰,2017,《善言为贤:货币政策前瞻性指引的中国实践》,《国际金融研究》第12期,第3～16页。
|
| [18] |
Aastveit,K. A.,G. J. Natvik,and S. Sola,2017, “Economic Uncertainty and the Influence of Monetary Policy”,Journal of International Money and Finance,76,pp. 50～67.
|
| [19] |
Angeloni,I.,Faia,E.,and Duca,M. L.,2015,“Monetary Policy and Risk Taking”,Journal of Economic Dynamics and Control,52,pp. 285~307
|
| [20] |
Arellano,C.,Y. Bai, and P. J. Kehoe, 2019, “Financial Frictions and Fluctuations in Volatility”, Journal of Political Economy,127(5),pp, 2049～2103.
|
| [21] |
Arping,S.,2017, “Deposit Competition and Loan Markets”, Journal of Banking and Finance, 80: 108-118.
|
| [22] |
Berger,A.N., O. Guedhami ,H.H. Kim and X. Li, 2022, “Economic Policy Uncertainty and Bank Liquidity Hoarding”, Journal of Financial Intermediation,49,100893.
|
| [23] |
Bordo,M.D., J.V. Duca, C. Koch, 2016, Economic Policy Uncertainty and the Credit Channel: Aggregate and Bank Level US Evidence over Several Decades”, Journal of Financial Stability, 26, pp.90～106.
|
| [24] |
Caballero,R.J. and A. Simsek, 2013, “Fire Sales in a Model of Complexity”, Journal of Finance, 68(6), pp. 2549～2587.
|
| [25] |
Friedman,B. M.,and K. N. Kuttner,2010, “Implementation of Monetary Policy:How Do Central Banks Set Interest Rates？”, Handbook of Monetary Economics,3,pp. 1345～1438,Elsevier.
|
| [26] |
Gardner,B.,C. Scotti and C. Vega, 2022, “Words Speak as Loudly as Actions: Central Bank Communication and the Response of Equity Prices to Macroeconomic Announcements”, Journal of Econometrics,231(2), pp.387～409.
|
| [27] |
Gorton,G., 2009, “Information, Liquidity, and the (Ongoing)Panic of 2007”, American Economic Review,99(2), pp.567～572.
|
| [28] |
Hansen,S. and M. McMahon, 2016, “Shocking Language: Understanding the Macroeconomic Effects of Central Bank Communication”, Journal of International Economics,99, pp.S114～S133.
|
| [29] |
Jiang,F.,Lee,J.,Martin,X.,and Zhou,G,2019,“Manager Sentiment and Stock Returns”,Journal of Financial Economics,132(1),pp. 126~149.
|
| [30] |
Nguyen,N. H.,and H. V. Phan,2017, “Policy Uncertainty and Mergers and Acquisitions”, Journal of Financial and Quantitative Analysis,52(2),pp. 613～644.
|
|
|
|
