The Impact of Business
Cycles on the Profitability of
Chinese Commercial Banks
MASTER THESIS WITHIN: Business Administration NUMBER OF CREDITS: 15 Credits
PROGRAMME OF STUDY: International Financial Analysis AUTHOR: Bo Wu and Mingfang Wang
We would like to express our gratitude to our tutor Haoyong Zhou, PhD, for his insightful and useful ideas throughout the entire research period. What’s more, we also would like to thank the students who helped us, in particular our opponents Lisa Fischer and Reamflar Elvio Estebano Troeman, for their great feedback in the seminars.
_________________________ __________________________ Bo Wu Mingfang Wang
Title: The Impact of Business Cycles on the Profitability of Chinese Commercial Banks Authors: Bo Wu and Mingfang Wang
Tutor: Haoyong Zhou Date: 2020-05-18
Key terms: Business Cycles, Chinese Commercial Banks, Profitability, Panel Data
Chinese commercial banks are the leading players directly participating in China's market economy activities. Macroeconomic stability is an essential prerequisite for the development of the banking industry. However, with the development of economic globalization, more and more influencing factors determine the changes in the business cycles, thus making commercial banks face higher risks in their operations. What's more, policy factors may make the operation of China's commercial banks inconsistent with changes in the business cycles. At present, China's domestic research literature that extends to the operation of commercial banks from the perspective of economic cycles lacks a systematic theoretical analysis.
The purpose of the study was to examine the correlation between the profitability of commercial banks and business cycles in China by using the panel data analysis method. This paper selected return on assets, loan growth rate, non-performing loan ratio and net interest spread of 34 listed commercial banks from 2001 to 2018 as related variables and selected GDP growth rate, unemployment rate, and industry value added growth rate of China from 2001 to 2018 as the indicators of business cycles. The findings show that business cycles have a negative effect on the profitability of commercial banks in China. Based on the result of the empirical conclusions and the interest-oriented profit model of Chinese commercial banks, the paper also analyzes the impact of the business cycles on commercial bank profitability from the perspective of loan and net interest spread. Commercial bank practitioners can use these findings to avoid risks in bank operations, while other researchers can use our findings to develop further research on the factors affecting the profitability of commercial banks.
1.
Introduction ... 1
1.1 Background ... 1 1.2 Problem discussion ... 3 1.3 Outline ... 4 1.4 Perspective ... 42.
Frame of Reference ... 4
2.1 Business Cycles Overview ... 5
2.1.1 Business Cycle Theory ... 5
2.1.2 Forecasts for the Business Cycles ... 5
2.1.3 Business Cycles Accounting ... 6
2.1.4 The Impact of the Business Cycles ... 6
2.1.5 Synchronicity and Influencing Factors of Business Cycles between Countries ... 7
2.2 The Profitability of Banks Overview ... 7
2.2.1 Bank Performance Evaluation System ... 8
2.2.2 Factors Affecting Bank Earnings ... 8
2.3 The Relationships between the Business Cycles and the Profitability of Banks Overview ... 9
2.3.1 The Relationship between Business Cycles and Credit ... 9
2.3.2 Research on the Relationship between Business Cycles and Bank Risk ... 9
2.3.3 Research on the Relationship between Business Cyclse and Bank Profitability ... 10
3.
Data and Research Method ... 12
3.1 Selection of Variables ... 12
3.2 Selection of Data ... 14
3.3 Research Method ... 15
4.
Empirical Finding and Analysis ... 17
4.1 Descriptive Statistics of Data ... 17
4.1.1 Review of China's Business Cycle Fluctuations ... 17
4.1.2 Chinese Banking Performance ... 20
4.2 Analysis of the Influence of Business Cycles on Commercial Banks' Profit Model ... 21
4.2.1 An Overview of Commercial Banks' Profit Model ... 21
4.2.2 Overview of the Relationship between China's Business Cycles and the Profitability of Commercial Banks ... 24
4.2.3 Analysis of the Impact of Business Cycles on Bank Profitability from the Perspective of Loan ... 25
4.2.4 Analysis of the Impact of Business Cycles on Bank Profitability from the Perspective of Interest Spread ... 28
4.3 Correlation... 32
4.4 The Cointegration Test of Panel Data ... 34
4.5 Regression ... 35
5.
Analysis of the Impacting Mechanisms of Business Cycles on
Commercial Banks' Profit Model ... 43
Figure 1 The GDP growth rate, unemployment rate and industry value added growth rate of China from 1979 to 2018 ... 18 Figure 2 China's commercial bank interest income and non-interest income ratio ... 22 Figure 3 China's economic growth rate and ROA growth of commercial banks trend comparison ... 24 Figure 4 Comparing the trends of China's economic growth rate and loan growth rate . 26 Figure 5 Comparing China's economic growth with the trend of NPL ratio ... 27 Figure 6 The net interest spread of 34 banks of China from 2001 to 2018 ... 31 Figure 7 The GDP growth rate, unemployment rate and industry value added growth rate of China from 2001 to 2018 ... 32 Figure 8 The diagram of impacting mechanisms ... 44
Tables
Table 1 The choice of the dependent variable, independent variables and control
variables ... 12 Table 2 Statistic description of samples ... 20 Table 3 Correlation of the explanatory variables in model 1 which include the
GDPGR as the indicator of business cycles ... 33 Table 4 Correlation of the explanatory variables in model 2 which include the
UNEM as the indicator of business cycles ... 33 Table 5 Correlation of the explanatory variables in model 3 which include the
IVAGR as the indicator of business cycles ... 34 Table 6 Kao test based on the Engle-Granger on the three models ... 35 Table 7 The regression result of model 1 which include the GDPGR as the indicator of business cycles ... 37 Table 8 The regression result of model 2 which include the UNEM as the indicator of business cycles ... 38 Table 9 The regression result of model 3 which include the IVAGR as the indicator of business cycles ... 39 Table 10 The regression result of ROA and Loan growth rate ... 40
Appendix
1. Introduction
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The introduction mainly introduces the writing background of this paper and describes the issues to be discussed and the research conducted. At the same time, the framework of the article introduced.
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1.1 Background
The economy determines finance, and finance serves the economy. This is the essential relationship between economics and finance. Therefore, changes in the stage and logic of economic development will fundamentally cause financial adjustments and form a development pattern in the new environment.
According to the "Top 1000 World Banks" list 2016 published by the UK-based The Bankers Magazine, the Industrial and Commercial Bank of China topped the list for the fourth consecutive year with Tier 1 capital of USD 274.432 billion. Industrial and Commercial Bank of China also topped the list with total assets of USD 3,422.154 billion. According to banker data, 119 Chinese banks were shortlisted in the ranking of 2016 "Top 1000 World Banks", and 17 of them were in the top 100. Tier 1 capital of a commercial bank is a crucial indicator of the business development capability and risk-bearing of a commercial bank, and an essential guarantee for the sustainable growth of a commercial bank. The World Bank's Tier 1 capital rankings for 2016 included 4 Chinese banks. In 2016, four banks in China entered the top 10 of Tier 1 capital. However, in 2004, none of the world's top ten banks were Chinese banks, which shows the rapid development of the Chinese commercial banking industry.
Chinese commercial banks are the main players directly participating in China's market economy activities. At the same time, monetary policy is also an important medium for currency circulation and plays an important leverage role in the market economy. The role of commercial banks is critical in Chinese economic growth. Therefore, the commercial banks of China inextricably linked to the development of the real economy.
The development of commercial banks will affect the development of the real economy, and fluctuations in the real economy will also affect the operation of commercial banks.
However, with the development of economic globalization, more and more influencing factors determine the changes in the business cycles, thus making commercial banks face higher risks in their operations. Affected by the U. S. subprime mortgage loan crisis in 2007, China's economic growth rate has dropped significantly. It experienced severe inflation in 2010 and 2011. However, according to the 2011-2012 China Banking Industry Development Report, China's bank performance grew significantly in the context of the overall economic slowdown in 2011. Policy factors may make the operation of China's commercial banks inconsistent with changes in the business cycles. Whether there is a causal relationship between the profitability of China's commercial banks and the business cycles, and the direction of the causal relationship has become a question worth exploring.
At present, China’s domestic research literature that extends to the operation of commercial banks from the perspective of business cycles lacks a systematic theoretical analysis. There are also appropriate problems in the application of relevant international research results in researching of Chinese commercial banks’ profitability. There are few pieces of literature can be found with the reference value. We find two that are “Relationship of the economic cycle and the performance of commercial banks-based on the panel data of listed banks in China” written by Shen (2013) and “The Impact of Economic Cycles on China’s Commercial Bank Profit: An Empirical Evaluation- Based on 14 Listed Banks As An Example” written by Yao (2014). Shen (2013) believed that the effect of GDP growth rate on the bank 's return on net assets is positive, but only weakly correlated, which is significant at the level of 8.4%, and the coefficient is relatively small, so the macroscopic effect represented by GDP on the bank 's return on net assets had little effect. According to the empirical analysis of panel data from Yao (2014), net interest spread, credit scale and GDP growth rate had positive relationships between the profitability of commercial banks, and a non-performing loan rate had a negative relationship with the profitability of commercial banks.
However, the two papers only included data from 14 listed banks in the sample, and the period was not long. This may result in a failure to fully reflect the impact of China’s business cycle fluctuations on commercial banks' earnings.
Therefore, in this paper, we expanded the sample data and the time span, and the number of indicators of the business cycles are increased. This increases the accuracy of the empirical analysis. There are advantages in studying the relationship between the business cycles and the profitability of commercial banks.
1.2 Problem discussion
Macroeconomic stability is an essential prerequisite for the development of the banking industry. At the same time, the operation of commercial banks can not only effectively promote the growth of macroeconomy, but also may aggravate the fluctuation of the macroeconomy. Through the reform of China's banking ownership system, major banks have been listed one after another, ordinary people can become shareholders of banks, and bank performance has become the focus of the attention of all parties. We conduct empirical research on the performance of commercial banks and China's business cycles during this period, establish the regression model, and systematically discuss and analyze the relationship between business cycles and bank performance.
From the perspective of China's commercial banks, this article tries to understand the development trends of economic environment changes to affect banks operating. We take 34 major commercial banks in China as research objects. Choosing the return on assets (ROA), loan growth rate (LGR), the non-performing loan ratio (NPLR), and the net interest spread (NIS) of the 34 listed commercial banks in China from 2001 to 2018 as relevant variables. Choosing the GDP growth rate, industrial added value growth rate, and unemployment rate from 2001 to 2018 of China as indicators of the business cycles. And we mainly analyze the impact of the business cycles on China's commercial banking industry from a profitability perspective.
Through research and discussion, we hope that the paper can provide a reference for Chinese commercial banks to better respond to changes in the business cycles, prevent related risks, and realize the stability of commercial bank operations.
1.3 Outline
The disposition of this paper will be as follows. After the introduction, the Chapter 2 frame of reference will follow, which include the literature we read and used. In this part, the main research problems of relevant literature are summarized, and we can find enlightenment about our paper by these researches. In Chapter 3, we will introduce our selection of variables and research methodology, together with our hypotheses about this thesis. Chapter 4 is about data that will include descriptive statistics of China's business cycles and descriptive statistics of Chinese banking performance. Then, the empirical results of the research will be presented. By focusing on the role of commercial banks in the fluctuation of the business cycles, the specific analysis of the transmission mechanism in the theory of the financial business cycles will be analyzed in Chapter 5. Finally, conclusions and discussion put forward in Chapter 6 and Chapter 7.
1.4 Perspective
Through literature reading and empirical analysis, we find that the bank's existing operating profit model will not suitable long-term development in the future, especially in today's increasingly fierce financial competition and intellectualization operating environment. From the economic environment and the profit models of commercial banks, the current profit models of commercial banks in China need to change.
2. Frame of Reference
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The purpose of this part is to introduce the related theories and researches. This is making theoretical foundation for the following chapters.
______________________________________________________________________
This study is about the relationship between the business cycles and the profitability of commercial banks. It contains three basic elements: the business cycles, the profitability of banks, and their interrelationships. This section briefly summarizes the existing research results on these three aspects.
2.1 Business Cycles Overview
Since the beginning of the 19th century, many scholars have proposed many theoretical hypotheses of business cycle changes. In recent decades, many new features have emerged from the development of the transformation economy.
2.1.1 Business Cycle Theory
Business cycle theory includes many different fields and schools, and various schools provide different mathematical methods to explanate business cycles.
Arnold (2002) divided the mainstream business cycle theory after the great depression of the 1930s into five schools: Keynesian economics, monetarism, new classical economics, the real business cycles theory, and new Keynesian economics—in a historical perspective by presenting them in the chronological order of their appearance and highlighting their differences and commonalities. Among them, the most representatives were the Real Business Cycles (RBC) and New Keynesianism Dynamic Stochastic General Equilibrium (NK-dsge). They held that economic fluctuations result from a variety of random shocks, including technology, preferences, macroeconomic policies (monetary and fiscal), consumption, investment, etc., and that economic fluctuations were the result of a combination of these shocks, which was also the prevailing view.
2.1.2 Forecasts for the Business Cycles
The measurement and prediction of the economic cycles have always been a significant area of analysis of the business cycles. In recent years, its results have mainly focused on the introduction of new measurement methods and the construction of prediction models.
Sarlan (2001) analyzed the changes in the intensity and duration of the U.S. business cycles through the spectrum of turning points chronology. He explained two chronologies: one is the chronology of the economic cycles measured by the NBER, and the Markov switching regime model measured the other with the macroeconomic growth rate as an indicator. The results showed that the economic cycles measured by these two methods divided into major cycles and minor cycles. An NBER-measured cycle included an average of two 5-year cycles, and each 5-year cycle embodied two minor cycles. Among the cycles measured by the Markov Markov switching regime model, in a 10-year period，
there were an average of three 3.5-year duration, and each cycle of the had a minor 1.5-year cycle.
Comin and Gertler (2003) defined a new spectrum oscillation to determine the business cycles and gives a definition of the medium-term cycle. They used high-frequency volatility that included cyclic terms to reflect periods and defined the medium period as the sum of high frequency and medium frequency volatility. They developed a methodology for identifying these kinds of fluctuations and then showed that some crucial macroeconomic time series exhibit significant medium-term cycles.
Although most models could anticipate some recessions and predict recessions that do not occur, however, they failed to anticipate other recessions altogether. Pelàez (2007) presented a logit model that could solve this problem. This could accurately forecast business-cycles turning points with a lead of one-quarter. And this model also could predict each of eight turns in the test period and predicted the state of the economy during the last 30 years.
2.1.3 Business Cycles Accounting
About how to measure economic fluctuations, Chari et al. (2007) proposed a simple method to help researchers develop quantitative models to solve this issue. The method rested on the insight that many models were equivalent to a prototype growth model with time-varying wedges. Their business cycles accounting method could use to judge which mechanisms were promising and which were not, thus helping theorists narrowed their options.
2.1.4 The Impact of the Business Cycles
Regarding the effects of the business cycles, Mukoyama et al. (2014) measured the impact of the business cycles from an employment perspective. By measuring the income and unemployment risk of workers, they found that when the economic cycles were in a recession, the impact of unskilled workers was higher than that of skilled workers. Such shocks not only increased their risk of unemployment but also would reduce the income of unskilled workers, thereby affecting their ability to protect themselves.
2.1.5 Synchronicity and Influencing Factors of Business Cycles between Countries With the deepening of economic globalization, the flow of goods, services, and even the capital of various countries is gradually strengthening. The close relationship between trade and finance has also made the economic relations between countries more and more close. Under this open economic system, economic fluctuations in one country will spread to other countries through globalization. Therefore, the synchronicity of fluctuations in economic cycles between countries will become stronger and stronger, and gradually form international periodicity fluctuation.
Calderón et al. (2007) studied the impact of trade intensity on the synchronicity of economic cycles among countries, but they focused on the synchronicity of cycles among developing countries. Through empirical research on the annual data of 147 countries from 1960 to 1999, they found that for developing countries, the increase in trade intensity could also have a positive and significant impact on the synchronization of their business cycles. The effect was relatively small, and the main reason for this difference between developed and developing countries was the different models of bilateral trade and specialization between these countries.
The research of Faia (2007) showed that countries with similar financial structures were more relevant to business cycles. He argued that when the two countries implemented a trusted exchange rate and opened trade, the synergy of output increased. When their financial openness increased, the synergy of output decreased.
2.2 The Profitability of Banks Overview
Bank performance is the result of measuring the input and output of banks over a period of time. At present, there are many mature enterprise performance evaluation systems in the academic world, such as economic value added (EVA) models, performance pyramid models, and strategic balance scorecard models. However, there are still different opinions in the academic circles regarding the measurement of bank operating performance. There are also many studies on this subject. Since the 1990s', a relatively large amount of research has conducted to measure the bank profits over time.
2.2.1 Bank Performance Evaluation System
The CAMELS Rating System has been widely used by major financial institutions and financial analysts around the world. The system mainly detects and evaluates the sensitivity of financial institutions to market risks. There are also a lot of studies using ROA, ROE, net interest income, and other indicators to bank performance, including the bank's financial ratio, macroeconomic factors, etc. Molyneux and Seth (1998) researched banks in the United States and found that the risk-adjusted capital ratio is a decisive factor affecting bank profits. Demirgüç-Kunt and Huizinga (1999) thought differences in interest margins and bank profitability reflect a variety of determinants that was bank characteristics, macroeconomic conditions, bank taxation, deposit insurance regulation, overall financial structure, and underlying legal and institutional indicators. Goddard et al. (2004) used ROE to measure the profit of European banks. They found that the relationship between bank size and bank profit was fragile.
2.2.2 Factors Affecting Bank Earnings
Factors affecting bank earnings are another specific topic of bank performance research about the comparison between traditional deposit-loan banking and financial activities. Albert and Alexandre (2013) analyzed the impact of economic and financial factors on banks' earnings through a subsection of net income. They used the European Banking Group to identify significantly impacted and clarified the type of activity during the period 2005-2010 that was a period of significant changes in bank income. They found that net profit was positively affected by GDP growth and the stock market, and for most banks, negatively affected by interest rates.
Duraj (2015) studied the profitability behavior of bank-specific industry-related and macroeconomic determinants. He analyzed internal and external factors and their relationship to the profitability of the banking sector. His research results showed that the profitability of a bank affected by factors related to operating decisions, internal factors, and also the impact of changes in the external macroeconomic environment. Recently, the study of Adalessossi and Erdoğan（2019）also proved this fact. In the paper of these two authors showed that the significant impact of bank-specific factors and banking industry and macroeconomic factors on WAEMU profitability.
2.3 The Relationships between the Business Cycles and the Profitability ofBanks Overview
2.3.1 The Relationship between Business Cycles and Credit
About the impact of business cycles on bank credit, King and Levine (1993) used data on 80 countries from 1960 to 1989 to conduct an empirical analysis of the relationship between bank credit supply and economic growth. The result of the study was that the two had a strong positive correlation.
Diamond (1984) mentioned that bank credit behavior was subject to the influence of information asymmetry. Specifically, the degree of information asymmetry was relatively high during the economic depression; the degree of information asymmetry was low during the economic prosperity period. As a result, the bank increased the credit scale during the economic boom period and reduced credit during the economic depression period. Therefore, bank credit had a procyclicality, and this procyclicality had exacerbated economic change.
Based on relevant bank credit data from some countries in the Organization for Economic Co-operation and Development (OECD) from 1979 to 1999, Borio et al. (2001) found that the ratio of bank credit to GDP accelerated significantly in the period of steady economic growth and it declined significantly during the economic recession.
2.3.2 Research on the Relationship between Business Cycles and Bank Risk
On the one hand, some scholars believe that business cycles and bank risk are positively related. Acharya and Naqvi (2012) stated that the relationship between business cycles and bank risk. When the economy went up, the bank would overestimate asset prices and formed asset price bubbles. Inside the bank, loan officers were paid based on the volume of loans, and volume-based compensation was also affected by the liquidity shortfall of a bank. In this mechanism, loan officers often took risky behaviors to obtain personal benefits, which would lead to a rapid increase in the amount of credit. Of course, as bank performance increases, risks also increased; while the economy was in a downturn, investors would choose to reduce direct investment and held more bank deposits, which would increase bank liquidity and reduced performance.
Castro (2013), using the processing method of dynamic panel data, analyzed bank data in the five countries of Greece, Ireland, Portugal, Spain, and Italy from the first quarter of 1997 to the third quarter of 2011. The result showed that the macroeconomic environment had a significant impact on credit risk. That was, as GDP raised, the bank's credit risk increased.
On the other hand, more scholars believe that business cycles and bank risk are negatively related. Demirgüç-Kunt and Detragiache (2000) thought that macroeconomic factors had played an essential role in triggering the banking crisis. Further, they pointed out that the adverse economic environment, which was often accompanied by low economic growth, high unemployment rate, high-interest rates, and high inflation rate facilitated the occurrence of the banking crisis. Demirgüç-Kunt and Detragiache (2005) confirmed the results in a follow-up study.
Ali and Kevin (2010) investigated the data of Australia and the United States from 1995Q1 until 2009Q2. They found that the macroeconomic development status was a significant negatively related explanatory variable of bank default risk. Louzis et al. (2012) found that the real GDP growth and the bank's non-performing loans showed a negative correlation after analyzing the dynamic panel data of nine Greek banks from 2003Q1 to 2009Q3.
Mileris (2014) analyzed the macroeconomic factors and their impact on the percentage of non-performing loans of commercial banks in EU countries. The research results of this paper had specific reference value for banks, and it revealed the relationship between macroeconomics and non-performing loans. Since 2009, Lithuania had one of the highest non-performing loan ratios in the EU, and the significant impact of the economic downturn on debtors' ability to repay bank debt had proven. The same situation confirmed in other EU countries with imperfect economic conditions. In contrast, the banking systems of developed EU countries were not very sensitive to changes in business cycles. 2.3.3 Research on the Relationship between Business Cycles and Bank Profitability In the theoretical research on the procyclicality of bank operation, Bernanke and Gertler (1990) thought that the operation of banks through the credit channel, a transmission
mechanism of the economic cycles, amplifies the impact on the real economy and forms the "financial accelerator" effect, which led to sharp fluctuations in the economy. In empirical research, King and Levine (1993) analyzed the data of about 80 countries from 1960 to 1989 and tested the relationship between financial development and economic growth. The result was that financial development is an essential factor driving the economy. Levine and Zervos (1998) added the scope of financial development to stock development on the basis of King and Levine (1993). They concluded that stock development and bank operation could promote economic growth. Segoviano and Lowe (2002) believed that the procyclicality of commercial bank operations directly affected the development and stability of business cycles in countries that implement indirect financing.
Concetta Chiuri et al. (2002) studied the impact of commercial banks from countries that experienced financial crises on the business cycles and pointed out that the growth rate of loans had a more significant effect on the growth rate of GDP. The financing channels of the real economy sector in these countries were mainly bank loans, so there was a clear characteristic relationship between economic fluctuations and commercial banks.
Albertazzi and Gambacorta (2009) studied the link between economic cycles fluctuations and the profitability of the banking sector and how this link affected by institutional and structural characteristics. The study estimated a set of net interest income, non-interest income, operating costs, reserves, and pre-tax profit equations of banks in major industrialized countries for assessing the impact of macroeconomic and financial factors on bank profitability.
Glen and Mondragón-Vélez (2011) selected relevant data of commercial banks in developing countries from 1996 to 2008 and analyzed the impact of business cycle fluctuations on the performance of commercial bank loans. Research had found that economic growth and interest rate fluctuations were the main factors affecting bank loan performance. Under extreme economic pressure, loan loss provisions and business cycles would show a highly non-linear relationship.
Olszak et al. (2018) studied the sensitivity of loan-loss provisions in commercial and cooperative banks to the business cycles. The data of commercial and cooperative banks were from Poland between 2000 and 2012. The result was that although the loan-loss provisions were procyclical both in commercial and cooperative banks, the procyclicality of commercial banks was particularly prominent and more influential than cooperative banks.
3.
Data and Research Method
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The chapter includes the selection of data.The selection of research method basing on the type of data also will be given.
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3.1 Selection of Variables
Table 1 The choice of the dependent variable, independent variables and control variables
Variable classification Variable name Variable symbol
Dependent variable Return on assets ROA
Independent variables GDP growth rate GDPGR
Unemployment rate UNEM
Industry value added growth rate IVAGR
Control variables Net interest spread NIS
Non-performing loan ratio NPLR
Loan growth rate LGR
Firstly, the dependent variable is an indicator related to the profitability of the bank. Usually, these indicators include ROA, ROE, operating profit margin, and so on. The article selects ROA as the dependent variable. Generally, the larger this indicator is, the stronger the profitability or the higher the profitability level of a commercial bank is. The reason why we choose ROA is that the banking is a highly leveraged industry, and the ROE under the high leveraged will produce distortion. Some banks have higher ROE because their leverage ratio is higher. However, the bank's net assets, under the leverage
of nearly 20 times, contribute little to the bank's profit. The net assets are just to meet the capital adequacy ratio to reduce the risk of bankruptcy. So, we think that measuring the profitability of the bank needs to focus on ROA.
Secondly, the independent variables related to the business cycles is the GDP growth rate (GDPGR), the unemployment rate (UNEM) and the industry value added growth rate (IVAGR). Specifically, the performance of GDP growth rate and the business cycles is relatively consistent in China. Therefore, the paper uses the GDP growth rate (GDPGR) to measure China's business cycles. A more detailed section is at 4.1.1 Review of China's Business Cycles Fluctuations. In addition to the GDP growth rate, we also select the indicators which can reflect the macro-regulation objectives, such as unemployment rate and industry value-added growth rate, to measure the business cycles. The unemployment rate is the main indicator reflecting the unemployment status of a country. The unemployment rate and economic growth rate have corresponding opposite changes. Therefore, it is a countercyclical indicator. The industry value added growth rate reflects the growth rate of the economic scale. Industry value added growth rate is a basic indicator of national economic accounting. The sum of the added value of all sectors is the GDP. Industry value added is the value created by industrial enterprises in a certain period and is an integral part of GDP.
In addition to the indicators relating the business cycles impact ROA, there are some other factors related to the profitability of banks could impact ROA. That is what we chose as the control variables here, namely the net interest spread (NIS), the non-performing loan ratio (NPLR), and the loan growth rate (LGR).
At present, China's banking industry is still dominated by traditional deposit and loan services, and banks' interest income accounts for a very high proportion. In this case, the bank's net interest spread basically reflects bank performance. Banks with high net interest spread tend to perform well, while banks with low net interest spread perform worse. The formula of net interest spread is net interest income / total bank assets (= interest income minus interest expenses/ total bank assets). So, we use net interest spread (NIS) to reflect the level of dependence of commercial banks on the interest-oriented profit model.
There are many kinds of research on bank risk. But so far, there is no unified indicator on the measurement of bank risk. Indicators such as risk-weighted assets to total assets, Expected Default Frequency (EDF), and Altman Z-Score can be seen in many studies. The reason why the paper chooses the non-performing loan ratio (NPLR) as an indicator to measure bank risk is determined by the current structure of Chinese banks. In China's banking industry, the proportion of loans in all the businesses is still absolute. So, banks must absolutely pay attention to the situation of changes in loan risk. According to Zhang (2011), the rapid increase in the number of non-performing loans often meant a sharp deterioration in bank risks in China. Generally speaking, low NPLR indicates an excellent bank performance, and high NPLR shows a poor bank performance. Therefore, we use NPLR to reflect the loan quality of commercial banks and the extent to which loan quality affects the profitability of banks.
Loan growth rate reflects the speed of bank loan expansion. Due to the current situation in China where traditional deposit and loan services are the main business. The formula of loan growth rate is present loan divided by loan for the previous year and then minus one.
3.2 Selection of Data
The paper uses the annual data of 34 biggest listed banks in China from almost 2001 to 2018 as the research object (the time these banks go public is different, and they did not release the annual report before going public, so the beginning of data of some banks are not from 2001). Of the 34 banks we selected, five large commercial banks, nine national joint-stock commercial banks, 13 city commercial banks and seven rural commercial banks. The specific banks' name list is in Appendix 1.
The reason why the data of these 34 banks are selected as the research sample is mainly due to the following reasons. First, representation. According to the China Banking and Insurance Regulatory Commission (n.d.), the total assets of these 34 banks are large and accounting for more than two-thirds of the total assets of all banks in China. Therefore, the data we choose is relatively representative and can effectively represent the risk and performance levels of China's banking industry. Second, accuracy. Since we selected the
data of listed banks, based on the listing requirements that the annual report can only release after strict audits, the accuracy of the data should be guaranteed. Finally, availability. The data before China's economic system reform is challenging to obtain. And only the financial statements of listed banks are published regularly and easily get. Therefore, we choose the listed banks as the research sample.
We have the 34 listed banks as the research sample. However, the time these banks go public is different. Therefore, the number of observations is not 612 (34*18), which is 408. The ROA, net interest spread, non-performing loan ratio, and loan growth rate in this paper all come from the annual report of 34 listed banks. The macro-level variable GDP growth rate, unemployment rate, and industry value added growth rate derived from the National Bureau of Statistics of China and the World Bank.
3.3 Research Method
Regarding the research method, the paper uses quantitative analysis. Based on statistical data reflecting objective facts, mainly including annual report data of 34 listed commercial banks almost from 2001 to 2018, quantitative analysis measurement the relationship between business cycles and the profitability of commercial banks in China by using the method of panel data analysis. After that, based on related theories and combining with China's actual situation, transmission mechanism of the impact of business cycles on commercial banks' profitability will be explained.
According to Gujarati and Porter (2009), time series data included one or more variables over some time. Cross-section data collect one or more variables for several subjects or sample units at the same point in time. And panel data observe one or more variables in several subjects or sample units over a period of time, which have time and space dimensions. In the paper, the data we collected is return on assets (ROA), net interest spread (NIS), non-performing loan ratio (NPLR), and loan growth rate (LGR) for 34 commercial banks of China for the period 2001–2018. All of those variables have time and space dimensions, which means that they belong to panel data.
Because the data we used in the paper is panel data for multiple indicators of listed banks, the analysis we choose is a panel data regression model. Compared with the time series
model, the panel data model contains more sample information, making the estimation results of the variables more effective and objective (Baltagi, 2005).
The panel data constructed in Eviews 11 include variables that reflects the operations of commercial banks and China's business cycles. The econometric model we established is as follows:
𝑅𝑂𝐴𝑖𝑡1 = α1 + 𝛽11𝐺𝐷𝑃𝐺𝑅𝑖𝑡 + 𝛽21𝑁𝐼𝑆𝑖𝑡+ 𝛽31𝑁𝑃𝐿𝑅𝑖𝑡+ 𝛽41𝐿𝐺𝑅𝑖𝑡+ 𝜀𝑖𝑡1
𝑅𝑂𝐴𝑖𝑡2 = 𝛼2+ 𝛽12𝑈𝑁𝐸𝑀𝑖𝑡+ 𝛽22𝑁𝐼𝑆𝑖𝑡+ 𝛽32𝑁𝑃𝐿𝑅𝑖𝑡+ 𝛽42𝐿𝐺𝑅𝑖𝑡 + 𝜀𝑖𝑡2 𝑅𝑂𝐴𝑖𝑡3 = 𝛼3+ 𝛽13𝐼𝑉𝐴𝐺𝑅𝑖𝑡+ 𝛽23𝑁𝐼𝑆𝑖𝑡+ 𝛽33𝑁𝑃𝐿𝑅𝑖𝑡+ 𝛽43𝐿𝐺𝑅𝑖𝑡+ 𝜀𝑖𝑡3
Specifically, this project targets testing and validating the following hypothesis:
Hypothesis H1: The business cycles have a positive effect on the profitability of commercial banks in China.
Hypothesis H2: The business cycles have a negative effect on the profitability of commercial banks in China.
Hypothesis H3: The business cycles have no effect on the profitability of commercial banks in China.
As mentioned above, after we determined the purpose of the paper, we chose the dependent variable, independent variables, control variables, and we chose the specific banks based on the situation of China. What’s more, we established an econometric model and identified three hypotheses. Next step, we will perform the regressions by using the panel data analysis. However, before performing regression on these three models, we need to do a series of tests first, including a correlation test and cointegration test, to ensure that our regression results are reliable. Therefore, the specific steps in the part of empirical findings include correlation, the cointegration test of panel data, and regression. Specifically, the panel data model comprises the pooled regression model (Pooled), fixed effects model (FEM), and random effect model (REM). In the part of empirical findings, we will perform all the three models to accurate our research.
4.
Empirical Finding and Analysis
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The purpose of this chapter is to present the empirical finding of the impact of business
cycles on commercial banks' profits. The content about descriptive statistics of data and
the analysis of the influence of business cycles on commercial banks' profit models from different perspectives also will be given.
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4.1 Descriptive Statistics of Data
Before conducting empirical analysis, it is necessary to explain the phenomenon shown by the data.
4.1.1 Review of China's Business Cycle Fluctuations
In December 1978, China carried out economic reforms and formulated the goal of transforming the planned economic system into a market economic system. Since then, due to reform in the agricultural, industrial, and the introduction of foreign businesses, the Chinese economy had improved significantly compared to the past. Since about 40 years of economic reform, China's economic system had roughly gone through four stages, namely the period of preliminary exploration of developing productive forces from 1978 to 1991, the period of building a macroeconomic management framework from 1992 to 2002, the period of the rise of China's economic growth theory from 2003 to 2011, and the period of reconstruction of the macro-control system in the new normal from 2012 till 2018 (Zhang, 2018). At the end of 2001, China officially joined the World Trade Organization (WTO), which significantly improved China's international trade conditions, investment environment, and brought a strong impetus to the economy. However, at the same time, China's economic structure had also undergone significant changes, and the fluctuation characteristics of the business cycles had also significantly changed from before. Therefore, the section will describe the economic fluctuations of China and focus on the two recent economic volatility in combination with Figure 1.
Figure 1 The GDP growth rate, unemployment rate and industry value added growth rate of China from 1979 to 2018
Source: Adapted from National Bureau of Statistics of China and the World Bank
According to the figure above, we can see that China roughly experienced four business cycles since the Chinese economic reform, with every cycle of about ten years. This was in line with the four-phase economic system reform. 1979 to 1989 was the period with the most frequent economic fluctuations, but it could also be roughly seen that the first five years showed an upward trend and the next five years showed a downward trend. The second business cycle began in 1990. The frequency of economic fluctuations during this period was not as frequent as the first cycle, but the economic changes were higher, especially in the industry value added. The early stages of this cycle showed a definite upward period; however, the overheating of the economy soon appeared. The industry value added even exceeded GDP from 1994 to 1997. Then the Chinese central government adjusted its policies to control the situation, but the economy also moved falling period. After the 21st century, this was the third business cycle, and the frequency and amplitude of fluctuations were gentler than the previous two cycles. The Subprime mortgage crisis was a turning point in the third cycle and fourth cycle. From 2009 to 2018 is the fourth business cycle. The fluctuation of the GDP growth rate was relatively gentle and showed a slow decline during the period.
Specifically, the third business cycle began in 1999 and continued into 2009. In 1997, the outbreak of the Asian financial crisis caused a deflation in China. To eliminate the impact
of the financial crisis, the central government began to implement a proactive fiscal policy focusing on the issuance of long-term treasury bonds, expanded the floating degree of loan interest rates three times, and implemented a prudent monetary policy. After that, the Chinese economy increased. From 2000 to 2002, the GDP growth rate steadily rose to 9.1%. The GDP growth rate even reached more than 10% in 2003. At this time, aggressive fiscal policy brought severe inflation. Excessive investment and inflation were mainly due to the enormous investment demand for raw materials and other commodities used to build factories, roads, and other infrastructure projects. At the end of December 2004, real estate prices in Shanghai, Hangzhou, and other places rose rapidly. The objectives of the macro policy were adjusted to control excessive investment and the excessively high asset prices, and the active fiscal policy and prudent monetary policy were transformed into extensive use of exchange rates, interest rates, and open market business, etc. However, due to time-lag effects, these policies did not work in time. The main problems facing the economy in 2006 was still excess liquidity and high inflation. In 2007, China's GDP growth rate was as high as 14.2%, and economic growth reached its peak in the current business cycle.
Excessive investment had brought about an unreasonable pattern of economic growth. In early 2007, the government continued to implement a prudent fiscal policy, alleviating the pressure of excess liquidity through taxation and government purchases. The impact of the 2008 US subprime mortgage crisis swept the globe. The unexpected financial crisis had severely affected the global economy. Against the backdrop of the slump in the global economic situation, China had inevitably experienced an economic slowdown, rising unemployment, and increasing bank non-performing loans. And the GDP growth rate decreased from 14.2% in 2007 to 9.7% in 2008. At the end of 2008, the Chinese central government had raised the export tax rebate three times and had lowered the reserve requirement four times. It could be seen that the macroeconomic policy has once again transformed into a proactive fiscal policy and a moderately loose monetary policy. Although these policies had played an essential role in the steady development of the economy, the Chinese economy was still adversely affected by the financial crisis under the trend of economic globalization.
The fourth economic fluctuation began in 2009 and have continued to this day. In 2010, the adverse effects of the subprime mortgage crisis gradually subsided, the global economy was recovering to varying degrees. Driven by the favorable international economic situation, the GDP growth rate of China returned to 10.6% and stabilized, which indicated that the Chinese economy had entered a new business cycle. However, at the beginning of 2011, due to the inefficient allocation of resources and the stagnation of technological innovation capabilities, China's economy had begun to enter a new normal. And the GDP growth rate had plummeted from 9.5% in 2011 to 6.6% in 2018. In short, China ’s economic situation is slowly declining in the fourth business cycle.
4.1.2 Chinese Banking Performance
Table 2 Statistic description of samples
Variable Mean Median Standard
Deviation Minimum Maximum Observations
ROA 0.865348 0.900553 0.329539 0.01 1.865894 408
NIS 2.145249 2.19 0.509386 0.51 3.703655 408
NPLR 2.990049 1.395 5.366009 0.17 42.12 408
LGR 20.69868 17.54084 14.55953 -32.60398 117.21 408
Source: Adapted from the annual reports of 34 listed banks
From Table 2, the statistical description of the bank performance indicator ROA shows that the return on total assets of China's listed banks is not very high, with an average of 0.87%, which is in the middle-lower level in the ranking of the return on assets of banks around the world. Under government control, the Chinese banking industry relies too much on net interest spread income. So, for Chinese banks, the size of the net interest has also become one of the most critical factors determining bank performance.
Except for a small number of banks with high NPL ratios in a certain period, basically, the NPLR of each bank is relatively low, and there is a downward trend year by year. By searching the original data, we found that the NPLR of the Agricultural Bank from 2001 to 2007 was very high, of which the NPLR in 2001 was even above 40%. However, it quickly dropped to less than 5% in 2008. Actually, the NPLR of lots of banks was
relatively high before the reforms in the Chinese banking industry, and after the reforms, the NPLR decreased significantly.
The mean of the loan growth rate was very high, and the maximum value was even above 110%. By searching the original data, we found that the loan growth rate of the most bank is stable between 10 % and 20% in recent years, which indicates that the size of most banks is expanding year by year.
4.2 Analysis of the Influence of Business Cycles on Commercial Banks' Profit Model
4.2.1 An Overview of Commercial Banks' Profit Model
The profit model of a commercial bank refers to the dominant financial revenue and expenditure structure of a commercial bank based on a specific asset-liability structure under a specific market mechanism environment and economic development level.
The profit model of commercial banks contains many types. Different research scholars have different classifications. There is no unified conclusion on the classification of profit models in theory and practice. Chen (2006) divided the profit model from the perspective of commercial banking into the corporate banking business profit model, retail banking business profit model, intermediate business profit model, and private banking business profit model.
Zhang (2009) believed that the profit model of commercial banks divided into an interest-oriented profit model and a noninterest-interest-oriented profit model. This division, based on the income structure, mainly reflected in whether the primary source of income is interest income or non-interest income. The interest-oriented profit model mainly based on traditional interest income, and the business mainly depended on traditional deposit and loan business. In contrast, the noninterest-oriented profit model mainly depended on the intermediate business, private banking, retail banking, and mixed operating. Whether from the income or the business perspectives, the profit models of commercial banks are related, and there is an internal connection. Based on the current main income structure of commercial banks, this article divides the profit model into two types: traditional
4.2.1.1 The Traditional Business Model (Interest-Oriented Profit Model)
The interest-oriented profit model is that commercial banks make profits through the interest income generated by the difference between deposit and loan. In business development, they continue to expand the scale of loans to ensure the increase in profits.
Therefore, under the interest-oriented profit model, the profitability of commercial banks depends on the deposit-to-loan spreads, and the scale of the loan. Among them, the deposit-to-loan spreads refer to the interest difference between the loan business and deposit business of commercial banks. Loan business refers to the business of using commercial banks as lenders and supplying a certain amount of monetary funds to borrowers under certain loan policies and on the condition of repayment of principal and interest. The loan business is a relatively large business item in the asset business of commercial banks. Deposit business refers to the business where commercial banks accept deposits from depositors and can withdraw funds at any time or regular intervals, subject to interest payment.
The interest income driven by the growth in the size of interest-earning assets has become the main source of profit for China's banking industry. From Figure 2, we can see that in the income composition of China's 32 listed commercial banks that could find in 2018, interest income accounted for 78%, far exceeding non-interest income that is 22%.
Figure 2 China's commercial bank interest income and non-interest income ratio
4.2.1.2 Non-Traditional Business Model (Noninterest-Oriented Profit Model)
The non-traditional business profit model is corresponding to the noninterest-oriented profit model. The noninterest-oriented profit model mainly refers to the commercial banks through the development of intermediary business, off-balance sheet business, etc., to obtain non-interest income. Specifically, intermediary business refers to the financial service business in which commercial banks rely on their professional and technical advantages to collect payments, payments, and other matters for customers and obtain handling fees from them. Compared with China, developed countries and regions have more mature intermediary business development and more balanced development between public and private businesses. Non-interest income accounts for a large proportion of bank profits, which not only makes commercial banks obtain huge profits, it has also become an essential platform for commercial banks in developed countries to give full play to their respective professional advantages. According to data from the Bank of China Institute of International Finance, in recent years, the proportion of the non-interest income of the four major US banks (JPMorgan Chase, Bank of America, Wells Fargo, Citigroup) has averaged between 35-55%. The proportion of interest income of JPMorgan has long been at a leading level, reached 49.7% in 2017. From the perspective of the Japanese banking industry, large Japanese banks generally actively expand non-interest income. Driven by a series of robust measures, the proportion of the non-interest income of the three major banks (Mitsubishi UFJ, Sumitomo Mitsui Banking Corporation, Mizuho Bank) in Japan increased year by year, reached an average of 56.4% in 2017. Among them, Sumitomo Mitsui Banking Corporation had the highest rate of 62.4%.
On the whole, China's commercial banks make profits mainly through the interest income generated by the difference between deposit and loan now. Therefore, the following analysis is based primarily on an interest-oriented profit model.
4.2.2 Overview of the Relationship between China's Business Cycles and the Profitability of Commercial Banks
Figure 3 China's economic growth rate and ROA growth of commercial banks trend comparison
Source: Adapted from National Bureau of Statistics of China and the annual reports of 34 banks
The weighted average of the ROA growth rate of China's 34 listed commercial banks from 2001 to 2018 represents the fluctuation of the profitability of China's commercial banks and compares the growth rate of ROA with the growth rate of various indicators representing business cycles (as shown in Figure 3). It can be found that the direction of the fluctuation of the profit level of China's commercial banks and the direction of the fluctuation of the Chinese business cycles are reversed. From 2001 to 2018, the ROA growth rate of China's commercial banks generally showed an upward trend. However, in view of the overall situation, except for the unemployment rate, the other two indicators that represent the business cycles generally show a downward trend.
It can be seen from the comparison of the economic growth rate and the ROA growth rate, and there is a specific difference between the fluctuation of the profit level of China's commercial banks and the fluctuation of the business cycles. The fluctuation rate of the economic growth rate is larger than the fluctuation of the ROA growth rate. The fluctuation direction of the two is the opposite. It shows that the profit of Chinese commercial banks has the characteristics of reverse business cycles. However, how the business cycles affect the profitability of commercial banks and the specific degree of the
impact are yet to be analyzed by the following business cycles impact mechanisms and empirical analysis.
4.2.3 Analysis of the Impact of Business Cycles on Bank Profitability from the Perspective of Loan
Under the interest-oriented profit model, the profitability of banks is not only closely related to the level of interest rate spreads. At the same time, the profitability of commercial banks is also related to the growth rate of the scale of loan and loan quality.
The level of loan quality determines the size of a commercial bank loan and the level of profitability. Therefore, analysing the impact of the business cycles on bank loan has become a meaningful way to investigate the relationship between the business cycles and banks’ profitability, and analysing the impact of the business cycles on banks’ loan would necessarily involve the impact of the business cycles on the scale of loan and loan quality.
4.2.3.1 Analysis of the Impact of Business Cycles on Bank Profitability from the Perspective of the Scale of the Loan
Among the assets of commercial banks, the most substantial proportion exists in the form of loans, while the size of the loan and the business cycles have a significant relationship. In theory, when the economy is in a reasonable period, investment demand is strong, commercial banks are more optimistic about the economic situation, and restrictions on loan loss provision and capital will be more relaxed. Central banks and even commercial banks will expand the scale of loans to stimulate economic growth and make money from it, thereby improving the profitability of banks. On the contrary, when the macroeconomy is in a contracting phase, investment demand is also reduced, coupled with the deterioration of borrowers' repayment ability, which leads to banks' control of loan risk and the strengthening of the supervision of the central bank. All of the above measures will reduce the scale of the loan. The level of profitability of commercial banks will reduce accordingly. From the above theoretical analysis, we can find that the scale of loans and the profitability of commercial banks should be pro-cyclical.
However, the income of the loan is not only related to the size, but also the quality of the loan. Under the premise of quality assurance, the size of the loan may be positively
be combined with several aspects. The analysis of the scale of loans is only one aspect. The specific impact on the bank's profit is positive or negative, and we need to combine the quality of the loan and other related factors in the same period.
Figure 4 Comparing the trends of China's economic growth rate and loan growth rate
Source: Adapted from National Bureau of Statistics of China and the annual reports of 34 banks
Looking at the Figure 4 above, comparing the trends of China's economic growth rate and loan growth rate from 2001 to 2018, we can find that the phased trends and directions of change in economic growth rate and loan growth rate are basically the same. Moreover, the changing trend of LGR lags behind the trend economic growth rate. Especially since the Subprime mortgage crisis in 2008, the decline and increase of the loan growth rate are in line with the decrease and increase of the economic growth rate, which shows that China's cycles of loan have the characteristics of pro-business cycles.
4.2.3.2 Analysis of the Impact of Business Cycles on Bank Profitability from the Perspective of Loan Quality
The loan scale has a positive impact on commercial banks' profitability under the precondition of credit quality assurance. Loan quality refers to the likelihood that commercial banks can successfully recover loans from borrowers. Usually, many factors affect the quality of the loan, such as macroeconomic policies, corporate profitability, and the integrity of borrowers, etc. One of the widely accepted standards for evaluating the quality of this loan is the non-performing loan ratio. Non-performing loans will not only directly cause losses to commercial banks' profits but will also limit the scale of the loan.
In theory, with macroeconomic fluctuations, repayment factors such as the repayment ability of physical enterprises will also change. That would form non-performing loans of commercial banks when borrowers' ability to repay goes down. And the amount of non-performing loans directly affects the profitability of commercial banks, and the total amount of loans available to commercial banks leads to changes in the size of the loan, which further affects the profitability of commercial banks.
Qian (2000) believed that deflation was mainly caused by non-performing loans. If the credit deflation was formed, it would produce the expectation of worsening non-performing loans, thus making the problem of credit deflation more serious. As a result, the credit crunch and non-performing loans were intertwined, forming a vicious cycle trap of "credit crunch-non-performing loans."
Figure 5 Comparing China's economic growth with the trend of NPL ratio
Source: Adapted from National Bureau of Statistics of China and the annual reports of 34 banks
Looking at the Figure 5 above, comparing China's economic growth with the trend of NPL ratio from 2001 to 2018, we can find that the phased trend and direction of change of economic growth and NPL ratio are opposite. Since 2001, China's non-performing loan rates had been declining year by year, especially since the Asian financial crisis in 2008. From 2009 to 2012, the growth rate of the non-performing loan ratio had obviously decreased with the increase in the economic growth rate. By 2012, the non-performing
loan ratio of China's commercial banks had fallen to 1.56%. This shows that the non-performing loan ratio has a counter-cyclical relationship with the business cycles.
4.2.4 Analysis of the Impact of Business Cycles on Bank Profitability from the Perspective of Interest Spread
4.2.4.1 The Impact of Net Interest Spread on the Profitability of Commercial Banks
Net interest spread is an indicator to measure the net interest income of commercial banks. The net interest spread derived from the difference between commercial bank loan interest income and deposit interest expense, which is an important part of commercial bank profitability. According to the Federal Reserve Bank of St. Louis (n.d.), the bank's interest income to total income for the United States from 2010 to 2017 was 60%-70%, for Euro Area was 60%-80%, for China were 70%-80%. It can seem that whether it is for commercial banks in developed countries or domestic, commercial banks, the net interest spread occupies a pivotal position and have an important impact on the operation of commercial banks.
China's commercial banks are an interest-oriented profit model, so net interest spread is an important part of China's banks. Although China's commercial banks have been pursuing a profit model transformation in recent years, the interest income of major commercial banks still accounts for more than 60% of total income. In addition to the immature development of China's financial industry, the reason for this phenomenon is that the control of deposit interest rates by the Central Bank of China for many years.
Since the founding of China in 1949, to restore productivity as soon as possible, stabilize prices, and promote the system reform at that time, China has chosen long-term interest rate control. Before the 1980s, this way of planning the economy provided a reliable guarantee for China's financial and economic development. However, after the reform and opening up, the situation has changed, the degree of opening to the outside world has continued to expand, and the domestic economy has developed rapidly. For the sustainable development of the Chinese economy, the central bank decided to lift strict interest rate control gradually.
After preliminary attempts in 1999, 2004, and 2005, in June 2012, the central bank adjusted the upper limit of the deposit interest rate to 1.1 times the benchmark interest rate. This was the first time allowed a limited increase in bank deposit interest rates, marking the official start of the marketization of RMB interest rates. In July 2013, China lifted the lower limit of the loan interest rate. The bank and the borrower freely negotiated the loan interest rate. Compared with the liberalization of the loan interest rate, the deposit interest rate still had an upper limit. In October 2015, the central bank lifted the upper limit of deposit interest rate control.
After the deposit interest rate ceiling disappeared, the marketization of China's deposit interest rate was basically completed. However, this did not mean that China's interest rate marketization reform is entirely over. Compared with countries that have already completed the reform of interest rate liberalization in the world, China's deposit interest rate liberalization was still insufficient. Moreover, after removing the relevant restrictions on interest rate fluctuations, it did not mean that the central bank has wholly abandoned the rationale for interest rates but rather intervened through the transmission system and monetary policy tools. Before the interest rate determined by market supply and demand forms a perfect system, the central bank will still publish the benchmark interest rate to provide an essential reference for the pricing of financial institutions. This is conducive to stabilizing the market in a short period, but it still cannot fully offset the impact of the marketization of deposit rates on financial institutions in the long run.
In fact, due to the establishment of the market interest rate pricing self-regulatory mechanism in 2013, even after the upper limit of deposit interest rates lifted, banks formed an "interest rate alliance". They imposed industry self-regulation on the rise of deposit interest rates. For example, the current deposit interest rate ceiling for banks is generally around 40%-50% higher than the benchmark, and it is difficult to break through this range.
Regarding the analysis of bank interest spread and their influencing factors, the analysis background is different in China and other countries, and the emphasis is also different. Developed countries have implemented interest rate liberalization and believe that in the interest rate liberalization environment, the spread depends on the bank's operating
