Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 16, No. 3, 2024 164 Economic Development and the Development of the Securities Market: A Study of China's CSI 300 Index Aiping Luo 1, Shuoyan Lin 2, *, Beibei Hong 2 1 Guangzhou Beijing Road Branch, Industrial and Commercial Bank of China, Guangzhou, 510000, China 2 School of Economics, Guangzhou College of Commerce, Guangzhou, 510000, China Abstract: The Chinese securities market has experienced over two decades of development and has emerged as a significant global investment market. However, compared to other developed securities markets, it still faces issues such as an inadequate institutional framework, irrational investor behavior, and imperfect mechanisms. To explore the relationship between China's economic development and its securities market, this paper employs Eviews time series analysis and VAR modeling for empirical research, aiming to uncover the relationship and operational mechanisms between the two. The findings indicate a certain correlation between economic development and securities market development in China. The variance decomposition reveals that the contribution of economic development to the securities market is relatively low, highlighting a major issue within the Chinese securities market. Consequently, this paper proposes reform suggestions from various perspectives, including institutional, investor, and listed company aspects, to guide the healthy growth of the Chinese securities market and create a favorable environment for economic development. Keywords: Securities market, Broad money supply, Time series, VAR model. 1. Introduction The securities markets in developed European and American countries began earlier, with the establishment of the world's first stock exchange in the early 17th century in the Netherlands, the founding of the London Stock Exchange in the 1870s, and the creation of the New York Stock Exchange in the 1920s. In contrast, China's securities market started later, launching pilot operations in the 1990s with the establishment of the Shenzhen Stock Exchange and the Shanghai Stock Exchange. Despite its relatively short history of just over thirty years, it has achieved remarkable milestones. According to statistics from the China Securities Depository and Clearing Corporation, by February 2021, the number of investors in China reached 180 million. As of March 2021, the total market capitalization of stocks on the Shanghai and Shenzhen exchanges exceeded 78 trillion yuan, with a circulating market value exceeding 64 trillion yuan, and a total of 4,248 listed companies, indicating strong financing capabilities. The achievements of the Chinese securities market place it in a leading position globally. In 1991, the initial market capitalization was only 10.919 billion yuan, but it has attracted attention from global investors over the past thirty years. Analysis of the investor composition in the Chinese securities market reveals a yearly increase in the proportion of foreign and institutional investors, indicating a trend toward more rational investor behavior focused on value investment, with a growing internationalization of the investor base. The market is gradually maturing, with the issuance system transitioning from an approval-based to an approval-in-principle system, and the gradual advancement toward a registration system. However, it is undeniable that due to its late start, the Chinese securities market still lags behind its developed Western counterparts, facing challenges such as the singular function of securities firms, a relatively small scale of the securities industry, insufficient capital market functionality, and imperfect regulations. The relationship between the securities market and economic development is a popular research topic among economists both domestically and internationally. In developed economies with mature securities markets, the relationship between the two has not yet reached a unified conclusion, and the situation is even less clear in China. Therefore, there is a pressing need to study the relationship between the emerging Chinese securities market and economic development. Researching whether a correlation exists and analyzing the mechanisms of influence can facilitate improvements in the Chinese securities market and promote the functioning of the macroeconomy. Ultimately, this research aims to provide a theoretical foundation for China's future economic development. 2. Literature Review Mukherjee T.K. et al. (1995) conducted a co-integration analysis of the Tokyo Stock Exchange index with macroeconomic data such as GDP and CPI, concluding that there exists a correlation between Japan's macroeconomy and its stock market, indicating that stock market fluctuations can partially explain macroeconomic operations. Adamopoulos Antonios (2010) studied the relationship between the German stock market and economic development from 1986 to 2007, using Johansen co-integration analysis, which revealed a one- way causal relationship where stock market development Granger-causes German economic growth. Sheilla Nyasha and N.M. Odhiambo (2015) explored the causal relationship between financial development and economic growth in both developed and developing countries, finding that the nature of the causality varies significantly across nations. Overall, the varying levels of economic development in different countries influence the degree of stock market maturity; in developed economies, the stock market effectively reflects macroeconomic performance, whereas in less developed regions, the relatively recent establishment of stock markets 165 limits their economic impact. Tan Ruyong (1999) investigated the relationship between financial development and economic growth in China, using data from 1993 to 1998 to analyze the regression of stock market capitalization on GDP and transaction volumes, concluding that there is a significant positive correlation between the development of financial intermediaries and economic growth, while the relationship between stock market growth and economic growth is negative but not significant. Shi Zhiheng and Wang Yating (2004) addressed the divergence between the Chinese stock market and economic growth, attributing this to the costs associated with market transformation and regulatory adjustments, suggesting a return to normal growth levels post-adjustment. Wang Zhijun (2012) empirically examined the relationship between the Shanghai Composite Index and China's macroeconomy, finding low contribution rates from various factors affecting the index, including a reverse impact of CPI on the index. Cao Shuyuan (2015) explored the relationship between stock index fluctuations and GDP through a VAR time-series approach, concluding that there is a long-term equilibrium relationship, with the stock market serving as a barometer for economic development. Zhang Xin (2017) identified a long-term equilibrium relationship between the stock market and macroeconomic development in China, noting that while economic factors positively influence the stock market, external variables may play a more significant role. Jin Fang (2017) examined whether the stock market serves as a barometer for the macroeconomy in China and the U.S., finding that while the U.S. stock market effectively reflects economic conditions, the effect remains less pronounced in China due to its underdeveloped market. Thus, domestic scholars present divergent views: some affirm a long-term equilibrium relationship where economic growth promotes stock market prosperity, while others argue that the correlation is weak and the stock market does not serve as an economic barometer. 3. The Relationship Between the Securities Market and Macroeconomic Variables 3.1. Selection of Variables 3.1.1. Selection of Securities Market Indicators China's securities trading venues include the Shenzhen Stock Exchange and the Shanghai Stock Exchange, each with its representative indices. For instance, the Shanghai Stock Exchange features the SSE 50 index, while a composite index like the CSI 300 encompasses multiple exchanges. In this study, the CSI 300 is selected as the key variable representing the Chinese securities market, as it includes high-quality companies from both major exchanges, providing a comprehensive cross-market index that reflects the overall market situation in China. The CSI 300 consists of a combination of companies from the Shanghai and Shenzhen exchanges, selected based on liquidity, scale, and their ability to represent overall market fluctuations. 3.1.2. Selection of Macroeconomic Indicators Many indicators reflect economic development in China. After considering the relevance and availability of variables in relation to the securities market, GDP (Gross Domestic Product) is chosen as the primary representative. Additionally, to enhance data richness, M2 (broad money supply) and the interbank lending rate are included as supplementary indicators. GDP represents the total economic output of China, indicating its productive capacity. Cao Shuyuan (2015) also utilized GDP in his research during his master's studies. The money supply supports trading volumes in the securities market; adequate monetary supply provides the necessary funds for market growth, as noted by Wang Zhijun (2012) in his study of the relationship between the SSE index and macroeconomics. The interbank lending rate serves as a representative interest rate in the Chinese financial market, reflecting short-term supply and demand dynamics, and was similarly used by Wang Zhijun (2012) in his analysis of the economic relationship with the stock market. Definitions of Selected Indicators: Gross Domestic Product (GDP): The final result of production activities in a region (China) over a specific period (one year), encompassing final goods and services. Broad Money Supply (M2): This encompasses all forms of money that indicate purchasing power, including circulating cash, demand deposits, time deposits, individual savings, and other deposits. Interbank Lending Rate: The short-term borrowing rate between financial institutions, representing the cost of funds in the lending market (Table 1). Table 1. Selected Indicators and Their Corresponding Symbols Indicators Hu-Shen 300 Index Gross Domestic Product (Broad Money Supply Interbank Lending Rate Symbols HS300 GDP M2 R 3.2. The Impact of Macroeconomic Variables on Securities Market Development Gross Domestic Product (GDP) is a key indicator of China's macroeconomic performance, representing the overall level of economic development. During economic upturns, consumer demand for products increases, prompting producers to enhance supply, expand their workforce, and stimulate both production and employment. This strong supply-demand dynamic leads to improved living standards, positive corporate performance, and heightened investor sentiment, attracting more capital into the securities market. This influx results in rising stock prices, encouraging small and medium-sized enterprises to seek financing through the market. Conversely, during economic downturns, weak market demand can lead to oversupply, causing operational difficulties for listed companies. When enterprises lack investment value, investors are likely to exit the market. The broad money supply (M2) is an important indicator of money supply in China, encompassing all forms of money that can be converted into actual purchasing power. When the money supply increases, investor expectations regarding future returns may decline, leading both individual and institutional investors to allocate funds to the securities market in search of higher value investments, which drives stock prices up. Additionally, an increase in the overall money supply indicates more liquid funds in the market, and the time value of money incentivizes investors to seek higher returns in the securities market, further elevating stock prices. Conversely, a decrease in M2 results in reduced liquidity, leading to higher returns on risk-free investments and prompting investors to opt for safer, more stable assets rather than equities. This decline in investment volume can consequently result in falling stock prices. 166 The interbank lending rate represents the important settlement price for short-term borrowing among financial institutions, reflecting the cost of borrowed funds. This basic borrowing cost is ultimately passed on to clients who engage in securities transactions. When the cost of borrowing rises, the efficiency of fund utilization significantly decreases, leading to reduced liquidity in the securities market and impacting stock market returns. Conversely, a decrease in the interbank lending rate lowers borrowing costs among institutions, enabling clients to access funds at reduced rates, thus encouraging more capital to flow into the securities market in pursuit of higher returns and enhancing overall market liquidity. 3.3. The Impact of the Securities Market on Macroeconomic Variables The securities market influences China's macroeconomic landscape primarily through financing. Its primary function is to raise capital by facilitating indirect financing through the issuance of stocks during public offerings. Compared to direct financing, the securities market offers lower transaction costs and can mobilize large amounts of capital in a short time. According to Tobin's Q theory, when the securities market is thriving, stock prices exceed their fundamental values, motivating both individual and corporate investors to establish new enterprises for greater returns rather than pursuing acquisitions of existing firms at a premium. As the number of firms in the market increases and competition intensifies, capital allocation becomes more efficient, flowing into high-quality enterprises and stimulating rapid economic growth. The securities market also impacts the macroeconomy through the wealth effect. According to Modigliani's life- cycle hypothesis, current expenditure levels are not solely determined by present income but are influenced by anticipated future income. When the securities market performs well, the wealth effect becomes apparent as investors experience significant gains in their asset values. This positive sentiment can enhance future income expectations, resulting in increased personal consumption levels and quality, which stimulates overall societal demand and bolsters the consumption component of macroeconomic activity. Furthermore, the securities market improves capital allocation efficiency. The Chinese securities market is characterized by good liquidity, enabling investors to adjust their capital portfolios flexibly, thereby mitigating risk while maximizing returns and fostering economic growth. Additionally, the market plays a crucial role in resource allocation. Professional investment teams, such as fund managers, identify undervalued assets for investment, ensuring that funds flow into enterprises that genuinely need them. This alleviates financing challenges and optimizes the utilization of capital, thereby enhancing economic efficiency. Lastly, the securities market influences China's macroeconomy by standardizing corporate governance practices. The stringent regulations imposed on listed companies enhance their reputation and governance structures. Improved corporate credibility is reflected in stock price levels, thereby increasing the value of the assets available for lending, expanding the scale of loanable funds, and enhancing overall societal credit levels. This funding can then be directed toward social investment and enterprise development, contributing to an increase in total social output. 4. Empirical Analysis Results 4.1. Variable Selection In this study, the explained variable, the CSI 300 Index, is denoted as HS300. The explanatory variables include Gross Domestic Product (GDP), represented as GDP; the broad money supply (M2), denoted as M2; and the interbank lending rate, represented as R. Additionally, the data is organized chronologically on a quarterly basis, with time recorded as TIME. 167 4.2. Data and Basic Processing 4.2.1. Data and Sources (Table 2) Table 2. Study data presentation. Time Quarterly GDP (trillion) M2 (trillion) CSI 300 Index (points) Interbank Lending Rate (%) Q1 2011 10.447 75.813088 3223.29 1.93 Q2 2011 11.8894 78.082085 3044.09 4.56 Q3 2011 12.6562 78.74062 2581.35 3.74 Q4 2011 13.8012 85.15909 2345.74 3.33 Q1 2012 11.7358 89.55655 2454.9 2.58 Q2 2012 13.132 92.49912 2461.61 2.72 Q3 2012 13.809 94.368875 2293.11 2.93 Q4 2012 15.1812 97.415946 2522.95 2.61 Q1 2013 12.945 103.585837 2495.08 2.47 Q2 2013 14.3518 105.440369 2200.64 6.58 Q3 2013 15.2223 107.737916 2409.04 3.47 Q4 2013 16.7772 110.652498 2330.03 4.16 Q1 2014 14.076 116.068738 2146.3 2.49 Q2 2014 15.649 120.95872 2165.12 2.85 Q3 2014 16.5484 120.205141 2450.99 2.97 Q4 2014 18.0829 122.837481 3533.7 3.49 Q1 2015 15.1138 127.533278 4051.2 3.69 Q2 2015 16.855 133.337536 4473 1.44 Q3 2015 17.6597 135.982406 3202.95 2.05 Q4 2015 19.2573 139.227811 3731 1.97 Q1 2016 16.241 144.619803 3218.09 2.09 Q2 2016 18.1408 149.049183 3153.92 2.14 Q3 2016 19.1011 151.63605 3253.28 2.25 Q4 2016 21.1566 155.006667 3310.08 2.33 Q1 2017 18.1868 159.960957 3456.05 2.62 Q2 2017 20.195 163.128253 3666.8 2.94 Q3 2017 21.2789 165.566207 3836.5 2.92 Q4 2017 23.5429 167.676854 4030.85 2.91 Q1 2018 20.2036 173.985948 3898.5 2.74 Q2 2018 22.3962 177.017837 3510.98 2.73 Q3 2018 23.4474 180.166558 3438.86 2.59 Q4 2018 25.8809 182.674422 3010.65 2.57 Q1 2019 21.7168 188.941214 3872.34 2.42 Q2 2019 24.1503 192.136019 3825.59 1.7 Q3 2019 25.1046 195.225049 3814.53 2.55 Q4 2019 27.6498 198.648882 4096.58 2.09 Q1 2020 20.5727 208.092341 3686.16 1.4 Q2 2020 24.8985 213.494866 4163.96 1.85 Q3 2020 26.4976 216.40848 4587.4 1.8 Q4 2020 29.6298 218.679589 5211.29 1.3 4.2.2. Data Preprocessing Based on the data compiled in Table 2, it can be observed that there are significant disparities among the datasets. The units for Gross Domestic Product (GDP) and broad money supply are in trillion yuan, while the units for the CSI 300 index are in points, and the interbank lending rate is expressed in basis points. Since both the GDP and broad money supply data are nominal, to enhance the empirical authenticity, this study incorporates the quarterly real GDP figures from the National Bureau of Statistics as the actual GDP data, as well as the real money supply as actual M2. To minimize absolute differences in the data and reduce empirical errors arising from differing units, while balancing the sensitivity of different datasets, this paper applies a logarithmic transformation to the CSI 300, quarterly real GDP, and quarterly real M2 data. The interbank lending rate in China, which is already expressed as a percentage, will not undergo logarithmic transformation. The selected logarithmic data will contribute to a more stable model structure, thereby improving collinearity and stability. Table 3 presents the results of the logarithmic transformation applied to the selected data. 168 Table 3. Data Processing and Logarithmic Transformation Time Quarterly GDP (trillion) M2 (trillion) CSI 300 Index (points) Interbank Lending Rate (%) Q1 2011 8.08 2.35 4.33 0.66 Q2 2011 8.02 2.48 4.36 1.52 Q3 2011 7.86 2.54 4.37 1.32 Q4 2011 7.76 2.62 4.44 1.20 Q1 2012 7.81 2.46 4.49 0.95 Q2 2012 7.81 2.58 4.53 1.00 Q3 2012 7.74 2.63 4.55 1.08 Q4 2012 7.83 2.72 4.58 0.96 Q1 2013 7.82 2.56 4.64 0.90 Q2 2013 7.70 2.66 4.66 1.88 Q3 2013 7.79 2.72 4.68 1.24 Q4 2013 7.75 2.82 4.71 1.43 Q1 2014 7.67 2.64 4.75 0.91 Q2 2014 7.68 2.75 4.80 1.05 Q3 2014 7.80 2.81 4.79 1.09 Q4 2014 8.17 2.89 4.81 1.25 Q1 2015 8.31 2.72 4.85 1.31 Q2 2015 8.41 2.82 4.89 0.36 Q3 2015 8.07 2.87 4.91 0.72 Q4 2015 8.22 2.96 4.92 0.68 Q1 2016 8.08 2.79 4.97 0.74 Q2 2016 8.06 2.90 5.00 0.76 Q3 2016 8.09 2.95 5.02 0.81 Q4 2016 8.10 3.05 5.04 0.85 Q1 2017 8.15 2.90 5.07 0.96 Q2 2017 8.21 3.01 5.09 1.08 Q3 2017 8.25 3.06 5.11 1.07 Q4 2017 8.30 3.16 5.12 1.07 Q1 2018 8.27 3.01 5.16 1.01 Q2 2018 8.16 3.11 5.18 1.00 Q3 2018 8.14 3.15 5.19 0.95 Q4 2018 8.01 3.25 5.21 0.94 Q1 2019 8.26 3.08 5.24 0.88 Q2 2019 8.25 3.18 5.26 0.53 Q3 2019 8.25 3.22 5.27 0.94 Q4 2019 8.32 3.32 5.29 0.74 Q1 2020 8.21 3.02 5.34 0.34 Q2 2020 8.33 3.21 5.36 0.62 Q3 2020 8.43 3.28 5.38 0.59 Q4 2020 8.56 3.39 5.39 0.26 4.2.3. Basic Data Analysis Based on the results obtained from the basic processing of the data, the trend chart for Gross Domestic Product (GDP), broad money supply (M2), CSI 300 (HS300), and interbank lending rate (R) from quarterly data (2011 to 2020) is presented below: Figure 1. Trend Chart of LNGDP, LNM2, LNHS300, and LNR by QUARTERLY From the above chart, it can be observed that within the 169 selected time frame (2011-2020), the data for GDP, M2, and HS300 generally exhibit an upward trend year by year, while the selected data for R shows a declining trend. From an economic perspective, China's interbank lending rate represents the borrowing rate for short-term funds in the financial market, which indirectly influences the choice of China's deposit and loan interest rates. A long-term downward fluctuation in the interbank lending rate indicates a reduction in the cost of capital market financing, which is conducive to the upward growth of China's money supply, GDP, and the stock market. Table 4. Descriptive Statistics. LnHS300 LnGDP LnM2 LnR Mean 8.0681 2.7979 3.9782 0.9409 Median 8.0961 2.8079 4.0828 0.9497 Maximum 8.5586 3.2722 5.1253 1.8840 Minimum 7.6715 2.2915 2.7738 0.2624 Std.Dev. 0.2377 0.2681 0.5562 0.3206 Observations 40 40 40 40 Table 5. Correlation analysis table between explanatory and interpreted variables Correlation LnHS300 LnGDP LnM2 LnR LnHS300 1 LnGDP 0.7253 1 LnM2 0.7509 0.7946 1 LnR -0.5737 -0.4509 -0.8791 1 Through covariance analysis, the correlation coefficients of LnHS300 with LnGDP, LnM2, and LnR are found to be 0.7253, 0.7509, and -0.5737, respectively. This indicates a positive correlation between the CSI 300 and both GDP and broad money supply, while a negative correlation exists with the interbank lending rate. 4.2.4. Model Construction The logarithmic transformation of the data facilitates the reduction of variable scales, helping to alleviate the impact of heteroscedasticity. Thus, the following logarithmic model is constructed,  ++++= t3t2t1t n2nn300n RLMLGDPLCHSL Where 1  , 2  and 3 the parameters to be estimated,  represents the residual term. 4.3. Empirical Analysis 4.3.1. Unit Root Test The empirical analysis method selected for this study combines time series analysis and the VAR model. Time series analysis has several assumptions, such as the stationarity of the series. However, many series in reality are non-stationary, leading to adverse empirical outcomes, such as spurious regression. Therefore, it is essential to test the stationarity of each series before proceeding with time series analysis. This study employs the ADF test to assess the stationarity of the time series. In the test formulation, C represents the constant term, T represents the trend term, and K represents the lag period. The choice of intercept in the test equation is based on the minimum values of AIC, SC, and HQ. If the P- value determined by the minimum value is still greater than 0.05, the series is considered non-stationary. If the dependent variable LnHS300 is non-stationary, then the other explanatory variables are also deemed ineffective. Consequently, first-order differencing will be used for empirical analysis, with D1 indicating first-order differencing and D2 indicating second-order differencing. Table 6. ADF Test Variable Test Form ADF Test Value Critical Value (5%) Critical Value (10%) P-value Test Result LnHS300 (C, T, 9) -3.0305 -3.5297 -3.1964 0.1373 Non-stationary LnGDP (C, T, 9) -1.8212 -3.5443 -3.2047 0.6728 Non-stationary LnM2 (C, T, 9) -5.2542 -3.5297 -3.1964 0.0006 Stationary (Rejected) LnR (C, T, 9) -5.3041 -3.5298 -3.1964 0.0005 Stationary (Rejected) D (LnHS300, 1) (0, 0, 9) -6.1178 -1.9499 -1.6115 0.0000 Stationary D (LnGDP, 1) (C, 0, 9) -2.3625 -2.9484 -2.6129 0.1593 Non-stationary D (LnM2, 1) (C, T, 9) -9.8206 -3.5331 -3.1983 0.0000 Stationary D (LnR, 1) (C, T, 9) -10.0362 -3.5330 -3.1982 0.0000 Stationary D (LnHS300, 2) (0, 0, 9) -11.5548 -1.9501 -1.6113 0.0000 Stationary (5%) D (LnGDP, 2) (0, 0, 9) 29.8903 -1.9507 -1.6111 0.0000 Stationary (5%) D (LnM2, 2) (0, 0, 9) -4.8157 -1.9517 -1.6106 0.0000 Stationary (5%) D (LnR, 2) (0, 0, 9) -4.8527 -1.9517 -1.6105 0.0000 Stationary (5%) From the results of the ADF test in the above table, it is evident that only the dependent variable for broad money supply and the interbank lending rate show stationarity in the original series, while the original series for HS300 is non- stationary. In first-order differencing, HS300, broad money supply, and the interbank lending rate show stationarity at the 5% significance level, while GDP remains non-stationary. In second-order differencing, all variables exhibit stationarity at the 5% significance level. Next, a cointegration test will be conducted on the second-order differenced series. 4.3.2. Cointegration Test Cointegration refers to establishing a model for non- stationary time series to test the long-term equilibrium relationship between variables. This study will employ the E- G two-step method to test the residual series. In the first step, a simple linear regression is performed on all variables, yielding the results shown in figure 2. All regression results indicate t-values greater than 0, signifying significance, thus no variables need to be excluded. The following is the simple linear regression result for second- order integration: 170 ttttt e20005.02n22029.0n23090.06.3958300n2 ++++= LnRDMLDGDPLDHSLD Dependent Variable: LNHS300 Method: Least Squares Date: 04/21/21 Time: 20:41 Sample: 2011Q1 2020Q4 Included observations: 40 Variable Coefficient Std. Error t-Statistic Prob. LNGDP 0.309008 0.294204 1.050319 0.3006 LNM2 0.202928 0.265655 0.763876 0.4499 LNR 0.000504 0.313524 0.001606 0.9987 C 6.395785 0.661272 9.671937 0.0000 R-squared 0.608778 Mean dependent var 8.068140 Adjusted R-squared 0.576176 S.D. dependent var 0.237785 S.E. of regression 0.154802 Akaike info criterion -0.798701 Sum squared resid 0.862691 Schwarz criterion -0.629813 Log likelihood 19.97402 Hannan-Quinn criter. -0.737637 F-statistic 18.67313 Durbin-Watson stat 0.722345 Prob(F-statistic) 0.000000 Figure 2. OLS regression model. In the second step, a unit root test is conducted on the residual series, choosing the unit root test "without drift and trend," with the following results (Figure 3). Null Hypothesis: D(ECM,2) has a unit root Exogenous: None Lag Length: 0 (Automatic - based on SIC, maxlag=9) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -9.662404 0.0000 Test critical values: 1% level -2.628961 5% level -1.950117 10% level -1.611339 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation Dependent Variable: D(ECM,3) Method: Least Squares Date: 04/21/21 Time: 20:48 Sample (adjusted): 2011Q4 2020Q4 Included observations: 37 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(ECM(-1),2) -1.405341 0.145444 -9.662404 0.0000 R-squared 0.721557 Mean dependent var 0.006924 Adjusted R-squared 0.721557 S.D. dependent var 0.298063 S.E. of regression 0.157281 Akaike info criterion -0.834912 Sum squared resid 0.890542 Schwarz criterion -0.791374 Log likelihood 16.44588 Hannan-Quinn criter. -0.819563 Durbin-Watson stat 2.226354 Figure 3. Unit Root Test of Residuals The results show that P-value = 0 < 0.05, leading to the rejection of the null hypothesis. This indicates that the residual series does not contain a unit root and is stationary. Therefore, there exists a cointegration relationship between HS300 and GDP, M2, and R, with the cointegration equation as follows: ttttt e20005.02n22029.0n23090.06.3958300n2 ++++= LnRDMLDGDPLDHSLD 4.3.3. Granger Causality Test Through the cointegration test, it can be concluded that there is a causal relationship between the explanatory variables and the dependent variable, meaning that the lag of one variable impacts the current value of another. Thus, a Granger causality test will be conducted to examine the relationship between HS300 and broad money supply, GDP, and the interbank lending rate. The results will be based on the P-values, with smaller P-values indicating more significant Granger causality. Figure 4 displays the bidirectional Granger causality test results between HS300 and GDP, broad money supply, and the interbank lending rate. In the first step, the optimal lag period for the Granger causality test is determined by establishing a VAR model, yielding the lag information values shown in Figure 4. First, the AIC and SC values for the lag order are compared, observing values of 4 and 8, respectively. Finally, by considering HQ and LR values, the optimal lag order (indicated by an asterisk) is confirmed to be 4 periods. VAR Lag Order Selection Criteria Endogenous variables: DLNHS300 DLNGDP Exogenous variables: C Date: 04/21/21 Time: 22:41 Sample: 2011Q1 2020Q4 Included observations: 31 Lag LogL LR FPE AIC SC HQ 0 42.21356 NA 0.000256 -2.594423 -2.501908 -2.564265 1 47.33836 9.257707 0.000238 -2.666991 -2.389445 -2.576518 2 49.09067 2.939360 0.000277 -2.521979 -2.059402 -2.371190 3 71.38269 34.51668 8.59e-05 -3.702109 -3.054502 -3.491006 4 83.30770 16.92581* 5.25e-05 -4.213400 -3.380762* -3.941981 5 87.38484 5.260828 5.38e-05 -4.218377 -3.200708 -3.886642 6 94.70475 8.500539 4.55e-05 -4.432564 -3.229865 -4.040515 7 100.0435 5.511002 4.46e-05* -4.518937* -3.131208 -4.066572* 8 103.1006 2.761204 5.21e-05 -4.458102 -2.885342 -3.945421 * indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion Figure 4. Optimal Lag Period Determination In the second step, since the regression model did not determine a balance relationship between LnGDP, LnM2, and LnHS300, the causality test is conducted here. Setting the lag period to 4 in Eviews, if the accompanying probability is greater than 0.10, the null hypothesis is accepted, indicating no causal relationship. Conversely, the null hypothesis is rejected. 171 Table 7. Granger Causality Test Original Hypothesis Lag Order F- Statistic Probability DLnGDP does not Granger- cause DLnHS300 4 0.6965 0.6013 DLnHS300 does not Granger- cause DLnGDP 4 1.4146 0.2507 DLnM2 does not Granger- cause DLnHS300 4 0.6118 0.6578 DLnHS300 does not Granger- cause DLnM2 4 2.2909 0.0866 DLnR does not Granger- cause DLnHS300 4 0.6795 0.6124 DLnHS300 does not Granger- cause DLnR 4 2.1806 0.0992 It can be observed that DLnHS300 is a Granger cause of DLnM2, and DlnHS300 is a Granger cause of DLnR. However, DLnGDP and DLnHS300 do not exhibit Granger causality towards each other. Therefore, the GDP variable is deemed insignificant, and the variable for GDP will be excluded from further empirical research. 4.4. Establishing the VAR Model The VAR model, also known as the Vector Autoregression model, involves constructing multiple regression equations and linking them to form a system of equations. The dependent variables in each equation are the endogenous variables, while the explanatory variables are the endogenous lagged terms of these dependent variables. In the first step, the lag order for the VAR model is selected based on AIC and SC information criteria, with smaller values indicating a better fit for the optimal lag order. From the previous Granger causality test, the optimal lag order was determined to be 4, so this study selects 4 as the lag order. In the second step, the CSI 300, interbank lending rate, and broad money supply are all treated as endogenous variables, and the VAR model is established using Eviews software with a lag order of 4. The stability analysis results are shown in figure 5, where all points for lag 4 fall within the unit circle, indicating a stable model. -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 Inverse Roots of AR Characteristic Polynomial Figure 5. VAR stability analysis Once the lag order is determined, the constant term is treated as an exogenous variable, yielding the VAR estimation results as shown in Figure 6. Figure 6. VAR Parameter Estimation Results In the third step, impulse response analysis and variance decomposition are conducted on the VAR model. Impulse response measures the degree of response of the dependent variable to an impulse in the error term of the time series. The horizontal axis of the impulse response chart represents the lag periods (1-10), indicating the number of lagged quarters, while the vertical axis represents the rate of change of the dependent variable, with the upper side indicating positive correlation and the lower side negative correlation. The solid line represents the impulse response function, illustrating the response of the dependent variable to the impulse from the explanatory variable, while the dashed lines indicate the bands of deviation of two standard deviations (Figure 7). 172 -.05 .00 .05 .10 1 2 3 4 5 6 7 8 9 10 Response of LNHS300 to LNHS300 -.05 .00 .05 .10 1 2 3 4 5 6 7 8 9 10 Response of LNHS300 to LNM2 -.05 .00 .05 .10 1 2 3 4 5 6 7 8 9 10 Response of LNHS300 to LNR .0 .1 .2 .3 1 2 3 4 5 6 7 8 9 10 Response of LNM2 to LNHS300 .0 .1 .2 .3 1 2 3 4 5 6 7 8 9 10 Response of LNM2 to LNM2 .0 .1 .2 .3 1 2 3 4 5 6 7 8 9 10 Response of LNM2 to LNR -.3 -.2 -.1 .0 1 2 3 4 5 6 7 8 9 10 Response of LNR to LNHS300 -.3 -.2 -.1 .0 1 2 3 4 5 6 7 8 9 10 Response of LNR to LNM2 -.3 -.2 -.1 .0 1 2 3 4 5 6 7 8 9 10 Response of LNR to LNR Response to Cholesky One S.D. (d.f. adjusted) Innovations ?2 S.E. Figure 7. Impulse Response Results The first row shows the dynamic effects of the CSI 300 (LnHS300) receiving a one standard deviation impulse on the VAR model over 10 periods. The impact of LnHS300 is positive, gradually stabilizing and diminishing over the first 5 quarters, and approaching negligible influence after the fifth quarter. The impulse from the broad money supply (LnM2) has a significant effect only in the first two quarters, gradually weakening from the fifth quarter onward. The impact of the interbank lending rate (LnR) peaks slightly in the second quarter, then declines from the third quarter until it is fully absorbed. Overall, the CSI 300 does not influence China's interbank lending rate; its increase leads to a rise in broad money supply that lasts for 7 quarters before returning to its original state. The second row illustrates the dynamic effects of a one standard deviation impulse from broad money supply (LnM2) on the VAR model over 10 periods. The impact on the CSI 300 (LnHS300) reaches its peak in the first three quarters, with subsequent shocks gradually being absorbed; the influence of LnM2 is significant in the first three quarters, tapering off by the fourth quarter; the interbank lending rate (LnR) shows minimal impact throughout. Thus, the increase in broad money supply results in a rise in the CSI 300 for 6 quarters, while only a slight increase in the interbank lending rate occurs in the first two quarters. The third row depicts the dynamic effects of a one standard deviation impulse from the interbank lending rate (LnR) on the VAR model over 10 periods. The CSI 300 (LnHS300) experiences the greatest impact in the first three quarters, predominantly negative, gradually dissipating to negligible effect by the fourth quarter. The influence of LnM2 remains negative for the first four quarters before being absorbed to a lower level; the impact on LnR is nearly negligible throughout the period. Therefore, a decline in the interbank lending rate corresponds to a rise in the CSI 300 for 3 quarters and an increase in broad money supply for 4 quarters, both returning to negligible impact thereafter. Variance decomposition refers to the attribution of variance in the dependent variable. Table 8. Variance Decomposition Results for LnHS300 Period Standard Deviation CSI 300 Broad Money Supply Interbank Offered Rate 1 0.1078 100.00 0.0000 0.0000 2 0.1339 95.7840 3.2926 0.9233 3 0.1401 95.3188 3.7025 0.9787 4 0.1437 92.2293 6.8153 0.9554 5 0.1457 90.3226 8.5734 1.1041 6 0.1467 89.6898 9.0006 1.3096 7 0.1472 89.4257 9.0565 1.5178 8 0.1475 89.1993 9.0658 1.7350 9 0.1478 88.9540 9.0751 1.9710 10 0.1481 88.6989 9.0759 2.2216 According to the above table, aside from considering the contribution of the CSI 300 to itself, the broad money supply has the highest contribution to the CSI 300, peaking at 9.0759% in the 10th period, indicating significant potential for increased contribution in the later stages. Conversely, the 173 impact of the interbank lending rate on the CSI 300 shows a slow upward trend, reaching a peak of 2.2216% in the 10th period. Overall, as time progresses, the contributions of explanatory variables to the dependent variable will become increasingly significant. 5. Conclusion and Recommendations (1) Research Conclusions This study examined the relationship between the representative indices of the securities market and economic development in China from January 2011 to December 2020 using Eviews econometric models. The empirical results indicate a long-term equilibrium relationship between the Chinese securities market and economic development, leading to the following conclusions: The CSI 300 and broad money supply exhibit a unidirectional causal relationship, where the CSI 300 is the Granger cause of the broad money supply. The impulse response indicates a positive correlation from the broad money supply to the CSI 300, maintaining this positive effect after seven periods. Conversely, the CSI 300 also shows a long-term positive response to changes in the broad money supply. Variance decomposition reveals that fluctuations in the broad money supply significantly contribute to changes in the CSI 300, with increasing contributions over time. Correlation analysis confirms a positive relationship: when broad money supply grows, it contributes to an upward trend in the securities market. There is also a unidirectional causal relationship between the CSI 300 and the interbank lending rate, with the CSI 300 being the Granger cause of the interbank lending rate. The impulse response shows a negative impact from the interbank lending rate on the CSI 300 in the first four periods, followed by a slight positive response after the fifth period, warranting further exploration. In contrast, the CSI 300's response to the interbank lending rate remains predominantly negative over the long term. Variance decomposition shows that changes in the interbank lending rate also have a certain contribution to the CSI 300, increasing yearly. Correlation analysis indicates a negative relationship: as the cost of interbank lending rises, financing costs in the securities market increase, leading to a decline in market performance. The empirical analysis shows no causal relationship between GDP and the CSI 300, leading to the exclusion of GDP from the study. Despite China's economy maintaining positive growth over the past decade, the stock market has experienced significant fluctuations, reflecting a large disparity between the two. Thus, GDP was excluded from the analysis. In conclusion, while there exists a Granger causality between economic development and securities market development, the contribution attributed to economic development through variance decomposition is relatively low, with the broad money supply having the highest contribution, which did not exceed 10% by the 10th period. The CSI 300's own contribution remains significant, highlighting issues within the Chinese securities market. Research Recommendations Based on the empirical findings and the current state of China's economic development and securities market, this paper proposes the following strategies for reforming the securities market and promoting macroeconomic development in China: A. Accelerate the Implementation of the Registration System for Stock Issuance: Listed companies serve as a crucial bridge between China's securities market and economic development. However, some companies pursue listing primarily for profit rather than effective management, leading to issues such as financial fraud, inadequate profitability, and significant stock sales by management, which harm small investors. These behaviors are considered black swan events in the securities market. To reduce their occurrence, a stricter approval system for company listings is necessary to prevent fraud. Additionally, implementing a registration system for stock issuance will provide the securities market with more vibrant, high-quality companies, enhancing the market and economic development. B. Enhance Legal Regulations in Financial and Securities Markets and Ensure Strict Enforcement: The late development of China's securities market has resulted in an immature legal and regulatory framework, compounded by various factors causing market volatility and instability. Issues such as insider trading, price manipulation, financial fraud, and the spread of false information threaten the healthy development of the stock market. Therefore, it is essential to focus on improving legal regulations, imposing strict penalties for actions that disrupt financial order, and guiding investors toward value investing to promote healthy market growth. C. Reform State-Owned Enterprises (SOEs) to Integrate Them into a Market Economy: The current state of Chinese SOEs often relies on government support in times of profit or loss, creating an unequal competitive environment compared to small and private enterprises. It is vital to foster a competitive environment by encouraging financial institutions to lend more to small and innovative, environmentally-friendly enterprises. This approach will promote entrepreneurship and innovation, adding new momentum to China’s market economy. D. Strengthen Education for Retail Investors and Encourage the Establishment of Financial Investment Institutions**: Investors are a vital component of China’s securities market, yet most lack the expertise necessary for professional investing, often leading to subjective decision- making and difficulty in identifying investment risks. This situation makes them vulnerable to exploitation by institutional investors. 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