Based on the data of China Industrial Enterprise Database, this paper uses the propensity score matching double difference method (PSM-DID) to study the impact of mixed ownership reform of state-owned enterprises on enterprise performance. The study found that mixed ownership reform of state-owned enterprises can enhance the performance of enterprises. Further considering marketization, industry competition and regional characteristics, it is found that the effect of reform is heterogeneous. When the degree of marketization is high, the effect of reform on improving productivity is good, and when the degree of marketization is low, the effect of reform on reducing debt is good; the reform effect of industries with low degree of competition is better than that of industries with high degree of competition. The reform of state-owned enterprises in the eastern region has the best effect, and the reform in the central region has a better effect on reducing debt. The effect of mixed ownership reform in the western region is not significant.
How to raise productivity level has become the core issue of ensuring China’s sustained Economic Growth in the Future. The mixed-ownership has both the financing advantage of the SOEs and the competitive ability of the Private firms, which can improve the governance of the firms. This paper builds a model based on the financial frictions literature, and studies the process of the mixed-ownership reform. The main results include: 1. On average, the mixed-ownership reform enhances the performance of the firms; 2. The relationship between the share of state ownership—full privatization, state-ownership, or mixed-ownership—and the performance depends on both the productivity and the restriction of financing; 3. When production efficiency is low, privatization works best ; when production efficiency is medium, partial privatization works best ; when production efficiency is high, nationalization works best. 4. Our model explain the puzzle of state-owned equity ratio and performance.
This paper studies the sustainability of the financing model in China’s urbanization and the demand of local governments for PPP projects. Based on the integrated panel data of PPP, local investment and financing platforms, urban investment bonds, and local economic statistics, the fixed effect model and dynamic panel regression model are used to study whether local financing platforms promote economic growth. The results show that in general, the development model of financing platform is not conducive to sustainable economic development. Before the 2008 economic crisis, local governments were pushing up house prices through financing platforms which boosted economic growth, but after the 2008 economic crisis, this mechanism did not work. Therefore, the sample selection model is used to predict the demand of local PPP projects and verify the substitution relationship between local financing platforms and PPP. The study found that financing platforms hinder local government demand for PPP projects and the attraction to private investment. After adjusting the relevant variables to zero, the demand for PPP projects in a representative city is 3.46.
The problem of corporate debt is a hidden danger to China’s economic growth and financial stability. This paper studies the effect of regional marketization on the debt burden of the local enterprises based on a fixed effects model. The result demonstrates that the higher the market-oriented degree, the lower the debt burden of the enterprises in that region. Further analysis demonstrates that the improvement of marketization not only directly reduces the enterprise debt, but also reduces debt burden by increasing the enterprise productivity and promoting the enterprise property rights reform. Improving the level of regional marketization is conducive to the enterprise debt issue, and this provides some suggestions to the policy makers.
The stock price changes rapidly and is highly nonlinear in the financial market. One of the common concerns of many scholars and investors is how to accurately predict the stock price and the trend of rising and falling in a short time. Machine learning and deep learning techniques have found their place in financial institutions thanks to the ability of time series data prediction with high precision. However, the prediction accuracy of these models is still far from satisfactory. Most existing studies use original, single prediction algorithms that cannot overcome inherent limitations. This study proposes a hybrid model using principal component analysis (PCA) and backpropagation (BP) neural networks. The historical records of China Merchants Bank are used for data collection from 2015 to 2021. PCA preprocesses the original data to reduce the dimensionality and is then adopted by the BP neural network to predict the stock closing price of China Merchants Bank. We compare and analyze the PCA–BP model with three training algorithms, and the results indicate that the Bayesian regularization algorithm performs best. Besides, we perform the stock prediction using a traditional exponential smoothing approach. The experiment results show that the predicted stock closing price is close to the actual value, and the mean absolute percentage error can reach 0.0130, which is more significant than the traditional approach. Furthermore, A TOPSIS approach is utilized to evaluate the robustness of the proposed model. Finally, we demonstrate the usability of the designed hybrid model by predicting the stock price of another selected stock.
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