As a recognized complex dynamic system, the stock market has many influencing factors, such as nonstationarity, nonlinearity, high noise, and long memory. It is difficult to explain it simply through mathematical models. Therefore, the analysis and prediction of the stock market have been a very challenging job since long time. Therefore, this paper adopts an encoder-decoder model of attention mechanism, adding attention mechanism from two aspects of feature and time. Both encoder and decoder use LSTM neural network. This method solves two problems in time series prediction; the first problem is that multiple input features have different degrees of influence on the target sequence, the feature attention mechanism is used to deal with this problem, and the weights of different input features can be obtained. A more robust feature association relationship is obtained; the second problem is that the data before and after the sequence have a strong time correlation. The time attention mechanism is used to deal with this problem, and the weights at different time points can be obtained to obtain more robustness and good timing dependencies. The simulation and experimental results show that the introduction of the attention mechanism can obtain lower forecast errors, which proves the effectiveness of the model in dealing with stock forecasting problems.
Small- and medium-sized enterprises (SMEs) are important foundations to implement mass entrepreneurship and innovation and play an irreplaceable role in increasing employment, promoting economic growth, as well as scientific and technological innovations, and providing particularly social harmony and stability and imminently are strategic entities to the national economy and social development in underdeveloped regions. However, the low-efficiency financing of SMEs has gradually become a major factor that restricts the high-quality development of SMEs in the current conditions. In this paper, interest expenditure, gearing ratio, and the net debt ratio as input indicators and current asset turnover ratio, cost margin, and main business income as output indicators are used to conduct the DEA-BCC model. By utilizing the GEM-listed private enterprises between 2017 and 2020 in China, the nationwide financing efficiency of SMEs is firstly measured, and then the financing efficiencies of SMEs in economically developed regions and lagging regions are calculated separately. The comparison reveals that the financing efficiency of SMEs in economically underdeveloped regions is not only lower than the national average figure but also much lower than the financing efficiency level in economically developed regions, which is the result of the combined effect of internal and external factors that enterprises face. Further, this paper finds that unexpected public events, core technical personnel, and enterprise size have an impact on the financing efficiency of SMEs when running group testing. This paper puts forward rationalized suggestions to the institutions to improve the financing efficiency of SMEs in underdeveloped regions concerning the conducted research, which are called government, financial institutions, and enterprises.
In non-life intensive computation of insurance, loss amount model is mainly constructed with loss detention law or the tendency analytic method. But there are some problems in both methods, such as more restrictions on the empirical data, and they can not be used to construct the loss amount model with nonlinear data. This paper attempts to solve the problem with the Newton's interpolation method, and examines the forecast effect under the conditions of nonlinear data. The results show that the estimated value obtained by Newton's interpolation method is better than by the current methods.
Under the background of global economic integration, commercial banks are facing more and more complex business environment. As one of the major financial risks faced by commercial banks, liquidity risk determines and reflects the safety and profitability of bank operation. Based on joint-stock commercial banks as the research object, this paper, respectively, from the angle of the static and dynamic measurements and projections for liquidity risk and based on the current situation of four joint-stock commercial banks liquidity level study, tries to explore the change law of commercial banks liquidity risk and financial risk control of commercial bank and puts forward reasonable suggestions. In this paper, an AHP neural network model combining subjective and objective methods is proposed. This method can not only overcome the defects of the single evaluation method but also improve the data accessibility by using the qualitative data and quantitative data of AHP. In the aspect of financial risk control system, this paper tries to establish a more comprehensive and practical financial risk control model by combining the previous research of scholars, the business model process, and the experience of practical workers.
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