In this article, according to search for the definition of shadow banking, we can make sure the business kinds of “shadow banking”, discuss the influence of business in “shadow banking” on credit risk of commercial banks, and study the elements which may increase the credit risk of commercial banks by using the semi-annual panel data during 2011-2016 of 10 listed banks. Then we can come to some primary conclusions: The credit risk of commercial banks is related to the shadow banking business. All the survival scale increment of financial products increasing, the size of entrusted loans increasing in increment, and the increasing in the size of guarantee commitments will increase the credit risk of commercial banks. There is no obvious relationship between trust loan business and bank credit risk. Our study is of great significance for the government to supervise the off-balance-sheet business of commercial banks. At the same time, it also fills the vacancy of domestic commercial banking “shadow banking” business empirical research.
Under the stimulus of national policy vigorously, the higher education obtained the rapid development. Due to the dramatic increase of students, the slow growth of teaching hardware facilities and the shortage of teacher resources, how to make use of limited resources to meet the teaching demand in the optimal form becomes a problem that needed to be solved at present. Schedule arrangement is a process full of conflicts, because there are so many limitations for teaching resources allocation such as the class time of open courses, the classes, class locations and class teachers’ factors such as. In order to improve the efficiency of running school and complete the teaching mission better, it shall use modern information technology in time and space must as far as possible to distribute the teaching resources reasonably. With the aid of optimization theory firstly, this paper establishes a preliminary scheduling optimization model based on the hard constraint conditions to make the needed number for the classroom least as far as possible. Then, we add the soft constraint conditions to the preliminary model and obtain the final optimization model. Finally, this paper adopts the way of comprehensive evaluation, constructs the index system by calculating the classroom utilization, class strength of course object, the dissatisfaction rate of soft constraint conditions, and gets the score standard of curriculum arrangement scheme. For the optimization model of this article, we are using genetic algorithm to solve the results. This paper also gives the calculation steps of genetic algorithm based on course arrangement.
In European and American developed countries, quantitative trading is gradually replacing artificial transactions to occupy an important position in the market, and their daily turnover in the market is particularly evident. China securities market and derivatives market started late, and have a relatively obvious difference from abroad, especially in Western countries, in the level of quantitative transactions in mature capital markets. With the improvement of China’s market trading varieties, China’s quantization will develop very rapidly. In this paper, according to the characteristics of China’s CSI 300 Index Futures, we improve trend-tracking trading model based on the improved RSI. Firstly, we apply the wavelet transform for denoising of the price series, then improve RSI, and use the improved RSI and the denoised price series to establish an exit strategy and approach strategy. The strategy is excellent in practical application. In 1 minute K-line data back-test of CSI 300 index futures from 2010 to 2012, the return on invest has reached up to 102 million Yuan, and the ROI risk ratio is 2.61.
With the development and evolution of the futures market, the prosperity of a country's futures market is more and more important to the country's economic development. In China, the futures market development is not perfect, futures product structure still need to improve. Futures market, the existence of many speculators, futures laws and regulations imperfect, and the development of economic globalization, are the futures investment is facing many uncertain risks. In the choice of investment strategy, more and more investors to take prudent investment strategy, which, a large part of the investors tend to technical analysis. Through technical analysis, traders judge market trends and follow the cyclical changes in the trend to make stock and all financial derivatives trading decisions. In the technical analysis, the average system analysis is the most commonly used in practice is the highest accuracy of the analytical technology, this paper through the copper, rubber, sugar, cotton, zinc, several representative commodity futures to study, to verify the average bonding State of investment strategy choice. First, this paper determines the condition of the bonding state by calculating the distance of different mean points. Then, it is judged whether the reversal of the form is long or short by the comparison of the MA value of the different mean points. Finally, the factory strategy and the factory strategy are determined. And to take more and short after the operation of the proposed stop-loss strategy. The effect of these strategies is very significant, the sum of several commodity futures net profit of 113744.79 yuan, profit and loss ratio of 2.82.
This paper aims to establish a quantitative trading strategy of commodity futures based on market money flows. Firstly, we use Accumulation/Distribution index to respectively construct the CMF index which represents the ratio of total capital flows to total volume, and the CHO index which represents the exponential moving average of the cumulative capital flows. In view of the different flows of money between buyers and sellers, the establishment of the transaction net volume index VTL is used to describe respectively the flow of money between buyers and sellers. On this basis, the HMM model is introduced, and the above three kinds of indexes are combined to choose the time, at which we execute the stop-loss operation and risk control. Finally, all performance index values of the strategy are as follows: the rate of initial capital return is 193.77%, the annual rate of return is 99.86%, the maximum retracement rate is 15.73%, the Sharpe rate is 2.05 and the price earnings ratio is 4.01.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)