This paper develops a depth-gated recurrent neural network (DGRNN) with self-attention mechanism (SAM) based on long-short-term memory (LSTM)\gated recurrent unit (GRU) \Just Another NETwork (JANET) neural network to improve the accuracy of credit spread prediction. The empirical results of the U.S. bond market indicate that the DGRNN model is more effective than traditional machine learning methods. Besides, we discovered that the Depth-JANET model with one gated unit performs better than Depth-GRU and Depth-LSTM models with more gated units. Furthermore, comparative analyses reveal that SAM significantly improves DGRNN’s prediction performance. The results show that Depth-JANET neural network with SAM outperforms most other methods in credit spread prediction.

This paper attempts to evaluate the coordinated development state of the subsystems within the internet financial ecosystem in China from 2011 to 2016. Focusing on the main business modes, technological innovation, and the external environment, we select 29 indicators to construct an index system and adopt a coupling coordination degree model for evaluation. Furthermore, we use two weight calculation methods, entropy weight and principal component analysis, to ensure the robustness of the results. The empirical results show that China’s internet financial ecosystem experienced five development stages from 2011 to 2016, which are moderate disorder, near disorder, weak coordination, intermediate coordination, and good coordination. Different methods of obtaining weights have little effect on the empirical results. These findings suggest that at the beginning, the coordinated development of China’s internet financial ecosystem was hindered by factors including the scarcity of main business modes and the defect of technological innovation; then, with the rapid development of China’s internet industry, the external environment became another drawback in coordinated development. Finally, based on the findings, we give some policy recommendations from a global perspective to achieve a sustainable internet financial ecosystem.

This paper develops the interval maximum entropy model for the interval European option valuation by estimating an underlying asset distribution. The refined solution for the model is obtained by the Lagrange multiplier. The particle swarm optimization algorithm is applied to calculate the density function of the underlying asset, which can be utilized to price the Shanghai Stock Exchange (SSE) 50 Exchange Trades Funds (ETF) option of China and the Boeing stock option of the United States. Results show that maximum entropy distribution provides precise estimations for the underlying asset of interval number situations. In this way, we can get the distribution of the underlying assets and apply it to the interval European option pricing in the financial market.