Digital Economic Development and Its Impact on Econimic Growth in China: Research Based on the Prespective of Sustainability

At present, there is a consensus that the digital economy provides a new impetus for sustainable economic development. Based on domestic and foreign literature reviews, this paper focuses on representative industry sectors; we present China’s 2011–2018 digital economy development index, for 173 cities, from a three-level perspective—internet development, digital literacy, and industrial efficiency improvement. Various models, such as the instrumental variable method, the double difference method, the intermediary effect model, and the spatial econometric model were used to quantitatively analyze the impact of digital economic development on urban economic growth in China. The study finds that: (1) digital economic development in China has a positive effect on urban economic growth, and a heterogeneity of effects exists between different cities. (2) Urban employment is the “effect mechanism” of digital economic growth on urban economic growth. (3) The direct effect of digital economic development on urban economic growth in China is positive, the spillover effect is positive, the direct effect is greater than the spillover effect, and the total effect is positive. The research results enrich the measurement methods used in urban digital economic development in China, providing new perspectives for studying the influence mechanisms of digital economic development on urban economic growth

Financial Volatility Forecasting: A Sparse Multi-Head Attention Neural Network

Accurately predicting the volatility of financial asset prices and exploring its laws of movement have profound theoretical and practical guiding significance for financial market risk early warning, asset pricing, and investment portfolio design. The traditional methods are plagued by the problem of substandard prediction performance or gradient optimization. This paper proposes a novel volatility prediction method based on sparse multi-head attention (SP-M-Attention). This model discards the two-dimensional modeling strategy of time and space of the classic deep learning model. Instead, the solution is to embed a sparse multi-head attention calculation module in the network. The main advantages are that (i) it uses the inherent advantages of the multi-head attention mechanism to achieve parallel computing, (ii) it reduces the computational complexity through sparse measurements and feature compression of volatility, and (iii) it avoids the gradient problems caused by long-range propagation and therefore, is more suitable than traditional methods for the task of analysis of long time series. In the end, the article conducts an empirical study on the effectiveness of the proposed method through real datasets of major financial markets. Experimental results show that the prediction performance of the proposed model on all real datasets surpasses all benchmark models. This discovery will aid financial risk management and the optimization of investment strategies

Reducing Exchange Rate Risks in International Trade: A Hybrid Forecasting Approach of CEEMDAN and Multilayer LSTM

In international trade, it is common practice for multinational companies to use financial market instruments, such as financial derivatives and foreign currency debt, to hedge exchange rate risks. Making accurate predictions and decisions on the direction and magnitude of exchange rate movements is a more direct way to reduce exchange rate risks. However, the traditional time series model has many limitations in forecasting exchange rate, which is nonlinear and nonstationary. In this paper, we propose a new hybrid model of complete ensemble empirical mode decomposition (CEEMDAN) based multilayer long short-term memory (MLSTM) networks. It overcomes the shortcomings of the classic methods. CEEMDAN not only solves the mode mixing problem of empirical mode decomposition (EMD), but also solves the residue noise problem which is included in the reconstructed data of ensemble empirical mode decomposition (EEMD) with less computation cost. MLSTM can learning more complex dependences from exchange rate data than the classic model of time series. A lot of experiments have been conducted to measure the performance of the proposed approach among the exchange rates of British pound, the Australian dollar, and the US dollar. In order to get an objective evaluation, we compared the proposed method with several standard approaches or other hybrid models. The experimental results show that the CEEMDAN-based MLSTM (CEEMDAN–MLSTM) goes on better than some state-of-the-art models in terms of several evaluations