Different from traditional algorithms and model, machine learning is a systematic and comprehensive application of computer algorithms and statistical models, and it has been widely used in many fields. In the field of finance, machine learning is mainly used to study the future trend of capital market price. In this paper, to predict the time-series data of stock, we applied the traditional models and machine learning models for forecasting the linear and non-linear problem, respectively. First, stock samples that occurred from year 2010 to 2019 at the New York Stock Exchange are collected. Next, the ARIMA (autoregressive integrated moving average model) model and LSTM (long short-term memory) neural network model are applied to train and predict stock price and stock price subcorrelation. Finally, we evaluate the proposed model by several indicators, and the experiment results show that: (1) Stock price and stock price correlation are accurately predicted by the ARIMA model and LSTM model; (2) compared with ARIMA, the LSTM model performance better in prediction; and (3) the ensemble model of ARIMA-LSTM significantly outperforms other benchmark methods. Therefore, our proposed method provides theoretical support and method reference for investors about stock trading in China stock market.

The coronavirus (COVID-19) outbreak in the China has exposed small-and medium-sized enterprises (SMEs) to a variety of challenges, some of which are potentially life-threatening to their sustainability. Therefore, this study aims to investigate the macroeconomic lockdown effects of COVID-19 on small business in China. A survey questionnaire with 313 participants was used to collect the data. In this study, the SEM technique was used to analyse model. The data have been gathered for the study from the managers and employees of Chinese SMEs. The findings of the study show that COVID-19 has a significant negative impact on financial performance, operational performance, profitability, access to finance, and customer satisfaction. According to the study's findings, external support aids have a greater impact on SMEs' ability to survive and thrive through innovation than on their actual performance. The findings of this study have a number of important practical consequences for small-and medium-sized business owners, governments, and policymakers.

As a consequence of the COVID-19 pandemic outbreak, most commodities experienced significant price drops, which were expected to continue well into 2020. As a result, the Markov switching model is used to study the influence of policy uncertainty and the COVID-19 pandemic on commodity prices in the USA. Commodity markets are stimulated by economic policy uncertainty, according to results from a two-state Markov switching model. In both high and low regimes, economic policy uncertainty (EPU) influences the commodity market, according to the study's findings. However, in the high regime, EPU has a greater influence on the energy and metal sectors. EPU has different influences on commodity markets in highand low-volatility regimes, according to this study. There is a wide range of correlations between COVID-19 outcomes and EPU and how the prices of natural gas, oil, corn, silver, soybean, copper, gold, and steel respond to these tremors, in both high-and low-volatility tenure. Oil and natural gas, on the other hand, are unaffected by shifts in COVID-19 death rates under either regime. Results show that in both high-and low-volatility regimes, the demand and supply for most commodities are responsive to historical prices.

Innovation has been a major growing driver of sustainability. The topic addressed in this study is a much-required transition to environmental and social sustainability considering the role of innovation in pacing up those changes. Digital evolution has greatly helped in dealing with climatic changes and promoting sustainability. This has helped the entrepreneurial organizations to adopt innovative approaches to tackle the inflexible challenges. Few developed and developing countries are at the forefront regarding technological innovation that encounter significant challenges in terms of innovation and adoption of new technologies and there is still a study vacuum as to whether the influence of technical innovation on achieving social and environmental sustainability differs depending on the stage of sustainability. This quantitative study has explored these effects collecting data from the SME's (small and medium enterprises). The findings of the study show that attitude toward technological innovation has a strong role to play in organizational innovation, digital entrepreneurship, environmental and social sustainability. Organizational innovation has been found a strong mediator between technological innovation and sustainability while digital entrepreneurship could not find significant results as mediator. This study will be useful for the countries and organizations involved in adopting new technologies considering their organization's role in achieving an overall eco-friendly and social sustainability.

The application of information technology and various electronic communication equipment has grown rapidly. At the same time, information technologies such as the Internet and communication networks have become increasingly mature and widely used, making e-commerce transactions simpler and the roles of enterprises in the supply chain increasingly diversified. At this stage, supply chain finance has become an important way for small- and medium-sized enterprises to finance, and it is a key step in commercial trade. However, the risk control of this model is difficult to be effectively contained. How to control its financial risk to the lowest level is the research goal of this paper. This paper analyzes and calculates the supply chain financial risks of different enterprises through a questionnaire method, a case analysis method, and a comparison method and obtains relevant data. The data results show that the entropy value of the net interest rate is 0.97, which indicates that it has a larger market share and less risk. Through wireless multimedia communication technology and artificial intelligence algorithms, the system calculation of supply chain financial risk management is much simpler. In this regard, the research proposes a scientific system for building supply chain financial risk management.