This study analyzes the trends, context, and impact of corporate social responsibility (CSR) initiatives on company’s performance and productivity in China. We use environmental and social responsibility data in 34,000 CSR projects released by 839 companies in 31 provinces from 2006 to 2016. Clustering methods as wells as ordinary least squares and the fixed effects panel regression modeling are performed to provide insights on the context, trends, and impact of CSR projects on companies’ productivity and financial outcomes. Results of data processing and modeling indicate that: (a) most projects focused on improving companies’ environmental sustainability (compared to social); (b) implementation of both environmental and social projects had positive impacts on companies’ performance; and (c) trends, context, and impact of the projects varied with time, company type, and location (provinces). In addition, data suggest that companies operating in regions with lower economic conditions (GDP per capita) seem to be less motivated to implement environmental and social sustainability projects compared to those operating in regions with higher economic conditions. This study is meaningful for both companies that consider adopting CSR initiatives, as well as stakeholders and managers who aim to promote sustainable development in China.

High-frequency trading dominates trading in financial markets. How it affects the low-frequency trading, however, is still unclear. Using NASDAQ order book data, the authors investigate this question by categorizing orders as either high or low frequency, and examining several measures. They find that high-frequency trading enhances liquidity by increasing the trade frequency and quantity of low-frequency orders. High-frequency trading also reduces the waiting time of low-frequency limit orders and improves their likelihood of execution. The results indicate that high-frequency trading has a liquidity provision effect and improves the execution quality of low-frequency orders.

In the modern electricity markets, negative prices and spike prices coexist as a pair of opposite economic phenomena. This study investigates how these extreme prices play as the determinants to drive price fluctuations in the electricity market. We construct a two-stage analysis including a principal component analysis (PCA) and a nonlinear autoregressive distributed lags model (NARDL). We apply this analytical method to the wholesale Pennsylvania, New Jersey and Maryland (PJM) electricity market. We find that according to PCA, in the individual transmission lines, spike prices are determinants with largest explanatory power to the variation of prices, while according to NARDL, from the standpoint of the overall market, negative prices have a larger potential effect on both the real-time market and the forward market. These results are valuable and contributive to managers and operators in the electricity markets for policy decision making.

Large price fluctuations have become a significant character and impede resource allocation in the electricity market. Negative prices and peak load spike prices coexist and represent over-supply and over-demand, respectively. It is important to interpret the impact of these extreme prices on sustainable power management from the perspective of economics. In this paper, we build a principal component analysis (PCA) to assess the impact of the two opposite phenomena on the smart grid electricity system. We perform a big-data study using intra-day data from the Pennsylvania, New Jersey, and Maryland (PJM) electricity system with over 11,000 transmission lines. As the contribution, this paper (1) measures the price fluctuations from the perspective of economics, (2) captures and observes the full-length behavior of negative and spike pricing in a modern smart grid system with multi-transmission lines and high-frequency price updates, and (3) employs methods with distinctive advantages to bring more in-depth findings to interpret the smart grid system. We find that spike prices hold the principal explanatory power for electricity market fluctuation in all the transmission lines. The results are consistent with previous studies about resolutions such as electrical energy storage, transmission capacity upgrade, and demand response.