Empirical study on the efficiency of the stock index futures market from the information and functional perspectives–empirical evidence from China

This paper studies the effectiveness of the CSI 300 index futures markets from the perspective of information efficiency and function efficiency and examines the nonlinear dynamic characteristics of efficiency by using nonparametric methods. For information effectiveness, we find that the price of stock index futures follows a random walk. For function effectiveness, the results show that (1) the average optimal hedge ratio is 0.8702, and the average effective level reaches 86.11%. (2) The error correction mechanism is only supported by stock index futures. The error correction effect only exists in the extreme regime (only 6% of the total observed value). Most of the time (94%), both prices are subject to random walk process. There is no arbitrage trade between futures and spots. (3) Both linear and nonlinear leadership are observed in stock index futures. The nonlinear leadership is mainly reflected in stock index futures. Both leadership types are influenced by institutional changes and significant financial events and evolve over time, which indicates that stock index futures cannot play the dominant role in price discovery. In sum, we conclude that the CSI 300 stock index futures market is effective, despite the flaws in price discovery

Is Robustness Transferable across Languages in Multilingual Neural Machine Translation?

Robustness, the ability of models to maintain performance in the face of perturbations, is critical for developing reliable NLP systems. Recent studies have shown promising results in improving the robustness of models through adversarial training and data augmentation. However, in machine translation, most of these studies have focused on bilingual machine translation with a single translation direction. In this paper, we investigate the transferability of robustness across different languages in multilingual neural machine translation. We propose a robustness transfer analysis protocol and conduct a series of experiments. In particular, we use character-, word-, and multi-level noises to attack the specific translation direction of the multilingual neural machine translation model and evaluate the robustness of other translation directions. Our findings demonstrate that the robustness gained in one translation direction can indeed transfer to other translation directions. Additionally, we empirically find scenarios where robustness to character-level noise and word-level noise is more likely to transfer

Human Mobility Trends during the COVID-19 Pandemic in the United States

In March of this year, COVID-19 was declared a pandemic and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations regarding the pandemic propagation and the non-pharmaceutical interventions. All mobility metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states and becomes more stable after the stay-at-home order with a smaller range of fluctuation. There exists overall mobility heterogeneity between the income or population density groups. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. The study suggests that the public mobility trends conform with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.Comment: 11 pages, 9 figure