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In this study, we used a difference-in-difference (DID) approach to analyze the effect of environmental regulation on corporate tax avoidance behavior based on China's carbon emissions trading pilot policy of 2013. Our findings were as follows: (1) Environmental regulation has led companies to adopt further tax evasion behaviors. Furthermore, the core conclusion was confirmed after a series of robust and endogenous tests, such as parallel trends and PSM-DID (propensity score matching-difference-in-difference). (2) Environmental regulations increase tax avoidance activities by reducing corporate cash flows. (3) The influence of environmental regulation on firm tax evasion is highly pronounced among non-state-owned enterprises, big-scale enterprises, and enterprises with a high degree of industry competition. [ABSTRACT FROM AUTHOR]

Upper Triassic (Carnian) marine successions in the northwestern margin of Upper Yangtze Region (Sichuan Basin, China) show a lithological change from grey oolitic into a sponge-mound limestone (Units 1 and 2 of the lower member of the Ma'antang Formation), then overlain by greyish black to dark grey sandy shale and siltstone (Units 3 and 4 of the upper member of the Ma'antang Formation). Siliceous sponge mounds were built by Hexactinellida, and this succession was examined in three localities: Jushui section in Anxian (JS), and Guanyinya (HWG) and Qingyangou sections (HWQ) in Hanwang, Mianzhu. The conodont Quadralella polygnathiformis confirms a Carnian age for the biolithite and oolitic limestone of the lower Ma'antang Formation. Abundant ammonoids identified as belonging to the Discotropitid and Juvavitid families suggest a Tuvalian 1 (early Late Carnian) age for the lowermost part of the greyish black sandy shale that overlies the sponge mound at the Jushui Section. Field investigations and microfacies analysis suggest that sponge mounds had two stages of growth in relatively deeper water with low energy. The first sponge-growth mound stage ended in a regional karstified omission surface, and was followed by a sudden increase of siliciclastic input. Greyish black shales containing plant fossils cover a second Upper Carnian sponge mound stage, which is mainly recorded in HWG. The onset of these shales may be related to the Carnian Pluvial Phase (CPP) documented in the western Tethys region (e.g., in Italy, Austria and Hungary). The demise of the sponge mounds at all three sections may have been triggered by the joint effect of climatic changes associated with the fresh water input caused by CPP and with a relative sea-level change caused by local tectonic movements in the course of the Indosinian orogeny. [ABSTRACT FROM AUTHOR]

• Construct a light model library, train different light model for each market distribution. • Devise a novel reward function based on inverse reinforcement learning, and estimate the profits of each order. • Devise an algorithm for dynamically selecting model based on reinforcement learning. • Promising performance on real-world high-frequency trading in China Commodity Future market. In this paper, we propose an online model optimization algorithm based on reinforcement learning for quantitative trading. The combination of prediction model and trading policy is the most commonly used framework in practical quantitative trading. Integrated with machine learning methods, this framework brings huge profits to quantified companies. In the framework, the prediction model is used to predict future trading price trend, and the trading policy is used to determine the price and number of orders. Even though, the shortcomings of machine learning models are obvious, mainly are, (1) Slow prediction speed. Huge human-craft features and model computing cost much time, which is ten times of pure trading policy without model. (2) Poor generalization. This kind of models can hardly adapt to market data in each period, because market traders will change time to time at micro level, thus the distribution of market data will change. But current model is trained on a long period dataset, it achieves best effect at average, but can not adapt to different market at each period. To address this problem, we propose a novel online model optimization algorithm. A light model library will be constructed. Each light model in this library corresponds to a different market distribution. By devising the appropriate reward function via inverse reinforcement learning algorithm, the algorithm can accurately estimate the profits of each model. Then the model can be selected automatically in real-time trading, so that the trading policies can automatically adapt to changes in trading market, overcoming previous shortcoming of manually updating model and slow prediction speed. Experimental results show that the proposed algorithm achieves state-of-the-art performance on China Commodity Futures Market Data. [ABSTRACT FROM AUTHOR]