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Impact of the Adjustment of Maximum Order Volume on Pricing Efficiency of Stock Index Futures in China
In April 2017, China Financial Futures Exchange adjusted the maximum order volume of single trading in stock index futures, and this paper conducts research on this event. Firstly, it analyzes the influence of the adjustment of maximum order volume on the characteristics of the limit order book with high-frequency data and the impact of ordering situation on the trading depth and volatility of each contract with panel data. Secondly, it takes high-frequency tick-by-tick data to explore the causal relationship between the ordering situation and the probability of informed trading and analyzes the impact of the event on the probability of informed trading. Finally, the dynamic factor analysis method is used to quantify the pricing efficiency based on the probability of informed trading and the characteristics of limit order book, and the influence of the event on the pricing efficiency of stock index futures market is discussed. The results show that the reduction of maximum order volume has different effects on dominant contracts and nondominant contracts of stock index futures. After the event, the overall trading volume of the market increased, where the trading volume of dominant contracts decreased and that of nondominant contracts increased. For dominant contracts, the depth, slope, and liquidity decrease, the spread increases, and the probability of informed trading decreases so that the pricing efficiency becomes worse, while the results of nondominant contracts are the opposite. For Chinese stock index futures market, the pricing efficiency is greatly reduced and the resource allocation capacity is weakened under the influence of the event. Therefore, the adjustment of maximum order volume is not conducive to the healthy development of the stock index futures market. It is suggested that the reduction of the maximum order volume is only implemented for nondominant contracts.
Day-to-Day Traffic Assignment Model considering Information Fusion and Dynamic Route Adjustment Ratio
A new day-to-day traffic assignment model is proposed to describe travelers’ day-to-day behavioral changes with advanced traffic information system. In the model, travelers’ perception is updated by a double exponential-smoothing learning process combining experience and traffic information that is explicitly modelled. Route adjustment ratio is dynamically determined by the difference between perceived and expected utilities. Through theoretical analyses, we investigate the existence of its fixed point and the influence factors of uniqueness of the fixed point. An iterative-based algorithm that can solve the fixed point is also given. Numerical experiments are then conducted to investigate effects of several main parameters on its convergence, which provides insights for traffic management. In addition, we compare the system efficiencies under the static route adjustment ratio and dynamic route adjustment ratio and show the application of the model.
RGBD Scene Flow Estimation with Global Nonrigid and Local Rigid Assumption
RGBD scene flow has attracted increasing attention in the computer vision with the popularity of depth sensor. To estimate the 3D motion of object accurately, a RGBD scene flow estimation method with global nonrigid and local rigid motion assumption is proposed in this paper. Firstly, the preprocessing is implemented, which includes the colour-depth registration and depth image inpainting, to processing holes and noises in the depth image; secondly, the depth image is segmented to obtain different motion regions with different depth values; thirdly, scene flow is estimated based on the global nonrigid and local rigid assumption and spatial-temporal correlation of RGBD information. In the global nonrigid and local rigid assumption, each segmented region is divided into several blocks, and each block has a rigid motion. With this assumption, the interaction of motion from different parts in the same segmented region is avoided, especially the nonrigid object, e.g., a human body. Experiments are implemented on RGBD tracking dataset and deformable 3D reconstruction dataset. The visual comparison shows that the proposed method can distinguish the motion parts from the static parts in the same region better, and the quantitative comparisons proved more accurate scene flow can be obtained.
Political Connections, Debt Restructuring, and Enterprise Investment: Evidence from China
This paper shows that the link between debt restructuring and enterprise investment in emerging economies hinges critically on the political connections. Taking Chinese A-share listed enterprises from 2005 to 2016 as samples, we examine whether and how political connections affect the relationship between debt restructuring and enterprise investment based on the DID method. The results show that compared with nonpolitically connected enterprises, debt restructuring effectively improves the investment efficiency in enterprises with political ties, which is mainly due to alleviating the overinvestment and underinvestment. Furthermore, the positive effect of debt restructuring on investment is more prominent in enterprises with weak strength of political connection. It is worth noting that as the strength of political connection increases from weak to strong, the positive impact of debt restructuring on investment turns to negative impact, which reflects the heterogeneity of connection strength on the relation between debt restructuring and investment. This paper provides new evidence for understanding the investment behaviour of debt restructuring enterprises and provides some policy implications for managers and decision-makers intending to improve the investment efficiency and enhance the sustainable development of enterprises from the perspective of political connections.
Analysis and Identification of Students with Financial Difficulties: A Behavioural Feature Perspective
The identification of students with financial difficulties is one of the main problems in campus data research. Effective and timely identification not only provides convenience to campus administrators but also helps students who are really in financial hardship. The popular using of smart cards makes it possible to identify students with financial difficulties through big data. In this paper, we collect behavioural records from undergraduate students’ smart cards and propose five features by which to associate with students’ poverty level. Based on these features, we proposed the Apriori Balanced Algorithm (ABA) to mine the relationship of poverty level with students’ daily behaviour. Association rules show that students’ poverty level is most closely related to their academic performance, followed by consumption level, diligence level, and life regularity. Finally, we adopted the semisupervised K-means algorithm to more accurately find out students with financial difficulties. Tested by classical classification algorithms, our method has a higher identification rate, which is helpful for university administrators discover students in real financial hardship effectively.
Travelling Wave Solutions of Wu–Zhang System via Dynamic Analysis
In this paper, based on the dynamical system method, we obtain the exact parametric expressions of the travelling wave solutions of the Wu–Zhang system. Our approach is much different from the existing literature studies on the Wu–Zhang system. Moreover, we also study the fractional derivative of the Wu–Zhang system. Finally, by comparison between the integer-order Wu–Zhang system and the fractional-order Wu–Zhang system, we see that the phase portrait, nonzero equilibrium points, and the corresponding exact travelling wave solutions all depend on the derivative order . Phase portraits and simulations are given to show the validity of the obtained solutions.