A test of the value relevance of financial indicators and macroeconomic factors to the performance of Chinese stock market

1 online resource (iii, 41 p.)Includes abstract.Includes bibliographical references (p. 38-41).This paper firstly aims to investigate how certain essential financial indicators, such as P/E ratio, P/B ratio, earning per share (EPS), and book value per share (BVPS) are associated with the Chinese stocks’ performance measured by abnormal return and price. I used two models (the P/E-P/B model and the Ohlson (1995) model) to estimate the relationship between abnormal returns and financial indicators, amidst which the first model uses the abnormal return as a dependent variable and the other uses the price as an explained variable. Secondly, the study of this paper will move from firm-specific factors to macroeconomic factors to discuss how Chinese stock prices have been affected by macroeconomic variables using a linear multi-variables model. For the analysis, five macroeconomic variables, namely the inflation rate, the M2 supply, the long-term interest rate, the exchange rate, and the expected GDP growth rate were taken into consideration. Regarding the contribution of this paper, it measures the value relevance of accounting information by testing the efficiency of the strategy that investors, with the expectation of earning abnormal return and beating the market, purchase undervalued stocks identified by low P/E and P/B ratios. Besides, this paper simultaneously considered firm specific information and macroeconomic variables and found out what kind of indicators (firm-specific indicators or macroeconomic indicators) are more influential on the performance of Chinese stock market, while previous studies solely focus on either decisive issues. Chinese investors, therefore, can get an insight from this research in whether firm-specific factors or macroeconomic factors are more reliable for decision- making

Variable-Dependent Partial Dimension Reduction

Sufficient dimension reduction reduces the dimension of a regression model without loss of information by replacing the original predictor with its lower-dimensional linear combinations. Partial (sufficient) dimension reduction arises when the predictors naturally fall into two sets X and W, and pursues a partial dimension reduction of X. Though partial dimension reduction is a very general problem, only very few research results are available when W is continuous. To the best of our knowledge, none can deal with the situation where the reduced lower-dimensional subspace of X varies with W. To address such issue, we in this paper propose a novel variable-dependent partial dimension reduction framework and adapt classical sufficient dimension reduction methods into this general paradigm. The asymptotic consistency of our method is investigated. Extensive numerical studies and real data analysis show that our variable-dependent partial dimension reduction method has superior performance compared to the existing methods

Variable-Dependent Partial Dimension Reduction

Sufficient dimension reduction reduces the dimension of a regression model without loss of information by replacing the original predictor with its lower-dimensional linear combinations. Partial (sufficient) dimension reduction arises when the predictors naturally fall into two sets X and W, and pursues a partial dimension reduction of X. Though partial dimension reduction is a very general problem, only very few research results are available when W is continuous. To the best of our knowledge, none can deal with the situation where the reduced lower-dimensional subspace of X varies with W. To address such issue, we in this paper propose a novel variable-dependent partial dimension reduction framework and adapt classical sufficient dimension reduction methods into this general paradigm. The asymptotic consistency of our method is investigated. Extensive numerical studies and real data analysis show that our variable-dependent partial dimension reduction method has superior performance compared to the existing methods

Evolution of Zhangjiang National Independent Innovation Demonstration Zone’s Administration Function based on Ground Theory

Upon setting up, Zhangjiang National Independent Innovation Demonstration Zone has been playing its pioneer and model roles, who not only had made great achievements in the field of science & technology innovation and industrial park construction, but also had attempted successful reform in the aspect of administration function. The development and innovation of committee’s administration function can influence zhangjiang's capacity for independent innovation profoundly. This paper through the grounded theory analysis of Zhangjiang Demonstration Zone’s work plans from 2011 to 2014, studied the evolution of its administration committee’s administration functions and explored the development tendency of its administrative system reform, so as to provide effective guidance for the future development of Zhangjiang National Independent Innovation Demonstration Zone. Keywords: Grounded theory, Zhangjiang National independent innovation demonstration zone, Administration functio

VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection

The recent contrastive language-image pre-training (CLIP) model has shown great success in a wide range of image-level tasks, revealing remarkable ability for learning powerful visual representations with rich semantics. An open and worthwhile problem is efficiently adapting such a strong model to the video domain and designing a robust video anomaly detector. In this work, we propose VadCLIP, a new paradigm for weakly supervised video anomaly detection (WSVAD) by leveraging the frozen CLIP model directly without any pre-training and fine-tuning process. Unlike current works that directly feed extracted features into the weakly supervised classifier for frame-level binary classification, VadCLIP makes full use of fine-grained associations between vision and language on the strength of CLIP and involves dual branch. One branch simply utilizes visual features for coarse-grained binary classification, while the other fully leverages the fine-grained language-image alignment. With the benefit of dual branch, VadCLIP achieves both coarse-grained and fine-grained video anomaly detection by transferring pre-trained knowledge from CLIP to WSVAD task. We conduct extensive experiments on two commonly-used benchmarks, demonstrating that VadCLIP achieves the best performance on both coarse-grained and fine-grained WSVAD, surpassing the state-of-the-art methods by a large margin. Specifically, VadCLIP achieves 84.51% AP and 88.02% AUC on XD-Violence and UCF-Crime, respectively. Code and features will be released to facilitate future VAD research.Comment: Submitte

Open-Vocabulary Video Anomaly Detection

Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. However, current approaches are inherently limited to a closed-set setting and may struggle in open-world applications where there can be anomaly categories in the test data unseen during training. A few recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. However, such a setting focuses on predicting frame anomaly scores, having no ability to recognize the specific categories of anomalies, despite the fact that this ability is essential for building more informed video surveillance systems. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies. To this end, we propose a model that decouples OVVAD into two mutually complementary tasks -- class-agnostic detection and class-specific classification -- and jointly optimizes both tasks. Particularly, we devise a semantic knowledge injection module to introduce semantic knowledge from large language models for the detection task, and design a novel anomaly synthesis module to generate pseudo unseen anomaly videos with the help of large vision generation models for the classification task. These semantic knowledge and synthesis anomalies substantially extend our model's capability in detecting and categorizing a variety of seen and unseen anomalies. Extensive experiments on three widely-used benchmarks demonstrate our model achieves state-of-the-art performance on OVVAD task.Comment: Submitte

Generalized invariance principles for stochastic dynamical systems and their applications

Investigating long-term behaviors of stochastic dynamical systems often requires to establish criteria that are able to describe delicate dynamics of the considered systems. In this article, we develop generalized invariance principles for continuous-time stochastic dynamical systems. Particularly, in a sense of probability one and by the developed semimartingale convergence theorem, we not only establish a local invariance principle, but also provide a generalized global invariance principle that allows the sign of the diffusion operator to be positive in some bounded region. We further provide an estimation for the time when a trajectory, initiating outside a particular bounded set, eventually enters it. Finally, we use several representative examples, including stochastic oscillating dynamics, to illustrate the practical usefulness of our analytical criteria in deciphering the stabilization or/and the synchronization dynamics of stochastic systems

Invariance principles for G-Brownian-motion-driven stochastic differential equations and their applications to G-stochastic control

The G-Brownian-motion-driven stochastic differential equations (G-SDEs) as well as the G-expectation, which were seminally proposed by Peng and his colleagues, have been extensively applied to describing a particular kind of uncertainty arising in real-world systems modeling. Mathematically depicting long-time and limit behaviors of the solution produced by G-SDEs is beneficial to understanding the mechanisms of system's evolution. Here, we develop a new G-semimartingale convergence theorem and further establish a new invariance principle for investigating the long-time behaviors emergent in G-SDEs. We also validate the uniqueness and the global existence of the solution of G-SDEs whose vector fields are only locally Lipschitzian with a linear upper bound. To demonstrate the broad applicability of our analytically established results, we investigate its application to achieving G-stochastic control in a few representative dynamical systems

High-Mobility and Bias-Stable Field-Effect Transistors Based on Lead-Free Formamidinium Tin Iodide Perovskites

Electronic devices based on tin halide perovskites often exhibit a poor operational stability. Here, we report an additive engineering strategy to realize high-performance and stable field-effect transistors (FETs) based on 3D formamidinium tin iodide (FASnI3) films. By comparatively studying the modification effects of two additives, i.e., phenethylammonium iodide and 4-fluorophenylethylammonium iodide via combined experimental and theoretical investigations, we unambiguously point out the general effects of phenethylammonium (PEA) and its fluorinated derivative (FPEA) in enhancing crystallization of FASnI3 films and the unique role of fluorination in reducing structural defects, suppressing oxidation of Sn2+ and blocking oxygen and water involved defect reactions. The optimized FPEA-modified FASnI3 FETs reach a record high field-effect mobility of 15.1 cm2/(V·s) while showing negligible hysteresis. The devices exhibit less than 10% and 3% current variation during over 2 h continuous bias stressing and 4200-cycle switching test, respectively, representing the best stability achieved so far for all Sn-based FETs.</p

High-Mobility and Bias-Stable Field-Effect Transistors Based on Lead-Free Formamidinium Tin Iodide Perovskites

Electronic devices based on tin halide perovskites often exhibit a poor operational stability. Here, we report an additive engineering strategy to realize high-performance and stable field-effect transistors (FETs) based on 3D formamidinium tin iodide (FASnI3) films. By comparatively studying the modification effects of two additives, i.e., phenethylammonium iodide and 4-fluorophenylethylammonium iodide via combined experimental and theoretical investigations, we unambiguously point out the general effects of phenethylammonium (PEA) and its fluorinated derivative (FPEA) in enhancing crystallization of FASnI3 films and the unique role of fluorination in reducing structural defects, suppressing oxidation of Sn2+ and blocking oxygen and water involved defect reactions. The optimized FPEA-modified FASnI3 FETs reach a record high field-effect mobility of 15.1 cm2/(V·s) while showing negligible hysteresis. The devices exhibit less than 10% and 3% current variation during over 2 h continuous bias stressing and 4200-cycle switching test, respectively, representing the best stability achieved so far for all Sn-based FETs.</p