Three Essays on Market Mechanisms in the Governance of Modern Firms

This study investigates the role of market mechanisms on corporate governance through the interactions of corporate insiders (e.g., CEO and board of directors) and outside investors (e.g., short sellers and institutional investors) in information leakage, CEO human capital preference and product differentiation respectively

Do Insiders Trade on Government Subsidies?

We examine whether and how insiders trade on government subsidies, a major instrument through which the governments intervene in the economy. Using a novel dataset of government subsidies of Chinese listed firms, we find that net insider purchase increases significantly during the month of subsidy receipt. The effect of subsidies on insider trading is weaker in firms with a more transparent information environment and when subsidies are granted in a more predictable manner. In contrast, the effect is more pronounced for politically connected firms. Further analysis shows that the subsidy-trading relation may reflect both insiders’ informational advantage concerning subsidies and their superior ability to detect mispricing-related opportunities. Our findings provide new insights into the capital market consequences of government subsidies through the lens of insider trading

Population Ageing, Labour Market Rigidity and Corporate Innovation: Evidence from China

Population ageing leads to labour scarcity and labour market rigidity. Contrary to supply-side economists’ belief that labour market rigidity tends to suppress firm innovation, we provide novel evidence of a positive relationship between population ageing and firm innovation in China. This enhancement effect is greater for firms with higher labour costs, consistent with the argument that labour scarcity encourages labour-saving innovation in response to demographic shifts. In addition, the observed positive effect is particularly pronounced for state-owned enterprises, which are widely acknowledged to be overstaffed with older workers, and firms in industries that pursue Schumpeter-II innovation and engage in more intense research and development. In addition, population ageing helps firms to generate more exploitative (vs. exploratory) innovation. Overall, our findings suggest that firms facing population ageing can adapt their strategies to innovate successfully

Trace residue analysis of dicyandiamide, cyromazine, and melamine in animal tissue foods by ultra-performance liquid chromatography

AbstractAn effective sample preparation procedure using an accelerated solvent extraction (ASE) procedure, followed by cleaning with melamine molecularly imprinted polymers solid-phase extraction (MISPE) was developed. A novel and highly sensitive ASE–MISPE–ultra-performance liquid chromatography (UPLC) method was developed for effective separation and simultaneous determination of dicyandiamide (DCD), cyromazine (CYR), and melamine (MEL) in complex animal tissue foods. Under optimized conditions, good linearity was achieved with a correlation coefficient (r) of 0.9999 in the range of at least two orders of magnitude. The limit of quantification of the method was 1.7 μg/kg, 5.0 μg/kg, and 3.2 μg/kg for DCD, MEL, and CYR, which was three orders of magnitude smaller than the maximum residue limits (MRLs). The intra- and inter-day precisions (in terms of the relative standard deviation, RSD) of the three analytes were in the range of 1.7–3.1% and 3.1–6.3%, respectively. The average recoveries of analytes from blank chicken, beef, mutton, pork, and pig liver samples spiked with the three levels varied from 91.2% to 107% with RSD of 1.7–8.3% for DCD, 89.0–104% with RSD of 2.1–6.1% for CYR, and 94.8–105% with RSD of 1.1–6.6% for MEL. The proposed method has the characteristics of speed, sensitivity, and accuracy, and can be used for the routine determination of DCD, CYR, and MEL at the μg/kg level in complex animal tissue foods

Energy Efficient Robust Beamforming for Vehicular ISAC with Imperfect Channel Estimation

This paper investigates robust beamforming for system-centric energy efficiency (EE) optimization in the vehicular integrated sensing and communication (ISAC) system, where the mobility of vehicles poses significant challenges to channel estimation. To obtain the optimal beamforming under channel uncertainty, we first formulate an optimization problem for maximizing the system EE under bounded channel estimation errors. Next, fractional programming and semidefinite relaxation (SDR) are utilized to relax the rank-1 constraints. We further use Schur complement and S-Procedure to transform Cramer-Rao bound (CRB) and channel estimation error constraints into convex forms, respectively. Based on the Lagrangian dual function and Karush-Kuhn-Tucker (KKT) conditions, it is proved that the optimal beamforming solution is rank-1. Finally, we present comprehensive simulation results to demonstrate two key findings: 1) the proposed algorithm exhibits a favorable convergence rate, and 2) the approach effectively mitigates the impact of channel estimation errors.Comment: Submitted to IEEE for future publicatio

Head-to-Tail: How Knowledgeable are Large Language Models (LLM)? A.K.A. Will LLMs Replace Knowledge Graphs?

Since the recent prosperity of Large Language Models (LLMs), there have been interleaved discussions regarding how to reduce hallucinations from LLM responses, how to increase the factuality of LLMs, and whether Knowledge Graphs (KGs), which store the world knowledge in a symbolic form, will be replaced with LLMs. In this paper, we try to answer these questions from a new angle: How knowledgeable are LLMs? To answer this question, we constructed Head-to-Tail, a benchmark that consists of 18K question-answer (QA) pairs regarding head, torso, and tail facts in terms of popularity. We designed an automated evaluation method and a set of metrics that closely approximate the knowledge an LLM confidently internalizes. Through a comprehensive evaluation of 14 publicly available LLMs, we show that existing LLMs are still far from being perfect in terms of their grasp of factual knowledge, especially for facts of torso-to-tail entities

New Interpretations of Normalization Methods in Deep Learning

In recent years, a variety of normalization methods have been proposed to help train neural networks, such as batch normalization (BN), layer normalization (LN), weight normalization (WN), group normalization (GN), etc. However, mathematical tools to analyze all these normalization methods are lacking. In this paper, we first propose a lemma to define some necessary tools. Then, we use these tools to make a deep analysis on popular normalization methods and obtain the following conclusions: 1) Most of the normalization methods can be interpreted in a unified framework, namely normalizing pre-activations or weights onto a sphere; 2) Since most of the existing normalization methods are scaling invariant, we can conduct optimization on a sphere with scaling symmetry removed, which can help stabilize the training of network; 3) We prove that training with these normalization methods can make the norm of weights increase, which could cause adversarial vulnerability as it amplifies the attack. Finally, a series of experiments are conducted to verify these claims.Comment: Accepted by AAAI 202