Cross listing, management earning forecasts, and firm values

This dissertation investigates the incentives for and consequences of management earnings forecasts released by foreign firms opting into the U.S. markets. It also attempts to measure how country-level governance mechanisms (e.g., legal institutions and the SEC enforcement) and firm-level governance mechanisms (e.g., ownership and auditor) interact to influence the answers. It is the earliest study to link management earnings forecasts, corporate governance and firm valuation in an international setting. Additionally, by investigating issues on voluntary information that cross-listed firms sequentially provide, this dissertation extends the Investor Recognition Hypothesis by Merton (1987), and adds to our understanding about reputational bonding mechanisms (Coffee, 1999; Siegel, 2005). Essay I provides a primer on the institutional background of cross-listed firms, and demonstrates that these firms are unique in regulatory, economic and legal schemes. Specifically, foreign firms listing in the U.S. are characterized by various home-country legal institutional environments, different listing statuses (ADR Level I, II, III, and direct listing), active global product market interactions and different firm-level concentrated ownerships. These distinctive aspects of cross-listed firms make my study relevant to the literature on management earnings forecasts as well as firm valuation. Essay 2 focuses on the incentives to provide management earnings forecasts released by foreign firms listing in the U.S. I document that legal institutions, as measured by legal origin, investor protection and judicial efficiency, are positively associated with the likelihood of forecast occurrence. In addition, cross-listed firms are more apt to release forecast disclosures when they list on major U.S. stock exchanges, and have a higher proportion of foreign sales. Further, I indicate that the likelihood of forecasts is positively associated with institutional ownership, but negatively associated with the proportion of cash flow rights controlled by the largest shareholders. Essay 3 explores how management earnings forecasts, other firm attributes, and country institutional factors interact with each other to affect firm values. I find that forecasting cross-listed firms enjoy higher valuation premiums relative to non-forecasting firms. I also provide evidence that cross-listed firms from weaker legal institutions benefit more from disclosing management earnings forecasts. Moreover, I demonstrate that forecast precision and forecast frequency are favorably associated with firm valuation. Overall, this essay suggests that cross-listed firms are rewarded for their voluntary bonding to transparent financial reporting practices. Key words. Crass Listing, Firm Values, International Governance Convergence, Legal Regimes, Management Earnings Forecasts, Reputational Bonding

The Effect of Voluntary Disclosure on Firm Risk and Firm Value: Evidence from Management Earnings Forecasts

This study investigates whether the voluntary disclosure of management earnings forecasts influences investors’ assessment of firm risk and firm value. We find a significant negative relationship between the issuance of management earnings forecasts and a variety of measures of firm risk (idiosyncratic risk, stock return volatility, beta, and bid-ask spreads), with more frequent, more precise and more accurate earnings forecasts further decreasing firm risk. Our results therefore suggest that information quality is an important determinant of both diversifiable risk and nondiversifiable systematic risk. We also demonstrate that management earnings forecasts are positively associated with firm value as captured by Tobin’s Q while more frequent, precise and accurate forecasts further enhance valuation premiums. Finally, we find that management earnings forecasts impact firm value not only through a reduction in firm risk, but also through changing investors\u27 perceptions about future cash flows. Our results are robust to various sensitivity checks. Overall, releasing high-quality management earnings forecasts is associated with important capital market benefits

Research on “Promoting teaching by competition” in the construction of electronic majors in local colleges and universities

This paper aims at training practical electronic undergraduate talents in local ordinary undergraduate colleges and universities. Through analyzing the current situation and common construction methods of electronic major construction, this paper proposes to integrate electronic design competition into teaching design. In the practice process, the competition training and daily teaching depth integration, to achieve the purpose of promoting teaching by competition, and at the same time in practice summed up several integration models, the common planning method of promoting teaching by competition is put forward

TD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation

Dynamic scene graph generation (SGG) focuses on detecting objects in a video and determining their pairwise relationships. Existing dynamic SGG methods usually suffer from several issues, including 1) Contextual noise, as some frames might contain occluded and blurred objects. 2) Label bias, primarily due to the high imbalance between a few positive relationship samples and numerous negative ones. Additionally, the distribution of relationships exhibits a long-tailed pattern. To address the above problems, in this paper, we introduce a network named TD$^2$-Net that aims at denoising and debiasing for dynamic SGG. Specifically, we first propose a denoising spatio-temporal transformer module that enhances object representation with robust contextual information. This is achieved by designing a differentiable Top-K object selector that utilizes the gumbel-softmax sampling strategy to select the relevant neighborhood for each object. Second, we introduce an asymmetrical reweighting loss to relieve the issue of label bias. This loss function integrates asymmetry focusing factors and the volume of samples to adjust the weights assigned to individual samples. Systematic experimental results demonstrate the superiority of our proposed TD$^2$-Net over existing state-of-the-art approaches on Action Genome databases. In more detail, TD$^2$-Net outperforms the second-best competitors by 12.7 \% on mean-Recall@10 for predicate classification.Comment: Accepted by AAAI 202

Calculation of electricity sales based on multi-factor correlation analysis

Electricity sales is one of the important assessment indexes of a power grid company’s operation. Since electricity sales is closely related to many factors, how to consider the influence of multiple factors and improve the accuracy of the calculation of electricity sales is a difficult problem that needs to be solved urgently. In this paper, we first analyze the six dimensions affecting electricity sales and select the key influencing factors that can be quantified statistically. Secondly, the key influencing factors are screened according to Pearson’s correlation coefficient and then the calculation model of electricity sales is established based on the random forest algorithm. Finally, we validate the feasibility and validity of the proposed calculation method for electricity sales through a case study

Towards Layer-Selective Quantum Spin Hall Channels in Weak Topological Insulator Bi4Br2I2

Weak topological insulators, constructed by stacking quantum spin Hall insulators with weak interlayer coupling, offer promising quantum electronic applications through topologically nontrivial edge channels. However, the currently available weak topological insulators are stacks of the same quantum spin Hall layer with translational symmetry in the out-of-plane direction, leading to the absence of the channel degree of freedom for edge states. Here, we study a candidate weak topological insulator, Bi4Br2I2, which is alternately stacked by three different quantum spin Hall insulators, each with tunable topologically non-trivial edge states. Our angle-resolved photoemission spectroscopy and first-principles calculations show that an energy gap opens at the crossing points of different Dirac cones correlated with different layers due to the interlayer interaction. This is essential to achieve the tunability of topological edge states as controlled by varying the chemical potential. Our work offers a perspective for the construction of tunable quantized conductance devices for future spintronic applications

Application of family-centered empowerment model in primary caregivers of premature infants: A quasi-experimental study

ObjectiveTo explore the effect of the family-centered empowerment model (FECM) on reducing anxiety, improving care ability, and readiness for hospital discharge of main caregivers of preterm infants.MethodsThe primary caregivers of preterm infants who were admitted to the Neonatal intensive care Unit (NICU) of our center from September 2021 to April 2022 were selected as the research objects. According to the wishes of the primary caregivers of preterm infants, they were divided into group A (FECM group) and group B (non-FECM group). The intervention effects were evaluated with the Anxiety Screening Scale (GAD-7), the Readiness for Hospital Discharge Scale-Parent Version (RHDS-Parent Form), and the Primary Caregivers of Premature Infants Assessment of Care Ability Questionnaire.ResultsBefore the intervention, there was no statistically significant difference in the general information, anxiety screening, the scores of each dimension, and total score of the comprehensive ability of the main caregivers, and the score of caregiver preparedness between the two groups (P > 0.05). After the intervention, there were statistically significant differences in the anxiety screening, the total score and total score of each dimension of the care ability, and the score of caregiver preparedness between the two groups (P < 0.05).ConclusionsFECM can effectively reduce the anxiety of primary caregivers of premature infants and improve their readiness for hospital discharge and care ability. To improve the quality of life of premature infants by implementing personalized training, care guidance, and peer support

Automatic segmentation of white matter hyperintensities in routine clinical brain MRI by 2D VB-Net: A large-scale study

White matter hyperintensities (WMH) are imaging manifestations frequently observed in various neurological disorders, yet the clinical application of WMH quantification is limited. In this study, we designed a series of dedicated WMH labeling protocols and proposed a convolutional neural network named 2D VB-Net for the segmentation of WMH and other coexisting intracranial lesions based on a large dataset of 1,045 subjects across various demographics and multiple scanners using 2D thick-slice protocols that are more commonly applied in clinical practice. Using our labeling pipeline, the Dice consistency of the WMH regions manually depicted by two observers was 0.878, which formed a solid basis for the development and evaluation of the automatic segmentation system. The proposed algorithm outperformed other state-of-the-art methods (uResNet, 3D V-Net and Visual Geometry Group network) in the segmentation of WMH and other coexisting intracranial lesions and was well validated on datasets with thick-slice magnetic resonance (MR) images and the 2017 medical image computing and computer assisted intervention WMH Segmentation Challenge dataset (with thin-slice MR images), all showing excellent effectiveness. Furthermore, our method can subclassify WMH to display the WMH distributions and is very lightweight. Additionally, in terms of correlation to visual rating scores, our algorithm showed excellent consistency with the manual delineations and was overall better than those from other competing methods. In conclusion, we developed an automatic WMH quantification framework for multiple application scenarios, exhibiting a promising future in clinical practice