Do Multiple Large Shareholders Affect Financing and Operating Strategies, and Firm Performance: Teen-aging of East Asian Owners

We investigate how the evolution of ownership structure affects corporate financial and operating performance and corporate strategies. In particular, we study whether the shift in control rights away from the dominant shareholder mitigates agency problems and accordingly expropriation of minority investors by the controlling shareholder. More specifically, does the increase in power of the second large shareholder manifest in the firm’s operating and financial performance, and financing and operating strategies? Using ownership data for 1996 and 2008 representing 403 firms from nine East Asian countries, we find strong and robust evidence that the change in the voting rights of the second largest shareholder over these twelve years is associated with higher firm valuation, better operating performance, better access to long term financing, more efficient operation management strategies and a higher dividend payout ratio. Consistent with prior literature that finds multiple large shareholders play an internal governance role and mitigate agency problems, our findings imply that an increase in the voting rights of the second large shareholder improves firm’s corporate governance and mitigates agency problems consequently increasing firm performance and improving strategies

Analysis of the Real Estate Enterprise’s Core Competitiveness Based on Sustainable Development

In China, the real estate industry has attracted great attention which healthy development or is of great of significance to build harmonious society. Otherwise, currently the majority in the industry as a whole is small developers, and there is a big gap in the core competitiveness, which has decisive influence on the future development of this industry. This article analysis the core competitiveness situation of the real estate and put forward the strategy based on the sustainable development for real estate enterprises. Key words: Sustainable development; Real-estate; Core competitivenes

Universal scaling of strange particle pTp_{\rm T} spectra in pp collisions

As a complementary study to that performed on the transverse momentum ($p_{\rm T}$) spectra of charged pions, kaons and protons in proton-proton (pp) collisions at LHC energies 0.9, 2.76 and 7 TeV, we present a scaling behaviour in the $p_{\rm T}$ spectra of strange particles ($K_{S}^{0}$, $\rm \Lambda$, $\rm \Xi$ and $\phi$) at these three energies. This scaling behaviour is exhibited when the spectra are expressed in a suitable scaling variable $z=p_{\rm T}/K$, where the scaling parameter $K$ is determined by the quality factor method and increases with the center of mass energy ($\sqrt{s}$). The rates at which $K$ increases with $\mathrm{ln}\sqrt{s}$ for these strange particles are found to be identical within errors. In the framework of the colour string percolation model, we argue that these strange particles are produced through the decay of clusters that are formed by the colour strings overlapping. We observe that the strange mesons and baryons are produced from clusters with different size distributions, while the strange mesons (baryons) $K_{S}^{0}$ and $\phi$ ($\rm \Lambda$ and $\rm \Xi$) originate from clusters with the same size distributions. The cluster's size distributions for strange mesons are more dispersed than those for strange baryons. The scaling behaviour of the $p_{\rm T}$ spectra for these strange particles can be explained by the colour string percolation model in a quantitative way.Comment: 8 pages, 10 figures, accepted by EPJ

How would Stance Detection Techniques Evolve after the Launch of ChatGPT?

Stance detection refers to the task of extracting the standpoint (Favor, Against or Neither) towards a target in given texts. Such research gains increasing attention with the proliferation of social media contents. The conventional framework of handling stance detection is converting it into text classification tasks. Deep learning models have already replaced rule-based models and traditional machine learning models in solving such problems. Current deep neural networks are facing two main challenges which are insufficient labeled data and information in social media posts and the unexplainable nature of deep learning models. A new pre-trained language model chatGPT was launched on Nov 30, 2022. For the stance detection tasks, our experiments show that ChatGPT can achieve SOTA or similar performance for commonly used datasets including SemEval-2016 and P-Stance. At the same time, ChatGPT can provide explanation for its own prediction, which is beyond the capability of any existing model. The explanations for the cases it cannot provide classification results are especially useful. ChatGPT has the potential to be the best AI model for stance detection tasks in NLP, or at least change the research paradigm of this field. ChatGPT also opens up the possibility of building explanatory AI for stance detection