Short-term Performance Research Articles

Market Mispricing Timing and Post-acquisition Performance: Evidence from Chinese A-share Firms

This study examines the impact of market mispricing timing on the short-term and long-term performance of acquiring firms in the context of China's capital market. Using a comprehensive dataset of A-share listed companies engaged in mergers and acquisitions (M&A) between 2007 and 2021, we investigate whether M&A activities conducted during periods of market overvaluation lead to enhanced short-term performance while failing to generate sustained long-term benefits. Our findings reveal that acquisitions executed during market overvaluation exhibit significantly improved short-term stock market performance, reflecting the alignment of merger announcements with optimistic investor sentiment. However, such positive effects on short-term performance do not extend to long-term outcomes. The study underscores the critical influence of market timing on the immediate post-acquisition performance of acquiring firms but highlights its limited impact on long-term financial and market performance. These insights contribute to a better understanding of M&A behavior within the Chinese capital market, demonstrating that while capitalizing on market mispricing can yield short-term advantages, sustainable long-term gains hinge on other factors beyond market timing.

Qualitative Characteristics of Accounting Information (Declared with IFRS Standards) and Financial Performance : Statistical Study and Correlation Test

In an international context characterized by the dismantling of economic barriers, the globalization of monetary operations and the fluidity of financial movements, the application of IFRS standards is no longer a luxury, but rather an increasingly persistent necessity for Moroccan and international companies. In this context, and using a statistical analysis on SPSS, this research aims to assess the correlation between the quality of accounting information (declared in accordance with IFRS standards) and the financial performance (in short term and in medium and long term) of Moroccan companies. This quantitative research which uses a Likert-type questionnaire scored from 1 to 5, and through the responses collected from a sample of 314 individuals (composed of accountants, chartered accountants, professors, trainers, doctors and doctoral students), target to demonstrate and verify, statistically, the relationship of correlation and interdependence that connects between « the qualitative characteristics of accounting information » and « the financial performance ». The results confirmed a negative correlation « in the short term » between the aforementioned variables and a positive correlation relationship between the same variables « in the medium and short term ». In the medium and long term, the positive correlation between « the qualitative characteristics of the accounting information declared according to IFRS » and « the financial performance », will probably be a factor that encourages companies to practice international accounting standards despite the costs incurred to ensure this passage.

Tunnelling with Full-Face Shielded Machines: A 3D Numerical Analysis of an Earth Pressure Balance (EPB) Excavation Sequence Using the Finite Element Method (FEM)

Urban tunnelling can be highly challenging, especially in areas where limited ground settlements and environmental disturbance is required. Mechanised tunnelling is usually preferred in such ground environments, specifically Slurry or EPBM (Earth Pressure Balance Machine), depending on the ground properties. Being able to predict the anticipated tunnel behaviour at the preliminary stages of the project can be very beneficial in optimising not only the design, but also control the construction activities and completion times. In practice, the short-term excavation response and support performance focus primarily on design, since most site characterisation inputs are focused on material properties gained from short-term testing. Although the analysis of tunnelling is a three-dimensional (3D) problem, conventional approaches and design methods employed during the design and construction of underground openings are often based on the ground’s static response in two dimensions (2D). In this paper, an initial 2D model is generated in PLAXIS2D and RS2 (Rocscience) to test advanced constitutive models and compare transverse settlement profiles; subsequently, a complete 3D FEM numerical model in RS3 (Rocscience) was used to simulate an Earth Pressure Balance (EPB) excavation sequence. The 3D numerical model simulates the relevant EPB components such as face pressure, TBM shield, backfilling of the tail void (time-dependent hardening of the grout) and gradual segmental lining erections in the longitudinal direction. The presented numerical approach can be used by tunnel designers and engineers to predict the soil response in EPBM tunnelling.

Do CSR Activities Improve Short-Term Financial Performance? Competitive Mediating Effects of Job Satisfaction

Do CSR Activities Improve Short-Term Financial Performance? Competitive Mediating Effects of Job Satisfaction

Responses in cognitive hierarchy games are correlated with academic performance and the cognitive reflection test.

Economics and Finance undergraduate students from four cohorts played LUPI, a game rewarding the person submitting the lowest unique positive integer, for a small bonus in an exam. Some months later, they played this game again with financial incentives and took a cognitive reflection test (CRT). We find that submitted responses to different configurations of LUPI are correlated with short-term (i.e., exam grade) and medium-term (i.e., final grade and GPA) academic performance, as well as the score in the CRT.

A novel wind power prediction model improved with feature enhancement and autoregressive error compensation

Wind energy is a widely utilized form of clean energy with significant implications for maximizing its utilization and ensuring the stability of power systems. However, existing methods for short-term wind power prediction face challenges in effectively capturing hidden features in historical data characterized by local random fluctuations. Furthermore, these methods lack appropriate compensation mechanisms to account for deviations between predicted and actual wind farm results. This paper proposes a novel wind power prediction model that addresses these limitations through feature enhancement and autoregressive error compensation. The model emphasizes the relationship between six wind power variables collected in the field and utilizes bidirectional long short-term memory to capture bidirectional time series characteristics. The attention mechanism is employed to assign different weights to these features, enhancing their representation and reducing computational complexity. To overcome frequent local fluctuations in wind power, an autoregressive error compensation model is integrated to further improve prediction accuracy. The proposed method is validated through practical application in a wind power plant, and experimental results demonstrate significant reduction in root mean square error compared to other models. This approach effectively extracts change trends in non-stationary wind power data, enhancing short-term power prediction performance. The proposed method has potential for application in other large-scale wind farms within the hybrid prediction framework.

Media Coverage and the Incidence of Financial Restatements in Taiwan

Abstract This study investigates the impact of media coverage on the incidence of financial restatements by testing two competing hypotheses: the media monitoring hypothesis and the media pressure hypothesis. The media monitoring hypothesis suggests a negative relationship between media coverage and financial restatements, as media acts as an external monitor, improving the quality of financial reporting. The media pressure hypothesis posits a positive relationship, as media coverage can induce short-term performance pressure on managers, leading to opportunistic earnings manipulation and restatements. Analyzing a sample of Taiwan-listed companies from 2000 to 2021, our overall findings support for the media pressure hypothesis, indicating that higher media coverage increases the likelihood of financial restatements. Further results suggest that the positive impact of media coverage on restatements is mitigated by the presence of foreign institutional investors acting as substitution governance monitors. We also show that the positive impact of media coverage on restatements is more evident among firms with high coverage of directors’ and officers’ liability insurance, serving as a complementary mechanism to amplify the media-driven short-term performance pressure on managers. This study expands our knowledge of financial restatements and underscores the significance of acknowledging the role of media in short-term market pressure and the quality of financial reporting. JEL classification numbers: G14, G34, L82, M41. Keywords: Media Coverage, Financial Restatements, Corporate Governance, Short-term Pressure.

Pre-Exercise Caffeine Intake Attenuates the Negative Effects of Ramadan Fasting on Several Aspects of High-Intensity Short-Term Maximal Performances in Adolescent Female Handball Players.

The aim of this investigation was to determine whether, after Ramadan, pre-exercise caffeine intake can reduce any possible negative effects of this month on short-term maximal performances in young female handball players. A randomized study involved thirteen young female handball players. Participants performed a squat jump (SJ), Illinois agility test (AG), and 5 m run shuttles test (total (TD) and peak (PD) distances) at 08:00 AM and 06:00 PM on three different occasions: one week before Ramadan (Pre-R), the last week of Ramadan (R), and the week after Ramadan (Post-R). A placebo (Pla) or caffeine (Caff) (6 mg·kg-1) was administered 60 min before exercise test sessions at two distinct times of day (08:00 AM and 06:00 PM) during the two periods: Pre and Post-R. The PSQI and dietary intake were assessed during all testing periods. The results revealed that Pre-R, (SJ, AG, TD, and PD) test performances were greater in the evening (PM) than in the morning (AM) (all p < 0.001). However, compared with Pre-R, PM performances declined significantly during R (all p < 0.001) and Post-R (p < 0.05, p < 0.01, p < 0.01 and p < 0.001, respectively). In addition, Pre-R, AM Caff produced moderate significant improvements compared with AM Pla, with small-to-no beneficial effects observed with PM Caff in SJ (4.8% vs. 1%), AG (1.8% vs. 0.8%), TD (2.8% vs. 0.3%), and PD (6% vs. 0.9%). Nevertheless, Caff produced moderate ergogenic effects during both AM and PM sessions during Post-R in SJ (4.4% vs. 2.4%), AG (1.7% vs. 1.5%), TD (2.9% vs. 1.3%), and PD (5.8% vs. 3%) with values approaching those of Pre-R Pla within the same time of day (p > 0.05, p > 0.05, p < 0.05, and p < 0.05, respectively). In summary, pre-exercise Caff intake with a dose equivalent to 6 mg·kg-1 reduced the negative effects of Ramadan fasting in several aspects of short-term maximal performances in young female handball players at both times of the day.

Exploring the short-term in-field performance of Recovered Nitrogen from Manure (RENURE) materials to substitute synthetic nitrogen fertilisers

Exploring the short-term in-field performance of Recovered Nitrogen from Manure (RENURE) materials to substitute synthetic nitrogen fertilisers

Cognitive performance in open-plan office acoustic simulations: Effects of room acoustics and semantics but not spatial separation of sound sources

The irrelevant sound effect (ISE) characterizes short-term memory performance impairment during irrelevant sounds relative to quiet. Irrelevant sound presentation in most laboratory-based ISE studies has been rather limited to represent complex scenarios including open-plan offices (OPOs) and not many studies have considered serial recall of heard information. This paper investigates ISE using an auditory-verbal serial recall task, wherein performance was evaluated for relevant factors in simulating OPO acoustics: the irrelevant sounds including the semanticity of speech, reproduction methods over headphones, and room acoustics. Results (Experiments 1 and 2) show that ISE was exhibited in most conditions with anechoic (irrelevant) nonspeech sounds with/without speech, but the effect was substantially higher with meaningful speech compared to foreign speech, suggesting a semantic effect. Performance differences in conditions with diotic and binaural reproductions were not statistically robust, suggesting limited role of spatial separation of sources. In Experiment 3, statistically robust ISE were exhibited for binaural room acoustic conditions with mid-frequency reverberation times, T30 (s) = 0.4, 0.8, 1.1, suggesting cognitive impairment regardless of sound absorption representative of OPOs. Performance differences in T30 = 0.4 s relative to T30 = 0.8 and 1.1 s conditions were statistically robust. This emphasizes the benefits for cognitive performance with increased sound absorption, reinforcing extant room acoustic design recommendations. Performance differences in T30 = 0.8 s vs. 1.1 s were not statistically robust. Collectively, these results suggest that certain findings from ISE studies with idiosyncratic acoustics may not translate well to complex OPO acoustic environments.

Entrusted investment in China: Firm characteristics and economic consequences

Using a large sample of China's listed firms from 2001 to 2018, we investigate the determinants and consequences of entrusted investment in China. We find that firms with more idle funds and weaker corporate governance are more likely to engage in entrusted investment (EI). We also find that the stock market reacts positively to entrusted investment announcements. Further, EI improves short-term firm performance but has no effect in the long run. One possible reason is that EI reduces corporate R&D investment, which may have a negative impact on long-term performance. Finally, we find that the positive association between EI and short-term firm performance becomes more significant for firms without overseas experienced executives and non-high-tech firms. This study enriches the literature on the EI and corporate finance, and the findings have important implications for regulators, listed firms, and investors.

Auto adaptation of closed-loop insulin delivery system using continuous reward functions and incremental discretization

Several closed or hybrid loop controllers for Blood Glucose (BG) regulation, which are also known as Artificial Pancreas (AP) Systems or Automated Insulin Delivery systems (AIDs), are in development worldwide. Most AIDs are designed and evaluated for short-term performance, with a particular emphasis on the post-meal period. However, if controllers are not adapted properly to account for variations in physiology that affect Insulin Sensitivity (IS), the AIDs may perform inadequately. In this work, the performance of two Reinforcement Learning (RL) agents trained under both piecewise and continuous reward functions is evaluated in-silico for long-term adaptation of a Fully Automated Insulin Delivery (fAID) system. An automatic adaptive discretization scheme that expands the state space as needed is also implemented to avoid disproportionate state space exploration. The proposed agents are evaluated for long-term adaptation of the Automatic Regulation of Glucose (ARG) algorithm, considering variations in IS. Results show that both RL agents have improved performance compared to a rule-based decision-making approach and the baseline controller for the majority of the adult population. Moreover, the use of a continuous shaped reward function proves to enhance the performance of the agents further than a piecewise one.

The impact of green taxes on green innovation of enterprises: a quasi-natural experiment based on the levy of environmental protection taxes.

Green innovation is a strategic choice for Chinese enterprises to achieve in balancing economic performance and environmental benefits. Environmental protection tax (EPT) is the first green tax in China. How to fully leverage the institutional dividends of environmental tax reform to achieve green innovation in enterprises is of great significance for the high-quality development of China's current economy. This study takes the levy of environmental protection taxes as the quasi-natural experiment and uses DID, DDD, PSM-DID and so on to verify the impact of EPT on green innovation. The results show that EPT can improve green innovation through the path of legitimacy pressure and legitimacy management. Notably, the effects are more obvious in enterprises with non-state-owned, low-financing constraints and located in the eastern region. Furthermore, green innovations under the push of environmental protection tax can improve long-term performance, while it has a negative effect on short-term performance. The levy of EPT has the dual dividend effect of economy and environment. Moreover, this study explores the source of the legitimacy pressure and the strategic response of enterprises and provides guidance for government's precise implementation of policies to optimize the role of EPT in green innovation.

Evaluation of Bayesian spatiotemporal infectious disease models for prospective surveillance analysis

BackgroundCOVID-19 brought enormous challenges to public health surveillance and underscored the importance of developing and maintaining robust systems for accurate surveillance. As public health data collection efforts expand, there is a critical need for infectious disease modeling researchers to continue to develop prospective surveillance metrics and statistical models to accommodate the modeling of large disease counts and variability. This paper evaluated different likelihoods for the disease count model and various spatiotemporal mean models for prospective surveillance.MethodsWe evaluated Bayesian spatiotemporal models, which are the foundation for model-based infectious disease surveillance metrics. Bayesian spatiotemporal mean models based on the Poisson and the negative binomial likelihoods were evaluated with the different lengths of past data usage. We compared their goodness of fit and short-term prediction performance with both simulated epidemic data and real data from the COVID-19 pandemic.ResultsThe simulation results show that the negative binomial likelihood-based models show better goodness of fit results than Poisson likelihood-based models as deemed by smaller deviance information criteria (DIC) values. However, Poisson models yield smaller mean square error (MSE) and mean absolute one-step prediction error (MAOSPE) results when we use a shorter length of the past data such as 7 and 3 time periods. Real COVID-19 data analysis of New Jersey and South Carolina shows similar results for the goodness of fit and short-term prediction results. Negative binomial-based mean models showed better performance when we used the past data of 52 time periods. Poisson-based mean models showed comparable goodness of fit performance and smaller MSE and MAOSPE results when we used the past data of 7 and 3 time periods.ConclusionWe evaluate these models and provide future infectious disease outbreak modeling guidelines for Bayesian spatiotemporal analysis. Our choice of the likelihood and spatiotemporal mean models was influenced by both historical data length and variability. With a longer length of past data usage and more over-dispersed data, the negative binomial likelihood shows a better model fit than the Poisson likelihood. However, as we use a shorter length of the past data for our surveillance analysis, the difference between the Poisson and the negative binomial models becomes smaller. In this case, the Poisson likelihood shows robust posterior mean estimate and short-term prediction results.

Influence of train speed and its mitigation measures in the short- and long-term performance of a ballastless transition zone

AbstractThe ballastless track is nowadays the most popular railway system due to the required low number of maintenance operations and costs, despite the high investment. The gradual change from ballasted to ballastless tracks has been occurring in Asia, but also in Europe, increasing the number of transition zones. The transition zones are a special area of the railway networks where there is an accelerated process of track degradation, which is a major concern of the railway infrastructure managers. Thus, the accurate prediction of the short- and long-term performance of ballastless tracks in transition zones is an important topic in the current paradigm of building/rehabilitating high-speed lines. This work purposes the development of an advanced 3D model to study the global performance of a ballastless track in an embankment–tunnel transition zone considering the influence of the train speed (220, 360, 500, and 600 km/h). Moreover, a mitigation measure is also adopted to reduce the stress and displacements levels of the track in the transition. A resilient mat placed in the tunnel and embankment aims to soften the transition. The behaviour of the track with the resilient mat is evaluated considering the influence of the train speed, with special attention regarding the critical speed. The used methodology is a novel and hybrid approach that allows including short-term and long-term performance, through the development of a powerful 3D model combined with the implementation of a calibrated empirical permanent deformation model.

A New Combined Prediction Model for Ultra-Short-Term Wind Power Based on Variational Mode Decomposition and Gradient Boosting Regression Tree

Wind power is an essential component of renewable energy. It enables the conservation of conventional energy sources such as coal and oil while reducing greenhouse gas emissions. To address the stochastic and intermittent nature of ultra-short-term wind power, a combined prediction model based on variational mode decomposition (VMD) and gradient boosting regression tree (GBRT) is proposed. Firstly, VMD is utilized to decompose the original wind power signal into three meaningful components: the long-term component, the short-term component, and the randomness component. Secondly, based on the characteristics of these three components, a support vector machine (SVM) is selected to predict the long-term and short-term components, while gated recurrent unit-long short-term memory (GRU-LSTM) is employed to predict the randomness component. Particle swarm optimization (PSO) is utilized to optimize the structural parameters of the SVM and GRU-LSTM combination for enhanced prediction accuracy. Additionally, a GBRT model is employed to predict the residuals. Finally, the rolling predicted values of the three components and residuals are aggregated. A deep learning framework using TensorFlow 2.0 has been built on the Python platform, and a dataset measured from a wind farm has been utilized for learning and prediction. The comparative analysis reveals that the proposed model exhibits superior short-term wind power prediction performance, with a mean squared error, mean absolute error, and coefficient of determination of 0.0244, 0.1185, and 0.9821, respectively.

A holistic techno-economic evaluation framework for sizing renewable power plant in a hydro-based hybrid generation system

The configuration of the hybrid generation systems (HGSs) is critical for maximizing the synergy between different power sources. However, accurately simulating the operation of HGSs over their lifespan is challenging due to the need to balance multiple objectives covering different timeframes. For example, long-term water management and short-term peak-shaving can conflict. To tackle this problem, we propose a comprehensive evaluation framework that optimizes the size of a renewable power plant integrated with an existing hydropower station. This framework combines a long-term and short-term operation model with a techno-economic analysis. Unlike traditional models, our short-term operation model prioritizes power supply quality and quantity while maintaining satisfactory peak-shaving performance for the HGS. We generated numerous daily generation plans using this model to derive the multidimensional response functions, which describe the relationship between long-term hydropower decision, renewable power output and short-term performance indicators. These response functions were then incorporated into a refined long-term operation model to simulate the HGS's extended operation. Finally, we employed a holistic evaluation framework comprising various techno-economic indicators to determine the cost-effective size of the renewable power plant. We applied this framework to the Igong Zangpo River Basin in China, a tributary of the Yarlung Zangbo River. The results revealed the following: (1) Increasing the size of the photovoltaic (PV) component resulted in a decline in technical performance, including energy use efficiency and peak-shaving capabilities; (2) Given sufficient transmission capacity, a PV size threshold of 2400 MW was identified, while the optimal PV size with the highest net present value was 1950 MW, twice the capacity of the hydropower installation (1030 MW); and (3) The optimal PV size varied from 0 to 2400 MW based on electricity price and initial investment factors. Thus, our proposed techno-economic framework effectively guides the capacity configuration of the hydro-based HGSs.

A mosquito mouthpart-like bionic neural probe

Advancements in microscale electrode technology have revolutionized the field of neuroscience and clinical applications by offering high temporal and spatial resolution of recording and stimulation. Flexible neural probes, with their mechanical compliance to brain tissue, have been shown to be superior to rigid devices in terms of stability and longevity in chronic recordings. Shuttle devices are commonly used to assist flexible probe implantation; however, the protective membrane of the brain still makes penetration difficult. Hidden damage to brain vessels during implantation is a significant risk. Inspired by the anatomy of the mosquito mouthparts, we present a biomimetic neuroprobe system that integrates high-sensitivity sensors with a high-fidelity multichannel flexible electrode array. This customizable system achieves distributed and minimally invasive implantation across brain regions. Most importantly, the system’s nonvisual monitoring capability provides an early warning detection for intracranial soft tissues, such as vessels, reducing the potential for injury during implantation. The neural probe system demonstrates exceptional sensitivity and adaptability to environmental stimuli, as well as outstanding performance in postoperative and chronic recordings. These findings suggest that our biomimetic neural-probe device offers promising potential for future applications in neuroscience and brain-machine interfaces.A mosquito mouthpart-like bionic neural probe consisting of a highly sensitive tactile sensor module, a flexible microelectrode array, and implanted modules that mimic the structure of mosquito mouthparts. The system enables distributed implantation of electrode arrays across multiple brain regions while making the implantation minimally invasive and avoiding additional dural removal. The tactile sensor array can monitor the implantation process to achieve early warning of vascular damage. The excellent postoperative short-term recording performance and long-term neural activity tracking ability demonstrate that the system is a promising tool in the field of brain-computer interfaces.

The Effects of Statins on Cognitive Performance Are Mediated by Low-Density Lipoprotein, C-Reactive Protein, and Blood Glucose Concentrations.

Statins are widely used for cardiovascular disease prevention but their effects on cognition remain unclear. Statins reduce cholesterol concentration and have been suggested to provide both beneficial and detrimental effects. Our aim was to investigate the cross-sectional and longitudinal association between statin use and cognitive performance, and whether blood low-density lipoprotein, high-density lipoprotein, triglycerides, glucose, C-reactive protein, and vitamin D biomarkers mediated this association. We used participants from the UK biobank aged 40-69 without neurological and psychiatric disorders (n = 147 502 and n = 24 355, respectively). We performed linear regression to evaluate the association between statin use and cognitive performance and, mediation analysis to quantify the total, direct, indirect effects and the proportion meditated by blood biomarkers. Statin use was associated with lower cognitive performance at baseline (β = -0.40 [-0.53, -0.28], p = <.0001), and this association was mediated by low-density lipoprotein (proportion mediated = 51.4%, p = .002), C-reactive protein (proportion mediated = -11%, p = .006) and blood glucose (proportion mediated = 2.6%, p = .018) concentrations. However, statin use was not associated with cognitive performance, measured 8 years later (β = -0.003 [-0.11, 0.10], p = .96). Our findings suggest that statins are associated with lower short-term cognitive performance by lowering low-density lipoprotein and raising blood glucose concentrations, and better performance by lowering C-reactive protein concentrations. In contrast, statins have no effect on long-term cognition and remain beneficial in reducing cardiovascular risk factors.

Environmental governance, executive incentive, and enterprise performance: Evidence from Chinese mineral enterprises

The goal of China's development by 2035 is the widespread formation of green production and lifestyle. Enterprise is the most crucial micro subject in realizing a fundamental improvement in the ecological environment. However, corporate environmental governance has typical externalities regarding corporate profitability, especially for mining companies. This paper explores the relationship between enterprise environmental governance and enterprise performance from multi-dimensions based on the manually-collected microdata of 194 listed enterprises in the mining industry in Shanghai and Shenzhen A-shares from 2010 to 2016 (the corporate environmental data is provided up to 2016). This study's findings are as follows. (1) Enterprise environmental governance facilitates short-term enterprise performance, which verifies the “Porter hypothesis.” (2) The five sub-dimensions of enterprise environmental governance have an “enhancing effect” on short-term enterprise performance and a “decreasing effect” on long-term enterprise performance. (3) Moderating effect analysis shows that “executive compensation incentive” leads enterprise environmental governance to improve long- and short-term enterprise performances. In contrast, “equity incentive” inhibits its effect on short-term enterprise performance. (4) Environmental governance in state-owned enterprises significantly contributes to short-term performance. In non-state-owned enterprises, it significantly reduces long-term enterprise performance. Finally, corresponding policy implications and future research directions were recommended. Future initiatives must strengthen incentives for green innovation, accelerate the substitution of new energy sources, promote the green and digital transformation of mining enterprises, and improve carbon finance in the carbon market.