Stock price forecasting remains a prominent area of research in financial markets due to the complicated, nonlinear, and volatile nature of stock price paths. While high-performance deep learning models like Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) demonstrate superiority in financial forecasting, their comparative efficacy in emerging markets, such as the KSE-100 Index, remains inadequately explored. This study examines the prediction performance of these models based on past stock prices of the KSE-100 Index and based on standard performance measures (root mean square error, mean absolute error, and mean absolute percentage error) to provide a holistic assessment of the models. Our research indicates that GRU consistently performs better than CNN and LSTM, with lower errors and higher predictive accuracy. This work enhances current forecasting methods by utilizing advanced data preparation, feature engineering, and hyperparameter optimization to augment model performance. The results have practical implications for policymakers and investors in emerging markets by emphasizing the applicability of deep learning to financial decision-making.
In the technique of paired comparisons, objects are ranked since individual judgment. We use it when quantifiable measurement is not viable or impractical. In this study, the Glenn-David PC model under Bayesian framework is used to establish the rating of five brands of cold drinks. Bayesian analysis has been made using non-informative and informative priors. The posterior means are considered for the preference behavior of the cold drink brands. The predictive probabilities for a single future paired comparison of cold drink brands are also found. The posterior probabilities of the hypotheses for comparison of parameters for any two cold drink brands are obtained. Also, the preference probabilities for paired comparison are determined. The results obtained through Uniform and Normal-Gamma priors are compared. It is observed that similar results and same rankings for the cold drinks brands are achieved. The appropriateness of the model is tested by chi-squared statistic. All computations are made in SAS package by designing the programs/codes.
This study attempts to examine the pass-through impact of exchange rates on consumer prices in the specific context of Pakistan. To completely assess this association, a thorough configuration of five variables was carefully picked. The dataset covers financial patterns spanning more than twenty years, from January 2000 to December 2021. With the utilization of multivariate analysis and a Vector Auto Regression (VAR) model, the analysis presents convincing fragments of the data. The data indicates that fluctuations in the exchange rate have a significant impact on the Consumer Price Index (CPI). The Impulse Response (IR) analysis incorporates this association, mirroring a positive relationship between the two variables. In like manner, Granger causality analysis reveals understanding into the sharp control of the CPI in wrapping up exchange rate shifts. Unendingly out, these revelations feature the squeezing position of the exchange rate in making feeling of expansion parts inside Pakistan's monetary scene.
Repeated measurements design (RMDs) is economical, therefore, often used in several areas like, psychology, medicine, animal sciences, and pharmacology. In RMDs carry over effects arise which become the source of bias to estimate the treatment effects. Minimal strongly balanced RMDs and Generalized strongly balanced RMDs are used to control the carry over effects and to estimate the direct effects and carry over effects. Catalogues of the designs are always useful for the experimenters and practitioners because these provide them the readymade solution. Catalogue of efficient minimal circular generalized strongly balanced RMDs for v = ip1+2p2-2, i odd, p1 odd and p2 integer is not available in the literature. In this article, a catalogue of these efficient designs is presented for v ≤ 99, 5 ≤ p1 (odd) ≤ 11, 3 ≤ p2 ≤10.
Survival data from clinical trials often show the proportion of patients with long-term survivors and the standard models like Cox and accelerated failure time model are inappropriate for fitting such data. The two-component mixture cure model is often used for this purpose. However, for the failure time data with proportional hazards structure, the promotion time cure model can be more adaptable than the mixture cure model. The present paper compares semiparametric method with parametric Bayesian and maximum likelihood methods for estimating the proportion of insusceptible patients using log link function and for modeling the failure times of susceptible subjects using Burr-XII distribution. For Bayesian estimation, we use improper uniform prior distributions for regression parameters and vague gamma prior distributions for baseline distribution parameters. Numerical experiments are considered to examine the performance of different methods. It is observed that for small sample sizes, the Bayes method perform better than the parametric and semiparametric methods in terms of biases, mean square errors and empirical variances and for large sample sizes, the performance of the Bayes and maximum likelihood methods is approximately equal. The proposed methods are applied to real data for illustration and motivation.
One of the ten countries in the world most quickly affected by climate change is Pakistan. Temperature variability, irregular monsoon rain patterns, and other climatic constraints are becoming major threats to agricultural productivity, particularly cotton. This review study aims to highlight the importance of seed cotton sowing time and yield improvement. Four databases (Google Scholar, Springer, Elsevier, and MDPI) were used for this review up to 2020 for the identification of relevant original published and peer-reviewed studies about the impact of seed cotton sowing time on yield with the keywords "seed cotton sowing time" and "optimum seed cotton sowing time" in combination with yield. The final analysis includes data from five surveys. The sowing time of seed cotton varies in different ecological zones of Pakistan and has been found to be an important factor in increasing the yield. Furthermore, the study also highlights that sowing time and cultivars of seed cotton should be prioritized for better yield.
The typical linear regression model does this to have some sort of heteroscedasticity in the error terms and linear correlation in the regressors. The ordinary least squares estimates are significantly impacted by each of these issues. When these assumptions violated in any multiple linear regression model then ordinary least square estimator happen to unstable and no longer remain best linear unbiased estimator. Therefore, in attempt to tackle the issue of Multicollinearity the rigid, Liu and (k-d) regression exist and easily accessible in literature. The adaptive estimator was recommended to obtain an efficient estimator in comparison to the conventional least square estimator to address the problem of heteroscedasticity. This current work suggests the improved method of adaptation for (kd) class estimator to get more efficient results when dealing with multicollinearity and heteroscedasticity occur at same time. All the numerical work is done by using simulation scheme Monte Carlo, with different degrees of collinearity, severity (existence) of heteroscedasticity, and sample size to assess the performance of the suggested estimator. The simulation results provide best performance of adaptive (k-d) class estimator which is our proposed estimator.
Throughout history, people have been intrigued by what lies ahead. The purpose of this study is to develop an ARIMA model that can predict Pakistan's annual sugar production between the years 1974 and 2021, as well as provide forecasts for upcoming years. The ARIMA (1, 1, 1) model was found to be the best fit based on the minimum value of the Bayesian Information Criterion (BIC). According to the 95% confidence interval sugar forecast for Pakistan from 2021 to 2030, the anticipated sugar production in 2030 is 6881 million tons. Additionally, this study uncovered a rising trend in sugar production in Pakistan.
Cereal production, cooperation and economic growth need to be distinct to generate policies that keep the environment make sure sustainable agricultural development around the world. Panel data comprise of the observations of manifold phenomena obtained over lots of time periods for either the same firms or individuals. The current study was conducted with an aim to find the efficiency growth in agricultural land and also find cereal production best model for South Asian Association for Regional Cooperation countries. The study used the secondary panel data of cereal production obtained from the World Bank. Moreover, for the selection of variables, pool-ability, Fixed Effect Model (FEM) and Random Effect Model (REM) were applied by F-test, Chow Test, Hausamn specification test and model selection criteria. Through analysis of present data, we proposed a FEM that can assume both the cross section and period effects to be fixed. It can forecast the cereal production for any given country contained by a specific forthcoming year. Governments in these countries must take the necessary steps to maintain agricultural land and encourage farmers to increase arable land in order to satisfy the food demands of SAARC's rising population.
Among the most commonly used distributions for analysing lifetime data are exponential distributions, Rayleigh distributions, linear failure rate distributions, and Weibull distributions. There are several desirable properties and pleasant physical interpretation properties to these distributions. This paper introduces a new more flexible model for lifetime data and named as Generalized Reverse Exponential Transformed Weibull (GRETW) Distribution. We have discussed the properties of the GRETW. The order statistics of the proposed distribution have also been studied. Moreover, maximum likelihood estimator of unknown parameters has been obtained. Different data sets are analysed and observe that in comparison to other distributions, this distribution can provide a better fit.
In India, smartphone sales have increased in recent years, and Kerala, a South Indian state that accounts for 2.76% of the Indian population according to the 2011 Census of India1 , has seen an increase in smartphone use since the pandemic-induced lockdowns and quarantines. The rampant use of smartphone technology is believed to have a substantial impact on interactions among individuals. The current study aims to analyse the impact of smartphone use among various categories of people in Kerala, India, on their social interactions and the formation of social attitudes using primary data. The focus is on the time duration put aside for face-to-face interactions and for smartphone communications, and the results show that there is no drastic reduction in face-to-face interactions among Keralites due to phone use, despite evidence for widespread ownership and use of smartphones among all categories of individuals, especially for communication purposes. Strong associations among smartphone use, age, and employment status are also observed. Smartphone use is most prevalent among students and workers belonging to the age groups 15-29 years and 45-59 years, and no clear gender divide is visible
The aim of this study was to explore the burgeoning maternal risk predictors for selecting the mode of delivery (MOD) among the women of Punjab, Pakistan. This research is capable to contribute to the research field and provide an important foundation for researchers learning about factors contributing to childbirth delivery decisions in Pakistan. Over the course of the study period, the conscription of a sample of 399 expectant women was done. Chi-square analysis was used for comparison of baseline characteristics, delivery outcomes and mode of delivery. To evaluate predictive factors, logistic regression analysis was used. The 95% confidence interval and odds ratios were also calculated. Overall, 61.4 percent of women had caesareans, while 38.6 percent delivered vaginally. In the domain of organisational and biological factors, the findings of the binary logistic regression show that the odds of caesarean section (CS) were significantly higher among women who preferred a private hospital for delivery, were advised by their doctor to have CS, were too lazy to walk during pregnancy, suffered from Meconium Aspiration Syndrome, foetal distress, maternal anaemia, and had an abnormal baby presentation. The indicators of organisational predictors (hospital level and physician's recommendation) were the most important drivers in predicting MOD among women.
Based on the evolutionary game theory,the paper used the system dynamics simulation method and the China Super League to depict the evolution mechanism of competitive equilibrium in Chinese professional leagues.Then the paper adopted the methods of mathematical statistics,data envelopment analysis and econometrics,to analyze the effect of the competitive balance degree of professional leagues on performance.The results showed as follows.Firstly,the Chinese Super League has evolved into the league at the non-cooperative stage.Secondly,the competitive balance status of the Chinese Super League has deteriorated,and the overall efficiency of the league was at a lower level.Thirdly,the influence channel of the competitive balance level of professional leagues on performance was to affect the form and transition process of the system.Finally,the reduction of the competitive balance degree of the Chinese Super League might lead to a drop in performance level,and this effect was persistent and hysteretic.In order to optimize the competitive balance mechanism and improve the performance of professional leagues,represented by the Chinese Super League,the paper focused on the clarification of the core value orientation,the construction of the endogenous dynamic mechanism of participants'co-evolution,and the promotion of the exogenous government regulation appropriately intervened by the authorities.
As an important measure to coordinate urban-rural development and alleviate the urban-rural income gap,the policy effect of new-type urbanization construction remains to be tested.On the basis of theoretical analysis,the paper adopted the panel data of prefecture-level cities in China from 2011 to 2021,and set the pilot policy of new-type urbanization as a quasi-natural experiment.Then the paper conducted an empirical test by using the multi-phrase difference-in differences model.The results were as follows.The construction of new-type urbanization significantly narrowed the urban-rural income gap,and this conclusion still held after a series of robustness tests.Based on the regional perspective,the policy effect was more significant in underdeveloped regions and the regions having a larger urban-rural income gap,while it didn't exert an evident effect in the northeast region.Mechanism analysis revealed that,the influential mechanisms included promoting digital inclusive finance,accelerating the development of the circulation industry and improving entrepreneurial activity.Furthermore,the paper proposed some countermeasures and suggestions about the paths to accelerate the construction of new-type urbanization and promote the integrated urban-rural development.
Based on the national input-output tables in 2002,2007,2012,2017 and 2020,the paper extracted 15 core marine industries,and constructed the input-output table of maritime economy.Then the research measured the development scale of marine industry and its contribution to national economy,and then revealed the linkage relationship and dynamic changing trend between marine industries and land-based industries.The results showed that,the upward development of China's marine economy has kept the fundamentals,and the industrial scale in 2020 has increased nearly five times than that in 2002.Meanwhile,China's marine economy has formed a relatively stable industrial development pattern,and the marine tourism industry has developed as the leading one.The basic dependency structure of marine industries on land-based industries showed the pattern of"secondary industry>third industry>primary industry",and the linkage between marine industry and land-based third industry had a gradually deepening tendency.
This paper used the entropy power method to construct the comprehensive index of the sustainable development of people's livelihoods,and then monitored the sustainable development level of people's livelihoods in different regions of China.Meanwhile,the paper adopted the two-way fixed-effects model to study the impact and its internal mechanism of digital inclusive finance on the sustainable development of people's livelihoods.The results showed that:the sustainable development level of people's livelihoods in different regions showed a relatively larger gap,and the gap had a widening trend.The influential mechanisms of digital inclusive finance included promoting fair employment and improving innovation level,which were the key initiatives to realize"data governance".
This paper took the implementation of"the Belt and Road"initiative as a natural experiment,and constructed the difference-in-differences model to assess the impact and action mechanism of"the Belt and Road"initiative on corporate investment risk based on the financial data of A-share listed companies from 2012 to 2018.The results showed that,"the Belt and Road"initiative significantly reduced the investment risk of enterprises.And the conclusion was still valid after a series of robustness tests,like adopting the propensity score matching method and controlling geographical characteristics.This impact of"the Belt and Road"initiative had corporate heterogeneity,which was more evident in small-scale enterprises,the enterprises with poor internal governance,and the enterprises having the board with less overseas experiences.Meanwhile,this impact also had industrial heterogeneity,which was more evident in high-tech industries,capital-intensive industries,and non-labor-intensive industries.Besides,the reduction effect of"the Belt and Road"initiative has been supported by government communication,financing and smooth operation section.
Based on the provincial panel data from 2005 to 2021,the paper evaluated the coupling and coordinated development status of the"security-economy-green"system in China's energy sector,and used the dynamic QCA method to explore the configuration effect of the factors of"technology-organization-environment"on the longitudinal time axis,on energy's coupling and coordinated development of"security-economy-green".The research showed that,there was the energy"trilemma"in China,and any single factor couldn't drive the coupling and coordinated development of the"security-economy-green"system in China's energy sector.Instead,the collaboration of multiple factors could play a driving role.There were three types of configuration models to realize the coupling and coordinated development,that was,the"technology-organization-environment collaboratively driven"model,the"organization-environment driven"model and the"technology-environment driven"model.
在对农民生活富裕的现代化内涵进行深入探讨的基础上,分析农业科技创新对农民生活富裕的内部与外部影响机制,并基于我国省级数据在考虑内生性后实证检验该机制及其异质性.结果表明:通过内部诱导机制,农业科技创新可促进农民增收,改变农民消费结构;通过外部支撑机制,农业科技创新可促进农村生产、生活与生态环境改善;内、外部机制的促进作用受地区发展程度和财政涉农投入的影响而具有区域异质性.
从中国式现代化的本质要求和目标原则出发,初步构建中国式现代化指标体系.结合数据对2012-2021年中国式现代化发展状况进行测度.以现代化的共同特征和中国特色二维向度作为指标划分的基础,突出评价中国式现代化区别于一般现代化的特征.研究显示,中国式现代化总指数呈不断上升趋势;共同特征指数增长较快,中国特色指数则增长趋缓.未来,中国式现代化建设需继续强化党的领导,确保宏观政策的连续性和稳定性,提高自主创新能力和技术扩散效率,增加居民的要素收入和财产性收入,坚定不移推进高水平对外开放,推动构建和谐世界.
