Search

During COVID-19, marketing shows sharp fluctuation in upward and downward trends. Forecasting price actions is one of the most challenging problems in this situation. It is challenging to build an accurate model, which integrates economic and Covid-19 variables as input for KSE index prediction. To tackle this problem, our proposal comprises applying machine learning (ML) techniques to predict the KSE during Covid-19. The principal aim of this study is to examine accuracy of combined models with individual models to forecast the Karachi Stock Exchange during COVID-19. This study has analyzed the indices of KSE from March 1st, 2020, to November 26th, 2021. Therefore, this study is keen to find the best-fitted model that forecasts more accurately during the pandemic. To select the most suitable machine learning technique, the six inferred models (i.e., Linear regression (LR), Artificial Neural Network (ANN), Regression Tree (RT), Random Forests (RF), (KNN), and Support Vector Regression (SVR)) are selected to forecast the Karachi Stock Exchange During Covid-19. Performance metrics (i.e., MAE, MSE, MAPE, and R2) are applied to measure and compare accuracy. The modeling outputs presented the RF model provided the best performance of 0.98 versus the other models in predicting the KSE100 index. Thus, the addition of ML methods improves the exchange indications and the competitiveness of future trading guidelines. These projections helped the government to make strategies for the stock exchange KSE-100 and fight against a pandemic disease. The results suggest that the performance of the KSE-100 index can be predicted with machine-learning techniques.

The grey wolf Canis lupus lupus is Critically Endangered in Nepal, and is a protected species there. Understanding the species’ status and distribution is critical for its conservation in the Nepalese Himalaya. We assessed the distribution of the grey wolf in the Himalayan and Trans-Himalayan regions using data from faecal and camera trap surveys and published data sources. We recorded 40 instances of wolf presence. Using these data we estimated a distribution of 28,553 km2, which includes potential as well as known habitat and comprises 73% of the Nepalese Himalaya. There is evidence of recovery of the grey wolf population in Kanchenjunga Conservation Area in the eastern portion of the species’ range. A livestock insurance scheme has been shown to be a viable option to reduce retaliatory killing of wolves as a result of livestock depredation. The wolf plays an important ecological role in the Himalaya, and its conservation should not be delayed by the ongoing taxonomic debate about its subspecific status.