Stock Market Reactions on Returns and Trading Volume: The Impact of the Global Financial Crisis

Objective: This study empirically examines the short term under- and overreaction effect in the Karachi Stock Exchange, Pakistan, in the context of the 2008 Global Financial Crisis considering the period from September 2007 to 2009.Background: Investors’ probable reaction to an anticipated or unforeseen event is gaining immense importance in order to understand the complex market behavior. The arrival of good or bad news can tend to bring about a rise or decline in the stock price even if the news does not directly impact company’s performance.Method: The sample data for the stock price, trading volume and KSE 100 index are obtained from the Karachi Stock Exchange (KSE) and Securities and Exchange Commission of Pakistan (SECP) websites for the period September 2007 to 2009. To reach our objective, we used event studies.Results: There is evidence of significant overreaction in the first two weeks and significant under- reaction in the 12th and 24th week following specifically in the financial sector. For the non-financial sector, the returns stay positive and insignificant for both the winner and loser portfolios thereby negating any evidence of significant overreaction.Contributions: We wants to contribute to the existing literature, testing the under- and overreaction hypothesis in an emerging market. Our study also attempts to draw attention to any evidence of returns reversal in the loser and winner portfolios based on the trading volume. Investors may capitalize on the trading volume information to earn contrarian profits

A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron

The Coronavirus (COVID-19) outbreak in December 2019 has become an ongoing threat to humans worldwide, creating a health crisis that infected millions of lives, as well as devastating the global economy. Deep learning (DL) techniques have proved helpful in analysis and delineation of infectious regions in radiological images in a timely manner. This paper makes an in-depth survey of DL techniques and draws a taxonomy based on diagnostic strategies and learning approaches. DL techniques are systematically categorized into classification, segmentation, and multi-stage approaches for COVID-19 diagnosis at image and region level analysis. Each category includes pre-trained and custom-made Convolutional Neural Network architectures for detecting COVID-19 infection in radiographic imaging modalities; X-Ray, and Computer Tomography (CT). Furthermore, a discussion is made on challenges in developing diagnostic techniques such as cross-platform interoperability and examining imaging modality. Similarly, a review of the various methodologies and performance measures used in these techniques is also presented. This survey provides an insight into the promising areas of research in DL for analyzing radiographic images, and further accelerates the research in designing customized DL based diagnostic tools for effectively dealing with new variants of COVID-19 and emerging challenges.Comment: Pages: 44, Figures: 7, Tables: 1