Abstract
The rapidly increasing scientific research on the stock market and the visible impact of media on equity prices are nowadays in limelight. To a greater extent, causal analysis can reckon the sentimental effect of the broadcasted content on stock valuation. We propose a four stage model to detect the direction of information flow between the news sentiment and stock price. Whilst web scraping explores and extracts the news datasets, the modified VADER algorithm finds the sentiments of the aired media. The associational causal analysis determines the cause effect between the news and stock price. The results suggest that the non parametric Shannon and Renyi's entropy approach supersedes the Granger test, a parametric study which is constrained to Gaussian time series with linear causation. Since Renyi's Entropy can perfectly identify the deluge of information during quick leaps, it is regarded as a beneficial formulation for investors when evaluating stocks with a fewer number of news mentions. The impact of news during the COVID-19 pandemic over the pharmaceutical sector was also done. The study infers an explicit information flow and direction of causality between news sentiment and stock price movement, which can be used to devise future investment and consumption strategies.