STOCK PREDICTION MODEL (python)

The spread of the COVID-19 virus has globally dominated news cycles in 2020, and has had

prominent impact on the international financial markets. Officially declared a global pandemic

by the World Health Organization (WHO) in March 2020, significant uptick and downturns in

the financial market can be correlated to public sentiment towards future outlook on the

containment of the pandemic. The goal of this paper is to analyze the effects of existing financial

information and news data on the predictability of future stock prices during the global

COVID-19 pandemic. Financial parameters of stocks such as daily closing price, high price, low

price and market volume are considered along with sentiment scores for daily news headlines

related to stocks and COVID-19. To evaluate the accuracy of the prediction model, three distinct

prediction models are designed using a neural network architecture with different combinations

of input parameters. The broader goal for this project is to develop and establish a financial

market prediction model that can be utilized during future global pandemics to minimize the loss

of financial capital.

The program is written in Python.

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