| dc.description.abstract | Stock price forecasting is challenging as factors
like economic shifts, political changes, and investor
behavior influence it. Although numerous studies have
explored sentiment analysis and predictive modelling, a
critical gap remains in understanding how different sectors
react uniquely to financial news. This study takes an
innovative approach by focusing on the Colombo Stock
Exchange (CSE), characterized by unpredictable market
movements. We intend to identify the five most sentiment
sensitive industries within the CSE from a broader set of 20
sectors. We will use this knowledge to develop a forecasting
model that captures industry-specific responses to
sentiment by analyzing daily stock prices alongside
sentiment data extracted from approximately 100,000
financial news articles from 2016 to 2023. We aim to
develop an adaptable forecasting model that enhances
prediction accuracy, offering actionable insights for
investors, particularly in the volatile CSE market. Our
approach addresses industries' unique sensitivity to
sentiment and provides a more nuanced understanding of
market dynamics. The result will be a robust, industry
focused forecasting tool that better equips investors to
navigate the complexities of the CSE, ultimately leading to
more informed decision-making in volatile market
conditions. | en_US |
