Abstract:

In today's fast-paced and highly competitive financial markets, investors, analysts, and financial professionals seek reliable tools and methodologies to make well-informed decisions. This research study addresses this need by exploring the integration of two powerful techniques: stock price prediction and textual analysis of financial statements. The first aspect of the study revolves around stock price prediction techniques. To predict future price trends for listed companies, the analysis leverages historical stock data, including price movements, trading volumes, and other relevant market indicators. Various predictive models are employed, such as machine learning algorithms, time-series analysis, and statistical methods, to identify patterns and relationships within the historical data. By processing these patterns, the models attempt to forecast future price movements and potential market trends. The second aspect of the study involves textual analysis using natural language processing (NLP) techniques. Financial statements, annual reports, and other textual sources are collected for the listed companies under investigation. NLP algorithms are applied to process and analyse this textual data to extract valuable insights, trends, and sentiments that may impact the market behaviour. This includes identifying positive or negative sentiment around financial performance, strategic initiatives, risk factors, and other significant events. By combining stock price prediction and textual analysis, this research aims to offer a comprehensive understanding of market dynamics. The predictive models provide a forwardlooking view of potential price movements, while the textual analysis offers qualitative insights into the factors driving those movements. The integration of these methodologies can help uncover hidden opportunities, risks, and market sentiments that may not be immediately apparent through traditional numerical analysis alone. The intended beneficiaries of this research include investors looking to optimize their portfolio allocations, analysts seeking to enhance their research capabilities, and financial professionals involved in decision-making processes. By providing valuable market insights, this study enables these stakeholders to make more informed choices, adjust their strategies, and seize potential opportunities in the dynamic and ever-changing financial landscape. Overall, the goal of this research is to empower market participants with a data-driven and holistic approach to understanding market dynamics. By bridging the gap between quantitative analysis and qualitative insights, the study aims to contribute to the advancement of financial analysis and decisionmaking, ultimately leading to better-informed and more successful investment and trading strategy

Description:

Machine learning, Natural Language Processing (NLP).