Analysis of Financial Fraud Statements’ Effect on Stock Prices Using Regression Techniques

Using a thorough study and cutting-edge text mining algorithms, this research explores the complex link between fraudulent financial statements and their impact on stock prices, revealing the intricate market dynamics affected by false disclosures. Additionally, fraudulent financial reporting can negatively impact stockholders' investments by eroding trust, leading to decreased stock value and potential financial losses. The goal of the project is to clarify how financial fraud notifications affect the stock market by gaining valuable insights from textual data. Using machine learning algorithms, the process sorts through enormous databases of financial records and press releases to investigate the relationship between investor responses, market mood, and the ensuing regulatory authorities' remedial actions. By analyzing the impact of investor sentiment, market mood, and regulatory actions on stock prices, this research aims to showcase an elaborative understanding of the complex link between fraudulent financial statements and stock market dynamics. The goal of the study is to present a detailed picture of how the market reacts to financial crime, providing regulatory bodies, investors, and policymakers with insightful information. By delving into the root causes and dynamics triggered by false disclosures, this research contributes to the rapidly developing discipline of financial market analysis.