DEVELOPING AN INVESTMENT DECISION FRAMEWORK USING HISTORICAL STOCK MARKET DATA: A CASE STUDY OF ASIAN PAINTS LTD

Abstract

Investment decisions in the stock market require a systematic evaluation of both a company's financial performance and its historical market behaviour. Many investors rely on market sentiment or short-term price movements, which may lead to inconsistent investment outcomes. This study aims to develop an Investment Decision Framework by integrating fundamental analysis and technical analysis using Asian Paints Ltd. as the case study. The research is based on 23 years of historical stock price data (July 2002–June 2023) obtained from Yahoo Finance and 10 years of financial statement data (FY2015–FY2024) collected from publicly available sources such as the company's annual reports and Screener.in. Fundamental indicators including Revenue Growth, Earnings Per Share (EPS), Price-to-Earnings (P/E) Ratio, and Return on Equity (ROE) were analyzed to assess the company's financial strength, profitability, and market valuation. Technical indicators such as Compound Annual Growth Rate (CAGR), Daily Return, Simple Moving Average (SMA-50), Annualized Volatility, Maximum Drawdown, and Average Trading Volume were calculated to evaluate long-term stock performance, market trends, and investment risk. The findings indicate that Asian Paints has consistently demonstrated strong financial performance, sustained profitability, efficient utilization of shareholders' equity, and significant long-term wealth creation despite periods of market volatility. The study concludes that combining fundamental and technical indicators provides a more comprehensive and objective approach to investment decision-making than relying on a single method. The proposed investment framework can assist investors, financial analysts, researchers, and students in evaluating investment opportunities using historical data and financial metrics. It also highlights the growing importance of data analytics and analytical tools such as Python, Microsoft Excel, and Power BI in supporting evidence-based financial decision-making.