A STUDY OF FINANCIAL PERFORMANCE OF SOLAR INDUSTRIES LIMITES

Abstract

The increasing relevance of renewable energy companies like Solar Industries Limited necessitates advanced tools for evaluating their financial performance. Traditional financial analysis methods, while useful, often fall short in uncovering complex patterns in large financial datasets. In this study, we incorporate Machine Learning (ML) and Deep Learning (DL) techniques to assess the financial health of Solar Industries Limited over a defined period. Financial ratios such as profitability, liquidity, solvency, and efficiency are first analyzed using conventional techniques and then modeled using algorithms such as Random Forest, Support Vector Machines (SVM), and Deep Neural Networks (DNN). These models provide insights into hidden patterns, anomalies, and predictive financial trends that can support better investment and policy decisions.By leveraging historical financial data, the ML models enable predictive forecasting, while DL models, especially Long Short-Term Memory (LSTM) networks, capture sequential financial behavior to forecast future performance with higher accuracy. The study reveals that AI-based models not only enhance prediction precision but also support deeper interpretability of financial risk and growth opportunities. Overall, this dual-layered approach combining traditional and intelligent methods creates a comprehensive framework for financial performance evaluation, offering immense value to investors, stakeholders, and strategic planners
in the clean energy domain