Article
FINANCIAL MODELING FOR INVESTMENT BANKING
Financial modeling forms the backbone of strategic decision-making in the investment banking sector. Traditionally based on spreadsheets and historical data analysis, financial modeling has evolved significantly in the wake of digitization and algorithmic intelligence. This study explores the multifaceted applications of financial modeling in investment banking, focusing on equity valuation, mergers and acquisitions (M&A), leveraged buyouts (LBO), and risk management. As capital markets become more dynamic and complex, the reliance on traditional models becomes insufficient to capture real-time fluctuations and high-volume data analytics. Hence, this research integrates contemporary technological advancements, including machine learning (ML) and deep learning (DL), to develop predictive financial models. Using historical stock data, economic indicators, financial statements, and macroeconomic trends, this study employs supervised learning models such as Random Forest, Support Vector Machines (SVM), and LSTM (Long Short-Term Memory) networks for sequence prediction. The results demonstrate that AI-enabled models provide more robust, adaptable, and forward-looking financial insights compared to legacy models. Ultimately, the study proposes a hybrid ML-DL-based financial modeling framework that can be embedded into investment banking software to assist analysts and stakeholders in making more informed and data-driven decisions.
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