Article
INVESTMENT BANK FEE STRCTURES
Investment banks generate revenue through a range of complex and dynamic fee structures, including advisory fees, underwriting spreads, success fees, and retainers. Traditionally, fee determination and analysis have relied on historical averages, industry benchmarks, and linear models, often missing deeper relationships hidden within vast deal datasets. This study begins with a comprehensive analysis of investment bank fee structures, examining how fees vary by deal type, size, sector, region, and economic context.By extending the research to include Machine Learning (ML) and Deep Learning (DL) techniques, we aim to reveal non-linear patterns and identify the key drivers of fee variability that traditional methods overlook. ML models such as Random Forest and XGBoost are applied to predict fee percentages based on deal characteristics, client type, and market conditions, providing clearer insight into pricing strategies. In parallel, LSTM networks are used to forecast time-series trends in aggregate fee income, capturing seasonality and market cycles in a way that linear forecasting cannot.Our integrated approach demonstrates that AI models not only enhance forecasting accuracy but also uncover complex interactions between variables like market volatility, deal complexity, and client segment. The findings offer actionable insights for banks to design competitive yet profitable fee strategies and better align resources with market demand. Overall, this study illustrates the power of combining traditional financial analysis with modern AI tools to transform decision-making and strategy in investment banking.
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