From Trading Algorithms to Investment Banking Frameworks
AI & RISK
From Trading Algorithms to Investment Banking Frameworks
Alongside developing a trading algorithm prototype with colleagues, I recently completed the J.P. Morgan Investment Banking Virtual Experience, which provided structured insight into valuation, financial analysis, and transaction decision-making. While one project focuses on markets and quantitative experimentation, and the other on corporate finance, both reinforced the importance of disciplined analytical frameworks.
In developing the trading algorithm, we are currently coding a prototype to test structured, rule-based strategies on historical data. The emphasis has been on clearly defining assumptions, evaluating risk exposure, and understanding how small parameter changes influence outcomes. The process has highlighted how fragile models can be without rigorous testing and proper risk controls.
The J.P. Morgan simulation, in contrast, focused on corporate valuation — analysing financial statements, assessing comparable companies, and understanding transaction rationale. It reinforced how investment decisions rely on structured evaluation, clear assumptions, and careful interpretation of financial data rather than instinct or speculation.
What connected both experiences was the role of disciplined analysis. Whether assessing a company’s valuation in an M&A scenario or stress-testing a trading rule against historical volatility, the underlying skill is the same: structured reasoning under uncertainty.
Together, these experiences strengthened my understanding of how quantitative thinking applies across both market-based strategies and corporate finance decision-making, and highlighted the importance of clarity, risk awareness, and analytical rigor in financial environments.
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