Engineering Alpha: The Role of AI in Quantitative Finance
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Engineering Alpha: The Role of AI in Quantitative Finance discusses how Artificial Intelligence can be used in financial engineering as a supplementary tool that complements classical quantitative approaches but does not substitute them. The report is started with the statement of the drawbacks of the traditional models like Black- Scholes and Value-at-Risk, and that the calculational ability of AI can be applied in the exact areas where the classical models fail. The literature review follows the history of AI in quantitative finance by relying on seminal works that show that machine learning can discern non-linear market trends that classical models are unable to achieve. The methodology develops the comparison of AI and classical benchmarks in four areas, and performance metrics include Sharpe ratios, pricing errors, and AUC scores. The most data- intensive is the results section, which shows that AI halved the time to price exotic derivatives, helped to improve portfolio Sharpe ratios up to 390, generated directional trading hits 58-63% in major market crises, and breached Value-at-Risk by up to 35% less than human counterparts. The conclusion supports the idea that the future of quantitative finance is the hybridization where AI is involved in high-dimensional data processing, and human financial engineers in the guise of grounding models, governing models, and strategic control, which is why AI is not the substitute of the quant, but the most potent tool in his/her hands.
Keywords
Alpha Generation, Derivatives Pricing, Financial Engineering, Machine Learning, Quantitative Finance