Artificial intelligence (AI) is increasingly used across all industries, including finance, where it helps manage risks associated with complex investment products called derivative contracts. However, the high transaction costs and other limitations make continuous trading unfeasible. To balance these, investors adjust their portfolios discreetly while considering their risk tolerance levels. The merging of RL with deep Neural Networks (NNs) has proven to be highly effective in finance.
Recently, a research team from Switzerland and the U.S. studied the application of RL agents in hedging derivative contracts. They emphasized the challenge of the scarcity of training data and highlighted the importance of accurate market simulators. This study focused on Deep Contextual Bandits, known for their data efficiency and robustness in RL. This model requires less training data compared to traditional models, making it more adaptable to ever-changing markets, signaling the potential to overcome these hurdles.
Real-world investment organizations’ activities inspire this model: it integrates realistic elements such as the necessity for end-of-day reporting and requires less training data. The researchers found that while the quality of the model relies heavily on accurate market simulations, the model outperformed benchmark systems in efficiency, adaptability, and accuracy under realistic conditions.
In conclusion, this research demonstrates that integrating AI into derivative contract hedging offers a promising risk management avenue in investment banking. Despite the need for further investigation and refinement, the study’s findings contribute to the evolving landscape of AI applications in finance and offer a practical solution that aligns with the operational demands of real-world investment firms.
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