Artificial Intelligence for Delta Hedging

Financial risk management is the process of identifying, measuring, and managing financial risks. There are many different types of financial risks, including interest rate risk, credit risk, liquidity risk, market risk, and operational risk. Machine learning and artificial intelligence can be used to identify and measure these risks, as well as to develop strategies for managing them.

A concrete application of machine learning and artificial intelligence is hedging market risks which involves taking offsetting positions so that if the market moves in a particular direction, the hedging instruments will move in the opposite direction, thus reducing the risks of the portfolio. A practical example of using reinforcement learning for hedging is discussed in Reference [1].

Similarly, Reference [2] presented a delta hedging strategy using Neural Network,

In this paper, we leverage a set of state-of-the-art deep learning technologies to explore the landscape of neural delta hedging. We construct 4 different neural architectures, RNN, TCN, AttentionNet, and SpanMLP to verify their ability to approximate or even challenge the black – scholes model in calculating delta value. As a result, unlike previous works who have concentrated in building highly complex models which require excessive computing power, we suggest that simpler architectures, for instance RNN, also have the capacity to minimize Profit and Loss(PnL) for option trading. Our results show that Vanilla RNN structure outperforms post-RNN models such as TCN, AttentionNet and SpanMLP in PnL minimizing tasks.

While it is still in its early days, machine learning and artificial intelligence have the potential to manage financial risks for companies in a more efficient and accurate way than the traditional methods. As this technology continues to evolve, we can only expect these capabilities to improve. What do you think about the role of machine learning and artificial intelligence in risk management? Have you seen examples of either of these technologies being used effectively in this area? Let us know in the comments below.


[1] JC. Hull, Machine Learning in Business: An Introduction to the World of Data Science, Second Edition, 2020, Independently published

[2]  G. Son, J. Kim, Neural Networks for Delta Hedging, 2021,

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