How can Recurrent Neural Networks (RNN) be effectively utilized in the finance sector for real-time data processing?

Recurrent Neural Networks can read a stream of market data one piece at a time, just like a person listening to a conversation, so they can keep a memory of recent price changes and news events. By training on historical tick‑by‑tick data, the RNN learns to recognize patterns that often precede a price move, and it can output a short‑term forecast while still receiving new data. In a real‑time trading system the RNN sits in the data pipeline, receives each new price or volume tick, updates its hidden state, and immediately emits a predicted price or a trading signal. For example, an RNN trained on the last 200 ticks of a stock can predict the next minute’s close, allowing a trading desk to adjust orders in milliseconds. Because the network processes data sequentially, it naturally handles irregular time gaps and can adapt on the fly, making it ideal for high‑frequency trading and risk monitoring.