How do long short-term memory (LSTM) networks address the training data requirements for applications in finance, particularly in predicting stock prices over time?

Long short‑term memory networks use gating mechanisms that let the model keep important information over many time steps while discarding irrelevant data, so they can learn useful patterns from shorter histories. Because the gates filter noise, the network needs fewer past observations to identify trends that influence future prices. This reduces the amount of labeled training data required compared with standard RNNs, which often need huge datasets to avoid overfitting. For example, an LSTM can predict tomorrow’s stock price using only the last 60 days of data, whereas a plain RNN might need several years of daily prices to achieve similar accuracy. Thus, LSTMs lower the training data burden while still capturing the complex temporal dependencies in financial time series.