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 can learn useful patterns from relatively short financial time series by remembering important past information through gating mechanisms, so they need fewer examples than a plain feed‑forward network. Because LSTMs keep a hidden state that carries context over many time steps, they can capture seasonal and trend effects that would otherwise require millions of labeled examples. This ability to model long‑range dependencies means a model trained on a few years of daily stock prices can still predict tomorrow’s price with reasonable accuracy. For instance, an LSTM trained on five years of daily closing prices can output a one‑day‑ahead forecast after seeing only a few hundred thousand data points. Thus, LSTMs reduce the amount of training data required for finance applications while still handling the noisy, non‑stationary nature of stock markets.