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Using RNN, LSTM, and GRU Networks to Predict Stock Prices

Article QuantInsti blog
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Summary

This tutorial describes a neural-network workflow for predicting the next stock price from daily OHLC data for an Indian listed company. It covers scaling inputs and targets, arranging observations into sequential training, validation, and test sets, and comparing four recurrent architectures: a basic RNN, an LSTM, an LSTM with peephole connections, and a GRU. Training uses mini-batches, mean squared error, and the Adam optimizer; predictions are compared with held-out target prices using a table and graph.

The author reports that the predictions track the target prices closely, but supplies no quantitative forecast metrics or evidence that the models produce a tradable strategy. The dataset is daily and relatively small by the author’s account, and the article recommends more observations. It does not explain safeguards against time-series leakage in scaling or sample construction, nor does it evaluate transaction costs, risk, or out-of-sample trading returns. Price prediction quality therefore remains distinct from demonstrated trading performance.

Key ideas

  • The tutorial frames stock-price forecasting as a sequence-learning problem using OHLC inputs.
  • It compares basic RNN, LSTM, peephole LSTM, and GRU architectures.
  • Inputs and targets are scaled, then divided into training, validation, and test sequences.
  • The models use mini-batch training with mean squared error and Adam optimization.
  • The reported visual comparison is not accompanied by quantitative metrics or evidence of trading profitability.

Tags

EquitiesMachine learningStatisticsBacktesting

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.