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Stock market forecasting using deep learning with long short-term memory and gated recurrent unit

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Abstract

In this paper, recurrent neural networks consisting of GRU and LSTM architectures are used to extract meaningful insights, characteristics, and specific patterns from previously observed, equally spaced, stock market data. The long-term dependency of nonlinear time series data can be learned using GRU and LSTM. In the first phase, the sliding window technique is used to analyse the daywise (i) open, (ii) high, (iii) low, and (iv) closing values of various stocks on the stock market to forecast the future. Performance comparisons show that the proposed GRU and LSTM networks outperform the existing models in terms of prediction accuracy. Multiple datasets were compared and the findings are: (1) the proposed model has a MAPE of 0.630, while the present model’s is 1.748; (2) the MAPE of the proposed model is 0.6243, while that of the existing model is 1.92; (3) the recommended model has a MAPE of 0.7924, while the existing model’s is 0.8587; (4) the recommended model’s MAPE is 1.191, but the current model’s MAPE is 2.99. In the second phase of the process, SMA, EMA, RSI, MACD, and ADX are chosen from among the many technical indicators and used in conjunction with OHLC to further optimise the models.

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Data availability

Enquiries about data availability should be directed to the authors.

Abbreviations

b :

Bias

\(\widetilde{C}\) :

New candidate cell state

\(C\) :

Cell state

f :

Forget gate

h :

Output of LSTM

\(\widetilde{h}\) :

Current state

I :

Input gate

k :

Weight factor

n :

Number of days

o :

Output gate

r :

Reset gate

tanh :

Nonlinear activation function

z :

Block input

W :

Weight

X :

Input

z :

Update gate

t :

Time

σ :

Sigmoid activation function

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Sivadasan, E.T., Mohana Sundaram, N. & Santhosh, R. Stock market forecasting using deep learning with long short-term memory and gated recurrent unit. Soft Comput 28, 3267–3282 (2024). https://doi.org/10.1007/s00500-023-09606-7

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