Stack-Maven: An Adaptive Learning Rate Strategy for Stacked LSTM-based Stock Price Prediction
Mihir Choudhary, Rizwan Shaikh, Harshal Borude, Kartik Padayachi, Pushpalata Aher, Ram Kumar Solanki
LSTM, neural networks, RMSE, stock price prediction, stacked
Predicting stock prices is a complex task due to the influence of unpredictable factors. However, accurate predictions are valuable for investors and traders. Deep learning approaches, particularly Long Short-Term Memory (LSTM) neural networks, have shown promise in stock price prediction. This paper proposes a stacked LSTM model that captures short-term and long-term dependencies using technical indicators and historical price data. The model is trained using a sliding window approach and evaluated with metrics like MSE and RMSE. Experimental results demonstrate the effectiveness of the proposed method, outperforming other models with an average prediction accuracy of 95%. The approach offers advantages in capturing dependencies, handling diverse input features, and adapting to market changes. Potential applications include trading strategies, portfolio optimization, and risk management. However, limitations include the quality of input features and the model's inability to account for unforeseen events. Nonetheless, the stacked LSTM model holds promise for stock price prediction, aiding investment decisions, and further research is needed to improve its performance in highly volatile markets.
Article Details
Unique Paper ID: 160521

Publication Volume & Issue: Volume 10, Issue 1

Page(s): 559 - 564
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