Abstract

Predicting stock prices is one of the difficult issues for researchers and investors. The study suggests an equity price prediction based on feature neural network extraction. We expect the stock price using technovative forecasting from traditional Machine Learning (ML) models namely Linear Regression (LR), Autoregressive Integrated Moving Averages (ARIMA), and advanced Deep Learning (DL) algorithms such as Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). We select seven features based on historical data: date, close, open, high, low, volume, and change %. The study’s novelty is the prediction accuracy compared to the step-by-step backtesting methodology from ML to DL algorithms. We first use CNN to extract features from the data consisting of the items from the preceding 10 days to 100 days. After that the extracted feature data and LSTM to predict the stock price. Finally, the study used robotic error measure analysis, such as MAE, RMSE, and R2, to assess the forecasting accuracy of all four models. The CNN-LSTM model provides a consistent stock price forecast based on error measures with maximum prediction exactness ranging from 0 to 1, such as MAE-0.03, RMSE-0.04, and R2-0.98. The proposed CNN-LSTM model maintained its efficiency throughout the process when compared to the LR, ARIMA, and LSTM-RNN models. The study conducts a robustness hypothesis check using the ANOVA test statistic for superior predictability accuracy. In addition, this forecasting technique gives academics real-world experience analyzing financial time series data and confident investment ideas to investors.

Keywords

Equity Prediction, Linear Regression, ARIMA, LSTM-RNN, Deep CNN-LSTM,

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References

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