Stock price prediction using LSTM, RNN and CNN-sliding window model

TLDR

Stock markets profoundly influence the economy, and price movements critically affect investor gains, yet existing forecasting methods—linear AR/MA/ARIMA and nonlinear ARCH/GARCH or neural networks—typically target index trends or single‑company daily closing prices. The study proposes a model‑independent approach that employs three deep learning architectures to predict prices of NSE‑listed companies and compares their performance. We identify latent dynamics with deep learning models, applying a sliding‑window approach for short‑term predictions and evaluating performance via percentage error.

Abstract

Stock market or equity market have a profound impact in today's economy. A rise or fall in the share price has an important role in determining the investor's gain. The existing forecasting methods make use of both linear (AR, MA, ARIMA) and non-linear algorithms (ARCH, GARCH, Neural Networks), but they focus on predicting the stock index movement or price forecasting for a single company using the daily closing price. The proposed method is a model independent approach. Here we are not fitting the data to a specific model, rather we are identifying the latent dynamics existing in the data using deep learning architectures. In this work we use three different deep learning architectures for the price prediction of NSE listed companies and compares their performance. We are applying a sliding window approach for predicting future values on a short term basis. The performance of the models were quantified using percentage error.