Performance Assessment of Temporal Frame Size in Stock Price Prediction

Accurate Stock Price Prediction (SPP) is essential for those involved in stock market business. Due to the nonlinear and erratic characteristics of the stock market, estimation of the future prices of stocks is quite challenging. In addition to classical models such as autoregressive integrated moving average (ARIMA), random forest (RF), machine learning-based approaches like Multilayer Perceptron (MLP), Recurrent Neural Network (RNN) possessing Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and their variants have been proposed for SPP. Each of the techniques offers a certain level of accuracy with particular limitations. The developments in machine learning processes fascinating the forecasting of stock market prices. Estimating direction of change (DOC) is one of the most important and hard issues in the stock price estimation process. This particular study examines how the temporal frame size might affect the success rate of SPP and DOC estimation with the time evolution. To do this, seven National Association of Securities Dealers Automated Quotations (NASDAQ) equities recorded between June 30, 2009, and April 3, 2024 were employed in the study. A MLP and an RNN with LSTM were used for prediction. The accuracy of the model was measured in accordance with the actual values. The error control was made with mean absolute error (MAPE). The results showed that there is an impact of frame size on the SPP as well as DOC estimation. Also, results showed that while LSTM performed better in long-term prediction, MLP performed better in shortterm prediction.