Seasonal versus non-seasonal trends in stock market Malaysia

Abstract

Stock market prediction is considered a challenging task of financial time series analysis, which is beneficial for investors, stock traders, and future researchers. In Malaysia, many machine learning techniques have been used for stock price prediction such as Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and Long Short-Term Memory Network (LSTM). This study will use ARIMA and Seasonal ARIMA to present weekly, monthly and quarterly predictions, both with and without seasonal adjustment method. Stock movement prediction techniques are presented using weekly data of six industries in Malaysia such as gloves, property, airlines, banking, oil and gas, and pharmaceuticals from 26th September 2016 until 28th September 2020. The principle objective of this study is to verify seasonal and non-seasonal occurs in the Malaysian stock market and demonstrate the improvement in predictive performance of the stock market.

Keywords

• stock market prediction
• autoregressive integrated moving average
• artificial neural network
• long short-term memory network