Forecasting Lebanese stocks using ARIMA models
Abdo Ali Nasser Aldin - PhD student. Belarusian State Economic University
Abstract
This paper presents method of building ARIMA model for stock price prediction. The experimental results obtained with best ARIMA model to predict stock exchange on short-run basis with aim to guide investors in stock market, to create profitable investment selections. In this article our analysis and forecasting are focused on the price of three shares of three different sectors, SOLA (Solidere Company, development and reconstruction sector), BYB (Byblos bank, Banking sector) and HOLC (Holcim Liban, industrial sector). The three companies were selected based upon the market capitalization by sectors of activities in the Beirut Stock Exchange (BSE) and their role on Lebanese economic development. The taken data of the price of three shares of three different sectors from 29th April 2021 to April 29, 2022, and predict the future prices until the end of May 5, 2022, using ARIMA model. Using the standard model selection criteria such as AIC, BIC, log-likelihood and SigmaSQ we diagnosed the forecasting performance of various ARIMA models with a view to determining the best ARIMA model for predicting stock market in each sector under investigation. The outcome of the empirical analysis indicated that ARIMA (1,1,1), ARIMA (1,1,1) and ARIMA (1,1,2) models are respectively the best forecast models for BYB and HOLC and SOLA.
Keywords: time series analysis; modeling; autoregressive integrated moving average (ARIMA); forecasting; stocks.
For citation: Nasser Aldine A. A. Forecasting Lebanese stocks using ARIMA models. Digital models and solutions. 2023. Vol. 2, no. 1. DOI: 10.29141/2782-4934-2023-2-1-1. EDN: VWIFAL.
