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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang
Azizah, Apipah Nur
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Decision Sciences, Operations Research & Management

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STOCK PRICE FORECASTING OF PT. BANK CENTRAL ASIA USING HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE-NEURAL NETWORK (ARIMA-NN) METHOD Azizah, Apipah Nur; Fauzi, Fatkhurokhman; Arum, Prizka Rismawati
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 12, No 1 (2024): Jurnal Statistika Universitass Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.12.1.2024.48-59

Abstract

PT. Bank Central Asia is a private company that has superior shares in the Lq45 category but has share prices that fluctuate every period. So forecasting is needed to predict stock prices in the next period. These fluctuations can cause linear and nonlinear relationships in historical stock price data. This research uses the Hybrid ARIMA-NN approach, where the ARIMA model is able to overcome data non-stationarity while the Neural Network is used to capture nonlinear patterns that cannot be explained by the ARIMA model by using the residuals as NN input, the hybrid model can increase forecasting accuracy. The data used is weekly data on closing stock prices for the period January 2019 to June 2024. Prediction measurements use Mean Absolute Percentage Error. The research results show that forecasting with Hybrid ARIMA(2,1,2)-NN(1-5-1) obtained a MAPE value of 3.99% smaller than the ARIMA(2,1,2) a MAPE value of 4.13%, that the accuracy of the forecasting model is good.
Co-Authors Fauzi, Fatkhurokhman Prizka Rismawati Arum