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Endy Jeri Suswono
Department of Management and Business, Business School IPB University Jl. Raya Pajajaran, Bogor, 16151
Economics, Econometrics & Finance

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Constructing a predicting model for JCI return using adaptive network-based Fuzzy Inference System Endy Jeri Suswono; Dedi Budiman Hakim; Toni Bakhtiar
Jurnal Keuangan dan Perbankan Vol 23, No 1 (2019): January 2019
Publisher : University of Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (880.511 KB) | DOI: 10.26905/jkdp.v23i1.2521

Abstract

The high price fluctuations in the stock market make an investment in this area relatively risky. However, higher risk levels are associated with the possibility of higher returns. Predicting models allows investors to avoid loss rate due to price fluctuations. This study uses the ANFIS (Adaptive Network-based Fuzzy Inference System) to predict the Jakarta Composite Index (JCI) return. Forecasting JCI movement is considered to be the most influential predictor, consisting of Indonesia real interest rate, real exchange rate, US real interest rate, and WTI crude oil price. The results of this study point out that the best model to predict JCI return is the ANFIS model with pi membership function. The predicting model shows that real exchange rate is the most influential factor to the JCI movement. This model is able to predict the trend direction of the JCI movement with an accuracy of 83.33 percent. This model also has better performance than the Vector Error Correction Model (VECM) based on RMSE value. The ANFIS performance is relatively satisfactory to allow investors to forecast the market direction. Thus, investors can immediately take preventive action towards any potential for turmoil in the stock market.JEL Classification: D13, I31, J22DOI: https://doi.org/10.26905/jkdp.v23i1.2521 
Co-Authors Dedi Budiman Hakim Toni Bakhtiar