PERAMALAN INDEKS SAHAM SYARIAH INDONESIA (ISSI) MENGGUNAKAN MODEL MARKOV SWITCHING AUTOREGRESSIVE (MSAR)

AGI, KHOERUNNISA (2022) PERAMALAN INDEKS SAHAM SYARIAH INDONESIA (ISSI) MENGGUNAKAN MODEL MARKOV SWITCHING AUTOREGRESSIVE (MSAR). Sarjana / Sarjana Terapan (S1/D4) thesis, Universitas Muhammadiyah Semarang.

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Abstract

ABSTRAK Khoerunnisa, A, 2022, Peramalan Indeks Saham Syariah Indonesia (ISSI) menggunakan Model Markov Switching Autoregressive (MSAR), Skripsi, Program Studi Statistika, Universitas Muhammdiyah Semarang. Pembimbing I. Indah Manfaati Nur, M.Si, II. Prizka Rismawati A, S.Si, M.Stat Model Markov Switching Autoregressive (MSAR) adalah salah satu model deret waktu nonlinier yang memodelkan data deret waktu yang mengalami perubahan fluktuasi yang terjadi pada data. Indeks Saham Syariah Indonesia (ISSI) merupakan indeks saham syariah yang beranggotakan seluruh saham syariah. Data yang digunakan adalah data bulanan harga penutupan Indeks Saham Syariah Indonesia (ISSI) periode Juni 2011 sampai Mei 2022 yang bersumber dari Bursa Efek Indonesia (BEI). Pemodelan MSAR diperoleh sebanyak 10 model yang dibagi menjadi 2 yaitu model dengan 2 state dan model dengan 3 state. Selain itu, Model MSAR mempunyai variabel state dan nilai peluang matriks transisi yang dihitung dengan menggunakan metode Maximum Likelihood estimation (MLE). Estimasi parameter dilakukan pada seluruh model dan nilai Bayesian Information Criterion (BIC) minimum menyatakan model terbaik. Adapun model MSAR terbaik yang didapatkan adalah model MS(2)AR(2) dengan nilai BIC sebesar -395,8. Model dan peramalan diperoleh kondisi state 1 (peningkatan) dan state 2 (penurunan). Analisis hasil peramalan untuk 12 bulan ke depan bahwa data Indeks Saham Syariah Indonesia pada pertengahan tahun 2022 mengalami trend naik dan fluktuasi pada awal tahun 2023. Keakuratan hasil peramalan dihitung dengan Mean Absolute Percentence Error (MAPE) yang diperoleh sebesar 23% yang berarti kriteria hasil peramalan cukup baik. Kata Kunci: BIC, ISSI, MLE, MSAR ABSTRACT Khoerunnisa, A, 2022, Forecasting the Indonesian Syariah Stock Index (ISSI) using the Markov Switching Autoregressive (MSAR) Model. Thesisi, Statistics Study Program, Muhammdiyah University Semarang. Advisor I. Indah Manfaat Nur, M.Si., II. Prizka Rismawati A, S.Si, M.Stat. The Markov Switching Autoregressive (MSAR) model is a nonlinear time series model that models time series data that undergoes changes in fluctuations that occur in the data. The Indonesian Syariah Stock Index (ISSI) is a syariah stock index consisting of all syariah shares. The data used is the monthly closing price of the Indonesian Syariah Stock Index (ISSI) for the period June 2011 to May 2022, sourced from the Indonesia Stock Exchange (IDX). MSAR modeling obtained as many as 10 models which are divided into 2, namely a model with 2 states and a model with 3 states. In addition, the MSAR Model has a state variable and a transition matrix probability value which is calculated using the Maximum Likelihood estimation (MLE) method. Parameter estimation was carried out on all models and the minimum Bayesian Information Criterion (BIC) value was declared the best model. The best MSAR model obtained is the MS(2)AR(2) model with a BIC value of -395.8. Models and forecasts obtained state 1 (increase) and state 2 (decrease). Analysis of forecasting results for the next 12 months that the Indonesian Syariah Stock Index data in mid-2022 experienced an uptrend and fluctuation at the beginning of the year 2023. The accuracy of the forecasting results is calculated by means of the Mean Absolute Percentence Error (MAPE) which is obtained by 23%, which means that the criteria for the forecasting results are quite good. Key Words: BIC, ISSI, MLE, MSAR

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