Multifraktalitas dan Studi Komparatif Prediksi Indeks dengan Metode Arima dan Artificial Neural Network (ANN)
DOI:
https://doi.org/10.21512/tw.v9i2.720Keywords:
LQ45, multifraktality, ARIMA, artificial neural networkAbstract
This paper discusses technical analysis widely used by investors. There are many methods that exist and used by investor to predict the future value of a stock. In this paper we start from finding the value of Hurst (H) exponent of LQ 45 Index to know the form of the Index. From H value, we could determinate that the time series data is purely random, or ergodic and ant persistent, or persistent to a certain trend. Two prediction tools were chosen, ARIMA (Auto Regressive Integrated Moving Average) which is the de facto standard for univariate prediction model in econometrics and Artificial Neural Network (ANN) Back Propagation. Data left from ARIMA is used as an input for both methods. We compared prediction error from each method to determine which method is better. The result shows that LQ45 Index is persistent to a certain trend therefore predictable and for outputted sample data ARIMA outperforms ANN.
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References
Ang, R. (1997). Buku pintar pasar modal Indonesia, Jakarta: Mediasoft Indonesia.
Anonim. (2004). Membangun jaringan syaraf tiruan (menggunakan MATLAB dan Excel Link), Yogyakarta: Graha Ilmu.
Archelis, S. (2000). Technical analysis from A to Z, Equis International.
Fadjrih Asyik, N. (1999). Tambahan kandungan informasi arus kas. Jurnal Riset Akuntansi Indonesia, 2(2).
Fernandez-Rodriguez, F., Gonzales-Martel, C., and Sosvilla-Rivero, S. (1999). Technical analysis in the Madrid stock exchange. Fundacion de Estudios Economia Aplicada Working Paper, April 1999.
Fernandez-Rodriguez, F., Gonzales-Martel, C., and Sosvilla-Rivero, S. (2000). Technical analysis in foreign exchange markets: Linear versus nonlinear trading rules. Fundacion de Estudios Economia Aplicada Working Paper, September 2000.
Fernandez-Rodriguez, F., Gonzales-Martel, C., and Sosvilla-Rivero, S. (2001). Optimization of technical trading rules by genetic algorithms: Evidence from the Madrid stock exchange. Fundacion de Estudios Economia Aplicada Working Paper, August 2001.
Firmansyah. (2000). Peramalan inflasi dengan metode Box-Jenkins (ARIMA): Studi kasus tingkat inflasi kota Semarang dan Yogyakarta 1994-2000. Media Ekonomi dan Bisnis, 12(2), Desember 2000.
Gujarati, D.N (2003). Basic econometric, 4th ed., McGraw Hill, Inc.
Hanafi, M. (1997). Informasi laporan keuangan: Studi kasus pada emiten BEJ. Kelola, 16(6), 1997.
Huang, S.C. (1990). Timing the stock market for maximum profit, Chicago, Illinois: Probus publishing company.
Kusumadewi, S. (2003). Artificial intelligence (teknik dan aplikasinya). Yogyakarta: Graha Ilmu.
Machfoed, M. (1994). Financial ratio analysis and the prediction of earnings changes in Indonesia. Kelola, 7(3), 114-134, 1994.
Natarsyah, S. (2000), Analisis pengaruh beberapa faktor fundamental dan risiko sistematik terhadap harga saham: Kasus industri barang kKonsumsi yang go public di pasar modal Indonesia. Jurnal Ekonomi dan Bisnis Indonesia, 15(3), 294-312.
Parawiyati dan Baridwan, Z. (1998). Kemampuan laba dan arus kas dalam memprediksi laba dan arus kas perusahaan go public di Indonesia. Jurnal Riset Akuntansi Indonesia, 1(1), 1-10, Januari.
Parisi, F., and Vasques, A. (2000). Simple technical trading rules of stock returns: Evidence from 1987 – 1998 in Chile. Emerging Market Review, 1.
Qizam, I. (2001). Analisis kerandoman perilaku laba perusahaan di bursa efek Jakarta, Simposium Nasional Akuntansi IV IAI-KAPd, Agustus.
Sartono, A., dan Zulaihati, S. (1998). Rasionalitas investor terhadap pemilihan saham dan penentuan portofolio optimal dengan indeks tunggal di BEJ. Kelola, 17, Juli.
Seiler, M.J., and Rom, W. (1997). A historical analysis of market efficiency: Do historical returns follow a random walk. Journal of Financial and Strategic Decision, 10(2).
Sekaran, U. (1992). Research methods for business: Skill building approach, 2nd ed., John Wiley & Sons, Inc.
Sharpe, W.F., Gordon, J.A., and Bailey, V. (1995). Investment, New York: Prentice Hall.
Siang, J.J. (2005). Jaringan syaraf tiruan dan pemrogramannya menggunakan MATLAB, Yogyakarta: Andi Offset.
Trisna, D.Q. (2003). Pengujian penerapan analisis teknikal dalam memprediksi indeks LQ45 di bursa efek Jakarta. Tesis tidak dipublikasikan, Semarang: MAKSI UNDIP.
Trisnawati, R. (1999). Pengaruh informasi prospektus pada return saham di pasar modal. Simposium Nasional Akuntansi II dan Rapat Anggota II, Ikatan Akuntan Indonesia, Kompartemen Akuntan Pendidik, 1-13, 24-25 September.
Triyono dan Hartono, J. (2000). Hubungan kandungan informasi arus kas, komponen arus kas dan laba akuntansi dengan harga saham atau return saham. Jurnal Riset Akuntansi Indonesia, 2(1).
Utama, S., dan Budi Santoso, A.Y. (1998). Kaitan antara price/book value dan imbal balik saham pada bursa efek Jakarta. Jurnal Riset Akuntansi Indonesia, 1(1), 127-139, Januari.
Utami, W., dan Suharmadi. (1998). Pengaruh informasi penghasilan perusahaan terhadap harga saham di bursa efek Jakarta. Jurnal Riset Akuntansi Indonesia, 1(2), 255-268, Juli.
Yao, J., Lim Tan, C., and Jean-Lee, P. (1999). Neural networks for technical analysis: A study on KLCI. International Journal of Theoretical and Applied Finance, 2(2), 221-241.
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