PENGGUNAAN KOMBINASI INDIKATOR SMA, EMA, MACD, RSI, DAN MFI UNTUK MENENTUKAN KEPUTUSAN BELI DAN JUAL PADA SAHAM-SAHAM DI SEKTOR LQ45 BEI TAHUN 2018
DOI:
https://doi.org/10.24002/modus.v32i2.3519Abstract
ABSTRACT
This research was conducted to determine the selection of the highest performing stock group indicators, so that it can be used as a reference for buying shares. This study tested six combinations of technical indicator groups, then this technical combination was used to test the price of shares whose companies are included in the LQ45 group on the Indonesia Stock Exchange (BEI) in 2018. The results of this study indicate that the use of stock indicator groups with the maximum profit percentage and the shortest number of days is the best groups. The sequence of indicator groups with good performance to poor performance is 1: EMA, MFI, and RSI; 2: MA, MFI, and RSI; 3: MA, MACD, and MFI; 4: MA, MACD, and RSI; 5: EMA, MACD, and MFI; 6: EMA, MACD, and RSI.
Keywords: Indonesia Stock Exchange; LQ45; stock trading; technical indicators
ABSTRAK
Penelitian ini dilakukan untuk mengetahui pemilihan kelompok indikator saham yang paling tinggi kinerjanya, sehingga dapat dijadikan acuan untuk pembelian saham. Pada penelitian ini diuji enam kombinasi dari kelompok indikator teknikal, selanjutnya kombinasi teknikal ini digunakan untuk menguji harga saham yang perusahaannya termasuk dalam kelompok LQ45 di Bursa Efek Indonesia (BEI) tahun 2018. Hasil penelitian ini menunjukkan penggunaan kelompok indikator saham dengan persentase keuntungan maksimal dan jumlah hari yang paling pendek adalah yang paling baik. Urutan kelompok indikator dengan kinerja yang bagus ke kinerja yang kurang bagus adalah: Peringkat 1: EMA, MFI, dan RSI; Peringkat 2: MA, MFI, dan RSI; Peringkat 3: MA, MACD, dan MFI; Peringkat 4: MA, MACD dan RSI; Peringkat 5: EMA, MACD, dan MFI; Peringkat 6: EMA, MACD, dan RSI.
Kata kunci: Bursa Efek Indonesia; LQ45; jual beli saham; indikator teknikal
References
Bhandari, B. (2013). Equity Tranding Techniques - Stocks, Moving averages and how to leverage them. Futures: News, Analysis & Strategies For Futures, Options & Derivatives Traders. http://eds.b.ebscohost.com/eds/pdfviewer/pdfviewer?sid=63236117-3b85-410a-b3a9-de6153c82e42%40sessionmgr115&vid=1&hid=109
Bhandari, B. (2016). The absolute benefits of relative strength. 56–59.
Bornholt, G., Dou, P., & Malin, M. (2015). Trading Volume and Momentum: The International Evidence. Multinational Finance Journal, 19(4), 267–313. https://doi.org/10.17578/19-4-2
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance, 47(5), 1731–1764. https://doi.org/10.1111/j.1540-6261.1992.tb04681.x
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance. https://doi.org/10.2307/2325486
Gulia, S. (2016). Testing of Relationship Between Trading Volume, Return and Volatility. Amity Global Business Review, i, 96–103.
Hasan, M. I. (2002). Pokok-Pokok Materi Metodologi Penelitian & Aplikasinya. In Ghalia Indonesia.
Ilommki, J., Laurila, H., & McAleer, M. (2018). Simple Market Timing with Moving Averages. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3180614
Kwon, K. Y., & Kish, R. J. (2002). Technical trading strategies and return predictability: NYSE. Applied Financial Economics, 12(9), 639–653. https://doi.org/10.1080/09603100010016139
Lento, C. (2011). A Combined Signal Approach To Technical Analysis On The S&P 500. Journal of Business & Economics Research (JBER). https://doi.org/10.19030/jber.v6i8.2460
Lento, C., & Gradojevic, N. (2007). The profitability of technical trading rules: A combined signal approach. Journal of Applied Business Research. https://doi.org/10.19030/jabr.v23i1.1405
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. Journal of Finance, 55(4), 1705–1765. https://doi.org/10.1111/0022-1082.00265
Nor, S. M., & Wickremasinghe, G. (2014). The profitability of MACD and RSI trading rules in the Australian stock market. Investment Management and Financial Innovations, 11(4), 194–199.
Park, C. H., & Irwin, S. H. (2007). What do we know about the profitability of technical analysis? In Journal of Economic Surveys. https://doi.org/10.1111/j.1467-6419.2007.00519.x
Pring, M. J. (2014). Technical Analysis Explained. The Successful Investor’S Guide to Spotting Investment Trends and Turning Points. In Fifth Edition.
Rosillo, R., Fuente, D. De, & Brugos, J. A. L. (2013). Technical analisys and the Spanish stock exchange RSI MACD STOCHASTIC AND MOMENTUM. 1541–1550.
Silalahi, U. (2014). Pengertian metode dan metodologi penelitian. Feb, 26. https://doi.org/https://doi.org/10.1016/j.intmar.2016.12.002
Suarsa, A. (2006). Perbandingan Analisa Teknikal Metode Simple Moving Average, Weigted Moving Average, dan Exponential Moving Average dalam Memprediksi Harga Saham LQ-45 Sub Sektor Telekomunikasi di Bursa Efek Jakarta.
Subarkah, B. Y. (2008). Analisis Keakuratan Penggunaan Indikator Relative Strengh Index Periode 21 Hari sebagai Pedoman dalam Menentukan Sinyal Membeli dan Sinyal Menjual Saham Industri Pertambangan di Bursa Efek Indoneisa (Periode Maret 2006 - Februari 2008). Universitas Sanata Sharma Yogyakarta.
Sukamulja, S. (2004). Analisis Fundamental, Teknikal, dan Program Metastock.
Sukamulja, S. (2019). Analisis Laporan Keuangan Sebagai Dasar Pengambilan Keputusan Investasi. Penerbit Andi.
Wilder, J. (1978). New Concepts in Technical Trading Systems. New Concepts in Technical Trading Systems (Vol. 24, Issue 3, pp. 63–69). https://doi.org/10.2307/1930538
