Nur Latifatul Qolbi
Institut Teknologi Sepuluh Nopember

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Instrument Prospective Analysis on BISNIS-27 Index Stocks Using K-Means Cluster Analysis, Forecasting, and Error Correction Model (ECM) Jasmine Angelia Suriawan; Muhammad R Faathir Habibie; Nur Latifatul Qolbi; Anis Syaifatul Azizah; Davina Mufidah; Octavia Ayu Wardhani; Maharani Indriyana
Jurnal Visi Manajemen Vol. 10 No. 2 (2024): Jurnal Visi Manajemen
Publisher : Sekolah Tinggi Ilmu Ekonomi Pariwisata Indonesia Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56910/jvm.v10i2.502

Abstract

Investment is the activity of placing funds in one or several investment objects for a certain period of time to obtain future profits. The important role of investment in supporting economic growth can be seen from its ability to channel funds to sectors in need, thereby increasing productivity, creating jobs, and increasing income. Stock investment has become one of the most popular instruments because of its profit potential, both from dividends and rising stock prices, although it is accompanied by high risk. Technological developments and easy access to information have attracted more people to invest in stocks, especially through stock indices such as the BISNIS27 Index which reflects the performance of the best performing companies. The selection of this index is important as it reflects the overall market performance and provides a strong reference for investors. To analyze stock performance, fundamental and technical analysis approaches are used, which complement each other in providing a complete picture for investors. In addition, forecasting and ECM (Error Correction Model) methods are used to project future stock prices and analyze the long-term relationship between economic variables. The result of this study is that the best forecasting method of ICBP stock is single exponential smoothing with an alpa value (α = 0.7) because it has the lowest error rate and ICBP stock meets all assumptions of stationarity, cointegration, multicollinearity and IIDN.