Analisis Risiko Kerugian Saham dengan Liquidity Adjusted Value at Risk berdasarkan Implementasi Fuzzy Time Series

Siagian, Irene Hasiani (2023) Analisis Risiko Kerugian Saham dengan Liquidity Adjusted Value at Risk berdasarkan Implementasi Fuzzy Time Series. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pasar modal merupakan suatu bidang usaha perdagangan surat-surat berharga yang salah satunya ialah saham. Perkembangan pasar modal di Indonesia semakin menarik para investor untuk melakukan investasi. Investasi dapat dilakukan dengan berbagi macam instrumen investasi salah satunya ialah dengan membeli sejumlah saham. Salah satu saham yang sudah cukup umum di kenal di Indonesia ialah saham PT Astra International Tbk (ASII). Harga saham yang bersifat fluktuatif tersebut dapat menyebabkan adanya ketidakpastian harga saham untuk periode yang akan datang dan menyebabkan risiko yang dapat dialami oleh setiap investor. Oleh karena itu, peramalan dan analisis risiko terhadap suatu data saham diperlukan agar pengambilan keputusan pada masa mendatang lebih akurat. Pada penelitian ini akan membahas peramalan dan estimasi risiko yang akan diimplementasikan pada saham PT Astra International Tbk (ASII). Hasil analisis peramalan saham dengan menggunakan fuzzy time series Lee akan digunakan untuk menghitung risiko investasi dengan menggunakan Liquidity adjusted Value at Risk. Data saham yang digunakan merupakan data harian terhitung dari 1 Januari 2019 hingga 31 Januari 2023. Hasil penelitian menunjukkan bahwa metode fuzzy time series Lee memberikan hasil prediksi yang sangat baik terhadap data aktual dengan MAPE sebesar 1,5274%. Dari hasil peramalan dengan fuzzy time series Lee akan dilakukan analisis risiko dengan Liquidity adjusted Value at Risk. Liquidity adjusted Value at Risk merupakan penambahan risiko likuiditas terhadap Value at Risk. Hasil penelitian menunjukkan estimasi risiko dengan Liquidity adjusted Value at Risk pada tingkat kepercayaan 95% sebesar 3,65%, pada tingkat kepercayaan 96% sebesar 4,16%, pada tingkat kepercayaan 97% sebesar 4,86%, pada tingkat kepercayaan 98% sebesar 5,92%, dan pada tingkat kepercayaan 99% sebesar 7,92%.
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Capital market is a business sector of securities trading, one of which is stock. The development of the capital market in Indonesia is increasingly attracting investors to invest. Investments can be made by various types of investment instruments, one of which is by buying a number of shares. One of the stocks that is quite commonly known in Indonesia is PT Astra International Tbk (ASII) shares. Fluctuating stock prices can cause stock price uncertainty for the coming period and cause risks that can be experienced by every investor. Therefore, forecasting and risk analysis of stock data is needed so that future decision making is more accurate. This study will discuss forecasting and risk estimation that will be implemented on PT Astra International Tbk (ASII) shares. The results of stock forecasting analysis using Lee's Fuzzy Time Series will be used to calculate investment risk using Liquidity adjusted Value at Risk. The stock data used is daily data from January 1, 2019 to January 31, 2023. The results show that fuzzy time series Lee method provides very good predictive results for actual data with a MAPE of 1.5274%. From the results of forecasting with fuzzy time series Lee, risk analysis will be carried out with Liquidity adjusted Value at Risk. Liquidity adjusted Value at Risk is the addition of liquidity risk to Value at Risk. The results showed that the risk estimation with Liquidity adjusted Value at Risk at a 95% confidence level was 3,65%, at a 96% confidence level was 4,16%, at a 97% confidence level was 4,86%, at a 98% confidence level was 5,92%, and at a 99% confidence level of 7,92%.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Estimasi Risiko, Fuzzy Time Series Lee, Liquidity adjusted Value at Risk, Peramalan Saham, Risk Estimation, Stock Forecasting
Subjects:	Q Science > QA Mathematics > QA39.3 Fuzzy mathematics Q Science > QA Mathematics > QA9.64 Fuzzy logic
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Actuaria > 94203-(S1) Undergraduate Thesis
Depositing User:	Irene Hasiani Siagian
Date Deposited:	03 Jul 2023 06:49
Last Modified:	03 Jul 2023 06:49
URI:	http://repository.its.ac.id/id/eprint/98262

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