Analisis Model Carhart 4-Faktor Dan Arbitrage Pricing Theory Pada Saham IDXESGL Dengan Pendekatan Regresi Kuantil

Aisha, Nabila Rafa (2026) Analisis Model Carhart 4-Faktor Dan Arbitrage Pricing Theory Pada Saham IDXESGL Dengan Pendekatan Regresi Kuantil. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Penelitian ini bertujuan untuk menganalisis kemampuan model asset pricing dalam menjelaskan return saham indeks IDX ESG Leaders. Model yang digunakan dalam penelitian ini adalah Arbitrage Pricing Theory (APT) dan model Carhart 4-Faktor, model tersebut digunakan untuk menangkap pengaruh faktor-faktor risiko terhadap excess return saham IDXESGL. Faktor risiko yang digunakan yaitu, excess market return (EMR), size (SMB), value (HML), momentum (MOM), inflasi, ekspor, impor, dan jumlah uang beredar. Metode analisis yang digunakan dalam penelitian ini adalah regresi kuantil dengan tingkat kuantil yang digunakan adalah kuantil 10%, 25%, 50%, 75%, dan 90%. Metode ini dipilih karena dapat memperluas analisis pengaruh faktor-faktor risiko dalam model Carhart 4-Faktor dan APT terhadap excess return saham IDXESGL yang tidak hanya pada nilai rata-rata, tetapi juga pada berbagai tingkatan excess return dari kondisi sangat rendah, hingga sangat tinggi. Data yang digunakan merupakan data sekunder berupa excess return saham dan variabel faktor risiko yang diperoleh dari Bursa Efek Indonesia selama bulan Januari 2021 – Desember 2025. Hasil penelitian menunjukkan bahwa dalam model Carhart 4-Faktor dan APT, variabel excess market return (EMR) menjadi faktor yang paling dominan dalam menjelaskan excess return saham IDXESGL. Hal ini, dibuktikan dengan variabel EMR yang selalu signifikan diseluruh tingkat kuantil. Selain itu, kedua model dinilai lebih mampu menjelaskan excess return saham pada kuantil 10% atau pada saat kondisi excess return saham rendah berdasarkan hasil nilai Pinball Loss dan QBIC yang memiliki nilai paling rendah di setiap model.
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This study aims to analyze the ability of asset pricing models to explain the returns of the IDX ESG Leaders index. The models used in this study are the Arbitrage Pricing Theory (APT) and the Carhart 4-Factor model; these models are used to capture the influence of risk factors on the excess returns of IDXESG-indexed stocks. The risk factors used are excess market return (EMR), size (SMB), value (HML), momentum (MOM), inflation, exports, imports, and money supply. The analysis method used in this study is quantile regression, with quantile levels of 10%, 25%, 50%, 75%, and 90%. This method was chosen because it can expand the analysis of the influence of risk factors in the Carhart 4-Factor and APT models on the excess return of IDXESGL stocks, not only on the average value but also at various levels of excess return, ranging from very low to very high. The data used consists of secondary data in the form of stock excess returns and risk factor variables obtained from the Indonesia Stock Exchange for the period January 2021–December 2025. The results indicate that in the Carhart 4-Factor and APT models, the excess market return (EMR) variable is the most dominant factor in explaining the excess returns of IDXESGL stocks. This is evidenced by the fact that the EMR variable is consistently significant across all quantile levels. Furthermore, both models are deemed more capable of explaining stock excess returns at the 10% quantile or during periods of low stock excess returns, as indicated by the Pinball Loss and QBIC values, which are the lowest in each model.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Arbitrage Pricing Theory, Carhart 4-Faktor, Excess return saham, Regresi kuantil, Arbitrage Pricing Theory, Carhart 4-Faktor, Quantile regression, Stock excess returns.
Subjects:	H Social Sciences > HA Statistics > HA31.3 Regression. Correlation. Logistic regression analysis. H Social Sciences > HG Finance > HG4529 Investment analysis H Social Sciences > HG Finance > HG4915 Stocks--Prices Q Science Q Science > QA Mathematics Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Actuaria > 94203-(S1) Undergraduate Thesis
Depositing User:	Nabila Rafa Aisha
Date Deposited:	16 Jul 2026 03:51
Last Modified:	16 Jul 2026 03:51
URI:	http://repository.its.ac.id/id/eprint/135126

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