Budi Santoso, Juni Sasmiharti

Pengukuran Kinerja Saham Bank Digital di Indonesia dengan Model Capital Asset Pricing Model (CAPM)

Introduction

Pengukuran kinerja saham bank digital di indonesia dengan model capital asset pricing model (capm) . Teliti kinerja saham bank digital di Indonesia menggunakan CAPM. Temukan 7 dari 8 saham bank digital efisien & beri keuntungan, sementara 1 saham tidak efisien. Panduan investasi saham.

Abstract

In general, when investors make investments, they certainly take into account the risks and returns from their investments, including investments in digital bank shares. To achieve profits from investments, CAPM (Capital Asset Pricing Model) can be used to predict stock performance. This is because CAPM is useful for determining stocks that are efficient and provide profits for investors. Because each digital bank stock has different performance, the CAPM that suits these conditions is CAPM SML (securities market line). This research uses a sample of 8 digital banks that have conducted an IPO (initial public offering) on the capital market. The research results show that of the 8 digital bank shares, 7 digital bank shares showed efficient stock performance and were able to provide profits. The 7 digital banks are AGRO, AMAR, ARTO, BABP, BBHI, BBYB, and BKSW. Meanwhile, one digital bank share, namely BACA, is the only bank whose shares are inefficient or have poor performance.

Review

The paper, "Pengukuran Kinerja Saham Bank Digital di Indonesia dengan Model Capital Asset Pricing Model (CAPM)," addresses a highly relevant and contemporary issue concerning investor decision-making in the burgeoning digital banking sector. The study aims to evaluate the performance of digital bank stocks in Indonesia, a topic of significant interest given the increasing proliferation and importance of digital financial services. Employing the widely recognized Capital Asset Pricing Model (CAPM), specifically its Securities Market Line (SML) variant, the research sought to identify efficient and profitable investment opportunities among these stocks. The methodology involved analyzing a sample of 8 digital banks that have undergone an Initial Public Offering (IPO) in the Indonesian capital market. Crucially, the findings indicate that a substantial majority—seven out of the eight sampled digital bank stocks—demonstrated efficient performance and the capacity to generate profits for investors, with only one stock identified as underperforming. A significant strength of this research lies in its timely application of a robust financial model to a specific and evolving market segment. The use of CAPM SML offers a clear framework for assessing whether a stock's expected return compensates for its systematic risk, providing practical insights for both individual and institutional investors. The explicit identification of well-performing (AGRO, AMAR, ARTO, BABP, BBHI, BBYB, BKSW) and underperforming (BACA) stocks adds tangible value, enabling more informed investment strategies. However, the abstract could benefit from more detailed information regarding the specifics of the CAPM application. For instance, clarity on the observation period, frequency of data used for return calculations, the choice of market proxy, and the methodology for determining the risk-free rate would enhance the robustness and replicability of the findings. Additionally, while the sample size of 8 might be exhaustive for current IPO'd digital banks, a discussion on the implications of this relatively small sample for broader market generalizations would be valuable. Overall, this study provides a valuable preliminary assessment of digital bank stock performance in Indonesia, offering critical insights for investors navigating this dynamic market. The findings underscore the importance of systematic risk assessment in identifying efficient investment opportunities within this sector. While the abstract presents clear and actionable results, expanding on methodological details in the full paper would further solidify its academic contribution. This research serves as a foundational analysis and could pave the way for future studies exploring other risk-return models, factor analysis, or longer-term performance trends as the digital banking landscape matures. The paper is a welcome addition to the literature on financial market efficiency and investment strategy within emerging markets.

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