Fullchis Nurtjahjani, Dwi Ulfa Nurdahlia, Mutmainnah Mutmainnah, Sumarni Sumarni, Christoffel Mardy Oktarisa Mintardjo, Sulfi Abdul Haji, Muh. Irwan Nur Hamiddin

Analisis Strategi Optimalisasi Saham LQ 45 pada Bursa Efek Indonesia Tahun 2019

Introduction

Analisis strategi optimalisasi saham lq 45 pada bursa efek indonesia tahun 2019. Teliti strategi optimalisasi saham LQ 45 di BEI tahun 2019. Analisis kinerja portofolio optimal aktif (Metode Indeks Tunggal) vs. pasif, temukan perbedaan signifikan return dan risiko.

Abstract

Permasalahan dalam penelitian ini adalah bagaimana cara menganalisis komposisi dan kinerja portofolio optimal menggunakan MIT saham Indeks LQ 45 di BEI dan bagaimana cara menganalisis perbandingan kinerja portofolio optimal saham Indeks LQ 45 strategi aktif menggunakan MIT dengan strategi pasif mengikuti indeks pasar. Penelitian ini bertujuan untuk menganalisis komposisi dan kinerja portofolio optimal menggunakan MIT saham Indeks LQ 45 di BEI, dan menganalisis perbandingan kinerja portofolio optimal saham Indeks LQ 45 strategi aktif menggunakan MIT dengan strategi pasif mengikuti indeks pasar. Penelitian ini menemukan bahwa kinerja portofolio saham dari strategi aktif MIT menunjukkan hasil ER portofolio sebesar 0,05776 dengan tingkat risiko 0,02490. Sementara hasil analisis statistik dengan metode Paired-Samples T-Test diketahui bahwa return kelompok saham strategi aktif dan kelompok saham strategi pasif memiliki pebedaan yang signifikan. Angka signifikansi t hitung sebesar 0,046 < 0,05 (α = 5%). Untuk perbedaan risiko diukur dengan standar deviasi yang mencerminkan risiko total saham. Hasil perhitungan menunjukkan angka signifikansi sebesar 0,009 < 0,05 (α = 5%) yang mengindikasikan adanya perbedaan yang signifikan antara risiko portofolio strategi aktif Metode Indeks Tunggal dengan portofolio strategi pasif mengikuti Indeks LQ 45.

Review

The paper, "Analisis Strategi Optimalisasi Saham LQ 45 pada Bursa Efek Indonesia Tahun 2019," addresses a relevant and practical topic in financial markets: portfolio optimization for LQ 45 index stocks on the Indonesia Stock Exchange (BEI). The research sets out to achieve two primary objectives: first, to analyze the composition and performance of an optimal portfolio constructed using the Single Index Model (MIT) for LQ 45 stocks; and second, to critically compare the performance of this active MIT-based strategy against a passive market index-tracking approach. This comparative framework provides a valuable contribution to understanding the efficacy of active management within a specific emerging market context. Methodologically, the study employs the Single Index Model (MIT) for active portfolio construction and utilizes a Paired-Samples T-Test to statistically compare the active and passive strategies. The abstract indicates compelling findings: the active MIT portfolio demonstrated an "ER" of 0.05776 with a risk of 0.02490, though a clearer definition of "ER" would enhance interpretability. Crucially, the statistical analysis reveals significant differences. A p-value of 0.046 (< 0.05) strongly suggests a statistically significant difference in returns between the active (MIT) and passive portfolios. Furthermore, a p-value of 0.009 (< 0.05) indicates a significant difference in risk profiles (measured by standard deviation) between the two strategies, highlighting that the active approach resulted in a distinct risk-return outcome compared to simply tracking the LQ 45 index. The strength of this research lies in its clear comparative design and the application of a well-established portfolio theory model, buttressed by robust statistical testing to substantiate its claims. The findings suggesting significant differences in both return and risk between active and passive strategies for LQ 45 stocks in 2019 offer important practical implications for Indonesian investors. However, a notable limitation is the study's narrow time horizon, focusing solely on the year 2019. Extending the analysis over multiple market cycles and varying economic conditions would significantly enhance the generalizability and robustness of the conclusions. Future research could also benefit from providing more granular details on the MIT implementation, exploring the transaction costs associated with active strategies, and potentially integrating other multi-factor models for a richer comparative analysis.

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