Kenzha Amalia Zahra, Ifa Nurmasari

Pengaruh Inflasi, Suku Bunga Bi dan Nilai Tukar Rupiah terhadap Indeks Harga Saham Lq45 di Bursa Efek Indonesia Periode 2010-2024

Introduction

Pengaruh inflasi, suku bunga bi dan nilai tukar rupiah terhadap indeks harga saham lq45 di bursa efek indonesia periode 2010-2024. Teliti pengaruh inflasi, suku bunga BI, dan nilai tukar rupiah terhadap Indeks Harga Saham LQ45 di BEI periode 2010-2024. Kurs rupiah signifikan, inflasi & suku bunga tidak parsial.

Abstract

Penelitian ini bertujuan mengetahui apakah terdapat pengaruh inflasi, suku bunga BI, dan nilai tukar rupiah (kurs) terhadap indeks saham LQ45 tahun 2010-2024. Data yang digunakan merupakan data sekunder berupa time series selama 15 tahun, yang diperoleh dari beberapa sumber yang relevan seperti Bank Indonesia (BI), Badan Pusat Statistik (BPS), dan Yahoo Finance. Metode penelitian yang digunakan yaitu metode penelitian kuantitatif dengan teknik uji statistik deskriptif, uji asumsi lklasik, analisis regresi linear berganda, uji t, uji F, dan koefesien determinasi. Hasil penelitian menunjukkan bahwa secara parsial inflasi berpengaruh tidak berpengaruh signifikan terhadap indeks harga saham LQ45 dengan nilai thitung 0,959 < ttabel 2,20099 dengan signifikansi 0,358 > 0,05, suku bunga BI secara parsial juga tidak berpengaruh signifikan terhadap indeks harga saham LQ45 dengan nilai thitung |-2,141| < ttabel 2,20099 dengan nilai signifikansi 0,55 > 0,05, sementara nilai tukar rupiah berpengaruh signifikan terhadap indeks harga saham LQ45 dengan nilai thitung 3,504 > ttabel 2,20099 dengan nilai signifikansi 0,005 < 0,05. Sementara secara simultan inflasi, suku bunga BI, dan nilai tukar rupiah (kurs) berpengaruh signifikan terhadap indeks harga saham LQ45 dengan nilai Fhitung sebesar 7,179 > Ftabel 3,982 dengan nilai signifikansi sebesar 0,006 < 0,05.. Nilai koefesien determinasi Adjusted R-Square menunjukkan bahwa 57% ditentukan oleh variabellinflsi, suku bunga, dan nilal tukar rupiah. Sisanya 43% dipengaruhi oleh variabel lain yang peneliti tidak teliti.

Review

This study critically examines the influence of key macroeconomic indicators – inflation, Bank Indonesia (BI) interest rates, and the Rupiah exchange rate – on the LQ45 stock index performance within the Indonesian stock market over an extended period from 2010 to 2024. The research addresses a pertinent question in financial economics, exploring the often-debated relationships between macroeconomic stability and equity market dynamics in an emerging economy context. The chosen timeframe is substantial, offering a robust data set to analyze long-term trends and potential shifts in these relationships. Employing a quantitative research design, the study utilizes 15 years of secondary time-series data sourced from reputable institutions like Bank Indonesia, the Central Statistics Agency (BPS), and Yahoo Finance. The methodological approach is sound, incorporating descriptive statistics, classical assumption tests, multiple linear regression, and both partial (t-test) and simultaneous (F-test) significance tests, alongside the coefficient of determination (Adjusted R-Square). The findings reveal nuanced insights: individually, inflation (t-statistic 0.959, p-value 0.358) and the BI interest rate (absolute t-statistic |-2.141|, p-value 0.55) do not significantly impact the LQ45 index. Conversely, the Rupiah exchange rate (t-statistic 3.504, p-value 0.005) demonstrates a significant partial effect. However, when considered together, inflation, BI rate, and the exchange rate collectively exert a significant influence on the LQ45 index (F-statistic 7.179, p-value 0.006), explaining 57% of its variation, with the remaining 43% attributed to unexamined factors. While the study provides valuable preliminary insights, several aspects could enhance its depth and rigor. The abstract does not specify the frequency of the time-series data (e.g., annual, quarterly, monthly); given the 15-year period and the dynamic nature of stock markets, using higher-frequency data (e.g., monthly or quarterly) could capture market reactions more accurately and allow for the investigation of lagged effects, which are often crucial in macroeconomic-financial linkages. Furthermore, while the analysis identifies correlations, future research could explore causality more robustly using advanced econometric techniques like Granger causality or cointegration analysis. Discussing the theoretical implications of these findings within established financial models (e.g., Efficient Market Hypothesis, Arbitrage Pricing Theory) and offering practical recommendations for investors and policymakers would further enrich the paper's contribution.

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