EKSPEKTASI HETEROGEN DAN SENTIMEN PASAR DALAM PENELITIAN STABILITAS KEUANGAN:SINTESIS BIBLIOMETRIK TREN GLOBAL
DOI:
https://doi.org/10.24843/EJMUNUD.2026.v15.i7.p07Kata Kunci:
analisis sentiment; ekspektasi heterogen; kecerdasan buatan (AI); sentimen pasar; stabilitas keuanganAbstrak
Krisis keuangan yang berulang menunjukkan bahwa model tradisional masih belum memadai untuk deteksi dini ketidakstabilan sistemik. Studi ini menyajikan sintesis bibliometrik tren penelitian global tentang ekspektasi heterogen, sentimen pasar, dan stabilitas keuangan selama tahun 2010–2026. Tidak seperti studi sebelumnya yang meneliti konsep-konsep ini secara terpisah, penelitian ini mengintegrasikan ketiganya dalam kerangka bibliometrik yang terpadu. Metode bibliometrik diterapkan pada 212 dokumen yang diindeks Scopus menggunakan Biblioshiny dan VOSviewer. Analisis tersebut mengungkapkan tingkat pertumbuhan tahunan sebesar 21,31 persen, dengan kontribusi terbesar dari China. Analisis co-occurrence mengidentifikasi tujuh klaster tematik: keuangan perilaku, risiko sistemik, analisis sentimen berbasis AI, pembelajaran mesin, peramalan volatilitas, penularan krisis, dan perilaku pasar negara berkembang. Klaster-klaster ini mendukung proposisi bahwa heterogenitas ekspektasi memperkuat dampak sentimen terhadap stabilitas keuangan. Sebuah kesenjangan penelitian utama telah diidentifikasi: belum ada studi empiris yang menguji metrik sentimen berbasis AI di pasar negara berkembang. Implikasi dari penelitian ini adalah bahwa OJK dan Bank Indonesia perlu mempertimbangkan integrasi indikator sentimen digital dalam kerangka pemantauan risiko sistemik mereka, guna memperkuat ketahanan pasar keuangan nasional.
Recurring financial crises reveal that traditional models remain insufficient for early detection of systemic instability. This study presents a bibliometric synthesis of global research trends on heterogeneous expectations, market sentiment, and financial stability during 2010–2026. Unlike prior studies examining these concepts separately, this research integrates all three within a unified bibliometric framework. A bibliometric method was applied to 212 Scopus-indexed documents using Biblioshiny and VOSviewer. The analysis reveals an annual growth rate of 21.31 percent, with the largest contribution from China. Co-occurrence analysis identifies seven thematic clusters: behavioral finance, systemic risk, AI-based sentiment analysis, machine learning, volatility forecasting, crisis contagion, and emerging market behavior. These clusters support the proposition that expectation heterogeneity amplifies the impact of sentiment on financial stability. A key research gap is identified: no empirical study has tested AI-based sentiment metrics in emerging markets. The implication of this research is that OJK and Bank Indonesia should consider integrating digital sentiment indicators into their systemic risk monitoring framework to strengthen the resilience of the national financial market.
Referensi
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