FINANCIAL FORECASTING AND MACHINE LEARNING: A BIBLIOMETRIC ANALYSIS OF GLOBAL RESEARCH TRENDS

DOI:

https://doi.org/10.29303/akurasi.v9i1.839

Penulis

  • Iwan Kurniawan Universitas Pendidikan Indonesia
  • Nugraha Nugraha Universitas Pendidikan Indonesia
  • Maya Sari Universitas Pendidikan Indonesia

Kata Kunci:

Bibliometrik, financial forecasting, machine learning, deep learning, stock market

Abstrak

Pesatnya pertumbuhan aplikasi kecerdasan buatan (artificial intelligence) dalam bidang keuangan telah menghasilkan literatur yang luas, namun tinjauan komprehensif mengenai struktur intelektualnya masih terbatas. Penelitian ini bertujuan untuk memetakan dan menganalisis tren riset global dalam peramalan keuangan (financial forecasting) dan prediksi harga saham menggunakan machine learning antara tahun 2015 dan 2025. Dengan menggunakan pendekatan bibliometrik, sebanyak 197 artikel terindeks Scopus dianalisis melalui paket Bibliometrix di R Studio dengan mengikuti kerangka kerja PRISMA. Analisis ini mencakup performa publikasi, jaringan kolaborasi antarpenulis (co-authorship), serta evolusi tematik. Hasil penelitian menunjukkan tingkat pertumbuhan publikasi tahunan sebesar 13,98% dengan rata-rata 16,16 sitasi per dokumen. "Forecasting" muncul sebagai tema penelitian sentral yang terhubung erat dengan "machine learning", "financial markets", dan "LSTM." Kolaborasi internasional mencakup 32,99% dari total publikasi, dengan Tiongkok, India, dan Amerika Serikat sebagai kontributor utama. Evolusi tematik menunjukkan adanya pergeseran dari pendekatan ekonometrika tradisional menuju model prediksi berbasis kecerdasan buatan dan deep learning. Studi ini berkontribusi dengan menyediakan peta intelektual yang komprehensif mengenai riset peramalan keuangan berbasis AI serta mengidentifikasi arah penelitian masa depan bagi para akademisi, praktisi, dan pembuat kebijakan.

Unduhan

Data unduhan belum tersedia.

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Unduhan

Diterbitkan

2026-06-30

Cara Mengutip

Kurniawan, I., Nugraha, N., & Sari, M. (2026). FINANCIAL FORECASTING AND MACHINE LEARNING: A BIBLIOMETRIC ANALYSIS OF GLOBAL RESEARCH TRENDS . Akurasi : Jurnal Studi Akuntansi Dan Keuangan, 9(1), 133–154. https://doi.org/10.29303/akurasi.v9i1.839

Terbitan

Vol 9 No 1 (2026): Jurnal Studi Akuntansi dan Keuangan, Juni 2026

Bagian

Articles

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Hak Cipta (c) 2026 Iwan Kurniawan, Nugraha Nugraha, Maya Sari

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