Finansal Analizde Yapay Zekânın Etkinliği: İstanbul Menkul Kıymetler Borsası 100 (İMKB 100) Üzerine Kanıtlar
DOI:
https://doi.org/10.63556/tisej.2025.1652Anahtar Kelimeler:
Yapay Zekâ- Derin Öğrenme Mimarisi (LTSM)- Makroekeonomik Göstergeler ve BIST 100- Finansal Öngörü- Logit-ProbitÖzet
Bu çalışmanın amacı, yapay zekâ alanında bir teknoloji olan yapay sinir ağlarına dayalı derin öğrenme tekniklerini kullanarak makroekonomik göstergelerin Borsa İstanbul (BIST 100) endeksi üzerindeki etkilerini analiz etmektir. BIST 100 endeksini tahmin etmek için yedi makroekonomik göstergenin (Para Arzı (M2), Üretici Fiyat Endeksi (ÜFE), Sanayi Üretim Endeksi (SÜE), Döviz Kuru (USD/TL), Tahvil Faiz Oranları, Brent Petrol Fiyatları ve Altın Fiyatları) Ocak 2001-Ocak 2022 dönemindeki aylık kapanış fiyatları kullanılmıştır. BIST 100 endeksi, Uzun Kısa-Dönem Hafıza (LSTM) dahil olmak üzere hem derin öğrenme modelleri hem de Logit ve Probit modelleri çerçevesinde tahmin edilmiş ve bu yöntemlerin tahmin performansları karşılaştırılmıştır. Analiz sonuçları, seçilen makroekonomik değişkenlerin, BIST 100endeksi üzerindeki etkilerinin COVID-19 pandemisi ve 2008 küresel finansal krizi de dahil olmak üzere kriz dönemlerinde yoğunlaştığını ortaya koymaktadır. Ayrıca, reel sektördeki değişimlerin sermaye sektörü üzerinde önemli bir etkiye sahip olduğu ve derin öğrenme modelinin finansal krizleri öngörmede daha başarılı olduğu görülmüştür. Analiz sonuçları, makroekonomik göstergelerin BIST 100 endeksi üzerindeki etkilerini analiz etmek için geliştirilen LSTM derin öğrenme mimarilerinin çok düşük hata seviyesine sahip olduğunu ve Logit-Probit regresyon modellerine göre daha etkin ve tatmin edici sonuçlar verdiğini göstermektedir. Bu sonuçlar gelişmekte olan piyasalara ve politika yapıcılara yol gösterici bilgiler sunmaktadır.
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