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Journals / Denetişim / 2026 / Sayı: 35

ARTIFICIAL INTELLIGENCE APPROACHES FOR DETECTING TRADE-BASED MANIPULATION IN SECURITIES MARKETS

MENKUL KIYMETLER PİYASASINDA İŞLEM BAZLI MANİPÜLASYONLARIN TESPİTİNDE YAPAY ZEKÂ YAKLAŞIMLARI

Nurullah Celal Uslu(Ticaret Bakanlığı)0000-0001-5404-447X
Fuat Akal(HACETTEPE ÜNİVERSİTESİ)0000-0003-2582-4180
Journal
Denetişim
Pages
389–405
Publication Date
2026-08-23
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings suggest that integrating manipulation patterns with advanced computational methods may contribute to the development of more effective market surveillance systems and manipulation detection frameworks.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study presents a literature review of trade-based manipulations in securities markets.
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Key Findings & Data:The first approach focuses on identifying specific manipulation types by analyzing trading patterns and investor relationships, whereas the second approach detects manipulation through market indicators such as returns, volatility, trading volume, and stock turnover rates.
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Conclusion & Impact:The findings suggest that integrating manipulation patterns with advanced computational methods may contribute to the development of more effective market surveillance systems and manipulation detection frameworks.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:İşlem Bazlı Manipülasyon · Menkul Kıymetler Piyasası · Makine Öğrenmesi

Abstract

This study presents a literature review of trade-based manipulations in securities markets. Trade-based manipulation types and the corresponding manipulative trading patterns are systematically classified and analyzed. In addition, previous studies on the detection of trade-based manipulations are reviewed, and the computational methods employed in these studies are comparatively evaluated. The review indicates that the literature generally follows two main approaches. The first approach focuses on identifying specific manipulation types by analyzing trading patterns and investor relationships, whereas the second approach detects manipulation through market indicators such as returns, volatility, trading volume, and stock turnover rates. Recent studies also demonstrate the increasing use of machine learning, deep learning, graph neural networks, and explainable artificial intelligence for detecting trade-based manipulations in financial markets. This study contributes to the literature by providing a holistic evaluation of trade-based manipulation types, manipulative trading patterns, and computational detection methods. The findings suggest that integrating manipulation patterns with advanced computational methods may contribute to the development of more effective market surveillance systems and manipulation detection frameworks.

Özet

Bu çalışmada, menkul kıymetler piyasalarında gerçekleştirilen işlem bazlı manipülasyonlar literatür incelemesi kapsamında ele alınmıştır. İşlem bazlı manipülasyon türleri ile bu türlerin gerçekleştirilmesinde kullanılan manipülatif işlem örüntüleri sistematik olarak sınıflandırılmıştır. Ayrıca, işlem bazlı manipülasyonların tespitine yönelik literatürde yer alan çalışmalar değerlendirilmiş ve kullanılan yöntemler karşılaştırmalı olarak analiz edilmiştir. Literatür incelemesi sonucunda çalışmaların genel olarak iki farklı yaklaşım benimsediği belirlenmiştir. Birinci yaklaşım, işlem örüntülerini ve yatırımcı ilişkilerini analiz ederek belirli manipülasyon türlerini doğrudan tespit etmeyi amaçlarken, ikinci yaklaşım manipülasyon sonucunda ortaya çıkan getiri, volatilite, işlem hacmi ve pay senedi devir hızı gibi göstergeleri kullanarak genel manipülasyon tanısı koymaktadır. İncelenen çalışmalar, son yıllarda finansal piyasalarda üretilen veri miktarındaki artışa paralel olarak işlem bazlı manipülasyonların tespitinde makine öğrenmesi, derin öğrenme, grafik sinir ağları ve açıklanabilir yapay zekâ yaklaşımlarının giderek daha yaygın kullanıldığını göstermektedir. Bu çalışma, işlem bazlı manipülasyon türlerini, manipülatif işlem örüntülerini ve literatürde kullanılan tespit yöntemlerini bütüncül bir yaklaşımla değerlendirmesi bakımından literatüre katkı sunmaktadır.

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Cite This Article

Uslu, N. C., Akal, F. (2026). ARTIFICIAL INTELLIGENCE APPROACHES FOR DETECTING TRADE-BASED MANIPULATION IN SECURITIES MARKETS. Denetişim, Sayı: 35, 389–405. https://doi.org/10.58348/denetisim.1975548

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