TÜRK BANKA HİSSELERİNİN FİYAT TAHMİNİ İÇİN YAPAY ZEKÂ DESTEKLİ ALGORİTMİK ALIM-SATIM

Abstract

Bu çalışma, Türkiye hisse senedi piyasası olan Borsa İstanbul (BIST) bünyesinde hisse senedi fiyat hareketlerini tahmin etmek ve alım-satım sinyalleri üretmek amacıyla tasarlanmış yapay zekâ (YZ) tabanlı bir algoritmik alım-satım sisteminin performansını incelemektedir. Model, MACD, OTT ve MOST göstergelerini bütünleştirmekte, FastForest algoritmasını kullanmaktadır. Çalışmada, BIST Bankacılık Endeksi’ne (XBANK) ait 2.000 günlük veri seti kullanılmış, verilerin %80’i eğitim, %20’si ise doğrulama amacıyla ayrılmış olup sistem, al, sat ve bekle sinyallerinin tahmininde %79’un üzerinde yönsel doğruluk elde etmiştir. Buna ek olarak, modelin sağlamlığını değerlendirmek amacıyla Random Forest ve Destek Vektör Makineleri (Support Vector Machine) algoritmalarıyla karşılaştırmalı deneyler gerçekleştirilmiş ve ana modelin performansının tutarlı olduğu doğrulanmıştır. Gerçek piyasa koşullarını yansıtmak amacıyla, %0,1 ile %0,2 arasında değişen işlem maliyetleri geriye dönük test (backtesting) senaryolarına dâhil edilmiştir. Maliyetler dikkate alındığında dahi stratejinin kârlılığını koruduğu gözlemlenmiş, bu durum modelin pratik uygulanabilirliğini ortaya koymuştur. Elde edilen bulgular, YZ destekli sistemlerin maliyetleri azaltarak ve işlem etkinliğini artırarak bireysel yatırımcıları destekleyebileceğine işaret etmektedir. Model, XBANK endeksini geride bırakarak Stock-1 ve Stock-2 banka hisseleri için sırasıyla %437,88 ve %459,25 toplam getiri sağlamıştır. Bu sonuçlar, YZ destekli modellerin gelişmekte olan ve dalgalı piyasalarda yatırım stratejilerinin etkinliğini artırma potansiyelini ortaya koymaktadır.

Keywords

• Yapay Zeka
• Algoritmik Alım-Satım
• Hisse Fiyatı Tahmini
• Teknik Göstergeler
• Borsa İstanbul

ARTIFICIAL INTELLIGENCE-SUPPORTED ALGORITHMIC TRADING FOR PRICE PREDICTION OF TURKISH BANK STOCKS

Abstract

This paper explores the performance of an artificial intelligence (AI)-based algorithmic trading system designed to predict stock price movements and generate trading signals in the Turkish stock market, Borsa İstanbul (BIST). The model integrates MACD, OTT, and MOST indicators and uses the FastForest algorithm, and was trained on 2,000 days of BIST Banking Index (XBANK) data. With 80% of data was allocated for training and 20% for validation, the system achieved over 79% directional accuracy in predicting buy, sell, and hold signals. In addition, comparative experiments with Random Forest and Support Vector Machine algorithms were conducted to assess robustness, confirming the consistency of the main model’s performance. To reflect real market trading conditions, transaction costs ranging between 0.1% and 0.2% were incorporated into back testing scenarios. The strategy remained profitable even under cost-adjusted settings, demonstrating its practical viability. The results suggest that AI-driven systems can support retail investors by reducing costs and increasing trading efficiency. The model outperformed the XBANK index by generating total returns of 437.88% and 459.25% for Stock-1 and Stock-2 bank shares, respectively. These findings underscore the potential of AI-supported models in enhancing investment strategies in emerging and volatile markets.

Keywords

• Artificial Intelligence
• Algorithmic Trading
• Stock Price Prediction
• Technical Indicators
• Borsa Istanbul

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