Belirsiz kriter ağırlıkları altında yeni bir ÇKKV yöntemi: Yapay zekâ sohbet robotlarına (ChatGPT4, Copilot, Gemini) dayalı portföy seçimi üzerine bir uygulama

Year 2024, Volume: 26 Issue: Özel Sayı, 68 - 80

Furkan Göktaş Fatih Güçlü

https://doi.org/10.33707/akuiibfd.1454952

Abstract

Çok kriterli karar verme (ÇKKV) problemlerinin en tartışmalı noktası kriter ağırlıklandırmadır. Çünkü farklı kriter ağırlıkları genellikle farklı sonuçların ortaya çıkmasına neden olur. Bu çalışmanın amacı kriter ağırlıklarının belirsiz olduğu durumda ÇKKV problemlerini çözebilmek için yeni bir yöntem geliştirmektir. Bu kapsamda bu çalışmada Belirsiz Kriter Ağırlıklarıyla Olabilirlik Değerlendirme Sistemi (U-PES) önerilmiştir. Uzman bilgisinden (yapay zekâ sohbet robotlarından) ve geçmiş veriden yararlanılarak Borsa İstanbul’da işlem gören sekiz adet hisse senedi ile portföy oluşturmada U-PES kullanılmıştır. Buradaki kriterler; beklenen getiri, standart sapma ve Çevresel-Sosyal-Kurumsal Yönetim (ESG) bileşenleri olarak belirlenmiştir. Yapılan uygulamada uzman bilgisi ya da geçmiş veri ile elde edilen sonuçlar arasında genellikle pozitif ama yüksek düzeyde olmayan ilişki olduğu bulunmuştur.

Keywords

Çok kriterli karar verme, portföy seçimi, sürdürülebilirlik, yapay zeka, ChatGPT

A novel MCDM method under uncertain criteria weights: An application on portfolio selection based on artificial intelligence chatbots (ChatGPT4, Copilot, Gemini)

Abstract

The most controversial aspect of multi-criteria decision-making (MCDM) problems is criteria weighting. Because different criteria weights generally lead to different results. This study aims to develop a new method for solving MCDM problems with uncertain criteria weights. In this context, we propose the Possibilistic Evaluation System with Uncertain Criteria Weights (U-PES) in this study. We use U-PES to form a portfolio with eight stocks listed in Borsa Istanbul using expert knowledge (artificial intelligence chatbots) and historical data. Here, we determine the criteria as expected return, standard deviation, and Environmental-Social-Governance (ESG) components. In the application, we observe generally positive but not high-level relationships between the results obtained with expert knowledge or historical data.

Keywords

Multi-criteria decision making, portfolio selection, sustainability, artificial intelligence, ChatGPT

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