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Dergiler / Ekonomi Politika ve Finans Araştırmaları Dergisi / 2026 / Cilt: 9 Sayı: 1

Hisse Senedi Piyasasında Yön Bulma: Kayıptan Kaçınma, Eğilim Etkisi ve Çapa ve Ayarlama Yanlılığı ile Zenginlik Değişimi ve Ağ Etkileşimi Modellemesi

Navigating the Stock Market: Modeling Wealth Exchange and Network Interaction with Loss Aversion, Disposition Effect and Anchoring and Adjustment Bias

Ömür Saltık(Mersin Üniversitesi)0000-0001-8507-8971
Sayfa
88–122
Yayın Tarihi
2024-03-29
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Çalışmanın bulguları, ajanların sosyo-demografik ve psikolojik faktörlerini dikkate alarak, kayıp kaçınımı, mülkiyet etkisi ve çapa ve ayarlama yanlılığının etkileşimlerini gerçek dünyaya en yakın şekilde modellenebildiğini göstermektedir.

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Amaç & Hipotez:Bu çalışma, ajan tabanlı modellere meta-analizi entegre etmeyi ve yanlı ajan etkileşimlerine dair temel içgörüler sunmayı amaçlamaktadır.
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Temel Bulgular & Veri:Çapa, mülkiyet etkisi, kayıptan kaçınma ve diğer çeşitli davranışsal yanlılıkların piyasa dinamikleri ve yatırımcı kararları üzerindeki etkisini derinlemesine incelenmektedir.
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Sonuç & Katkı:Çalışmanın bulguları, ajanların sosyo-demografik ve psikolojik faktörlerini dikkate alarak, kayıp kaçınımı, mülkiyet etkisi ve çapa ve ayarlama yanlılığının etkileşimlerini gerçek dünyaya en yakın şekilde modellenebildiğini göstermektedir.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Kayıptan Kaçınma · Eğilim Etkisi · Çapa ve Ayarlama Yanlılığı

Özet

Bu çalışma, ajan tabanlı modellere meta-analizi entegre etmeyi ve yanlı ajan etkileşimlerine dair temel içgörüler sunmayı amaçlamaktadır. Çapa, mülkiyet etkisi, kayıptan kaçınma ve diğer çeşitli davranışsal yanlılıkların piyasa dinamikleri ve yatırımcı kararları üzerindeki etkisini derinlemesine incelenmektedir. Ajan tabanlı modeller kullanarak, piyasa senaryoları ve yatırımcı davranışlarının simülasyonlarını sunmakta ve bireysel kararların piyasa dinamikleri üzerindeki etkisini vurgulanmaktadır. Çalışmanın yenilikçi yaklaşımı, davranışsal finans teorilerini gerçek piyasa verileriyle bütünleştirmesinde yatmakta ve piyasa davranışlarının nüanslı bir analizini sunmaktadır. Bu çalışma, davranışsal finansa yeni bir perspektif katmakta ve piyasa dinamikleri ile yatırımcı davranışlarının daha iyi anlaşılması için ajan tabanlı modellerin kullanımını teşvik etmektedir, bu da finansal piyasa analizi ve politika oluşturmada yardımcı olabilir. Çalışma, meta-analizi ajan tabanlı modellere entegre etmek isteyen ve yanlı ajan davranışlarını incelemeyi sağlayacak temel bir altyapı sunmayı hedeflemektedir. Çalışmanın bulguları, ajanların sosyo-demografik ve psikolojik faktörlerini dikkate alarak, kayıp kaçınımı, mülkiyet etkisi ve çapa ve ayarlama yanlılığının etkileşimlerini gerçek dünyaya en yakın şekilde modellenebildiğini göstermektedir. Sonuçlar, insan davranışlarının portföy optimizasyonlarında daha doğru modellenebilmesine ve Genelleştirilmiş Yapay Zeka'nın finansal piyasalara yönelik uygulamalarının genişletilmesine yönelik uygulamalarda oldukça elverişli öngörüler sunmaktadır.

Abstract

This study aims to integrate meta-analysis into agent-based models and provide foundational insights into biased agent interactions. It delves deeply into the effects of various behavioral biases such as anchoring, disposition effect, loss aversion, and others on market dynamics and investor decisions. Using agent-based models, it presents simulations of market scenarios and investor behaviors, emphasizing the impact of individual decisions on market dynamics. The innovative approach of this study lies in integrating behavioral finance theories with real market data, offering a nuanced analysis of market behaviors. This work contributes a new perspective to behavioral finance and encourages the use of agent-based models to deepen our understanding of market dynamics and investor behaviors, which can be helpful in financial market analysis and policy-making. This study aims to provide a foundational framework for those looking to integrate meta-analysis into agent-based models and explore biased agent behaviors. The findings demonstrate the ability to model the interactions of loss aversion, disposition effect, and anchoring and adjustment bias taking into account agents' socio-demographic and psychological factors, as close to the real world as possible. The results offer highly favorable forecasts for modeling human behaviors more accurately in portfolio optimizations and for expanding the applications of Generalized Artificial Intelligence in financial market implementations.

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Bu Makaleye Atıf Yap

Saltık, Ö. (2024). Hisse Senedi Piyasasında Yön Bulma: Kayıptan Kaçınma, Eğilim Etkisi ve Çapa ve Ayarlama Yanlılığı ile Zenginlik Değişimi ve Ağ Etkileşimi Modellemesi. Ekonomi Politika ve Finans Araştırmaları Dergisi, Cilt: 9 Sayı: 1, 88–122. https://doi.org/10.30784/epfad.1435009

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