Koy, Ayben

Profile URL
https://hdl.handle.net/20.500.14627/1278
Name Variants
Job Title
Prof. Dr.
Email Address
ayben.koy@fbu.edu.tr
Main Affiliation
İŞLETME BÖLÜMÜ
Status
Current Staff
Website
ORCID ID
0000-0002-2506-6634
Scopus Author ID
57202917644
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
AAE-3565-2019

Research Topics

Domains
Social Sciences
Fields
Economics, Econometrics and FinanceDecision Sciences
Subfields
Economics and EconometricsGeneral Economics, Econometrics and FinanceFinanceManagement Science and Operations Research
Specific Research Areas
Market Dynamics and Volatility
Monetary Policy and Economic Impact
Financial Markets and Investment Strategies
Complex Systems and Time Series Analysis
Stock Market Forecasting Methods

Sustainable Development Goals

SDG data is not available

Publication Collaboration

Affiliation Name Count
Istanbul Commerce University 58
Istanbul Arel University 3
Marmara University 3
Cyprus International University 2
Niğde Ömer Halisdemir Üniversitesi 2
1 / 5
Data obtained from OpenAlex
Scholarly Output

5

Articles

3

WoS Citation Count

1

Scopus Citation Count

2

Filters

2025 - 20265

Settings

Author of Publication: search.filters.isAuthorOfPublication.d8e22fc5-6193-47ff-81a8-e4026c7d66bf

Scholarly Output Search Results

Now showing 1 - 5 of 5
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Google Trend Index as an Investor Sentiment Proxy in Cryptomarket: Nonlinear Relationships With Cryptomarket and Predicting Bitcoin Returns With Machine Learning Approach
    (Springer, 2025) Koy, Ayben; Demir, Semra; Colak, Andac Batur
    This study investigates the utility of Google trend indices as proxies of investor sentiment, examining their relationships with cryptocurrency market prices and their potential for return prediction. Employing several nonlinear econometric models including the momentum threshold autoregressive AR (MTAR), Kapetanios, Shin, and Snell, and exponential smooth transition autoregressive vector error correction model, the research the relationships between Google trend indices and BTC prices. Additionally, the study evaluates the performance of three developed artificial neural network models in predicting bitcoin returns based on investor sentiment derived from Google trend indices. The findings highlight that the MTAR model effectively captures significant relationships between the variables studied. However, predicting bitcoin returns remains challenging due to their typically small values, which represent the changes between observation points.
  • Article
    İşletmelerin Kurumsal Yönetim Uygulamalarının Finansal Performans Açısından İncelenmesi: Borsa İstanbul Kurumsal Yönetim Endeksi Şirketleri Üzerine Bir Uygulama
    (2025) Koy, Ayben; Güngör, Mehmet Yusuf
    This study examines the impact of corporate governance on financial performance in firms listed on the BIST Corporate Governance Index (BIST XKURY) over the 2010–2022 period using panel data analysis. The significance of the research lies not only in investigating this relationship but also in analyzing the role of female representation on board of directors. Corporate governance is evaluated across four dimensions: shareholders, transparency, stakeholders, and the board of directors. Financial performance is measured through ROA, ROE, and EPS, with firm size and leverage included as control variables. The sample is divided by a 25% threshold of female board members. The findings indicate that in firms with ≥25% female representation, the link between board effectiveness and profitability is stronger, highlighting the performance-enhancing role of gender diversity.
  • Editorial
    Preface
    (Peter Lang AG, 2025) Akincilar Köseoğlu, N.; Apak, D.; Khan, Shad Ahmad; Koy, Ayben; Kajla, Tanveer; Rani, Chandni; Kansra, Pooja
  • Book
    Citation - Scopus: 1
    Turning Human Resource Analytics Into Actionable Strategies
    (IGI Global, 2025) Khan, Shad Ahmad; Koy, A.; Rani, C.; Kansra, P.; Kajla, T.
    In today's data-driven workplace, the ability to harness unstructured text data is reshaping how organizations manage their human capital. Natural Language Processing (NLP) empowers HR professionals to extract insights from employee communications, feedback, and performance reviews, turning qualitative input into strategic decision-making tools. By improving areas such as recruitment, engagement, and retention, NLP enhances both employee experiences and organizational efficiency. Its application bridges the gap between traditional HR practices and advanced analytics, enabling more informed, proactive, and people-centered approaches. This integration of technology and human insight marks a transformative shift in the way businesses understand and support their workforce. Turning Human Resource Analytics Into Actionable Strategies emphasizes transforming raw textual data into actionable intelligence that enhances recruitment processes, improves employee engagement strategies, and optimizes organizational decision-making. It explores innovative approaches to effectively understand, manage, and leverage human capital in today's data-driven business environment. Covering topics such as forecasting workforce needs, job satisfaction, and recommendation systems, this book is an excellent resource for HR managers, recruiters, performance managers, employee engagement professionals, business leaders, data analysts, professionals, researchers, scholars, academicians, and more. © 2026 by IGI Global Scientific Publishing. All rights reserved.
  • Article
    KOBİ'lerde Ar-Ge Yoğunluğunun Özsermaye Kârlılığına Etkisi: Türkiye Panel Veri Analizi (2016-2023)
    (2026) Dikili, Kadir; Koy, Ayben
    Amaç: Bu çalışma, KOSGEB veri tabanında kayıtlı Ar-Ge yapan küçük ve orta büyüklükteki işletmelerde (KOBİ) Ar-Ge yoğunluğunun özsermaye kârlılığı (ROE) üzerindeki cari ve gecikmeli etkilerini incelemeyi amaçlamaktadır. Ar-Ge harcamalarının kısa ve orta vadedeki kârlılık etkisinin KOBİ ölçeğinde sistematik biçimde test edilmesi araştırmanın temel sorusunu oluşturmaktadır.Yöntem: 2014–2023 dönemini kapsayan ham veri setinden türetilen ve etkin analiz dönemi 2016–2023 olan 738 firmaya ait 4.903 firma-yıl gözleminden oluşan panel veri seti kullanılmıştır. Ar-Ge yoğunluğunun cari ve iki yıla kadar gecikmeli etkileri dağıtılmış gecikme modeli çerçevesinde test edilmiş; İki Yönlü Sabit Etkiler modeli ve Driscoll-Kraay robust standart hatalar tercih edilmiştir.Bulgular: Cari yıl Ar-Ge yoğunluğu özsermaye kârlılığını anlamlı biçimde olumsuz etkilemektedir. Bir yıl gecikmeli Ar-Ge yoğunluğu ise kârlılık üzerinde pozitif ve anlamlı etki yaratmaktadır. İki yıl gecikmeli etki istatistiksel anlam taşımamaktadır. Özgünlük: Çalışma, Ar-Ge ve kârlılık ilişkisini halka açık firmalar yerine KOSGEB veri tabanında yer alan KOBİ'ler üzerinden inceleyerek literatürdeki önemli bir ampirik boşluğu doldurmaktadır. Ayrıca, Ar-Ge yoğunluğunun etkilerini yalnızca cari dönemle sınırlı tutmayıp dağıtılmış gecikme modeli çerçevesinde dinamik olarak ayrıştırması, kısa ve orta vadeli etkilerin farklı yönlerde ortaya çıkabileceğini göstermesi bakımından özgün bir katkı sunmaktadır.