Koy, Ayben

Profile Picture
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

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Scholarly Output

4

Articles

2

WoS Citation Count

0

Scopus Citation Count

0

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0

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2021 - 20254
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Now showing 1 - 4 of 4
  • Thumbnail Image
    Article
    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.
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    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.
  • Thumbnail Image
    Editorial
    Preface
    (Peter Lang AG, 2021) Akincilar Köseoğlu, N.; Apak, D.; Khan, Shad Ahmad; Koy, Ayben; Kajla, Tanveer; Rani, Chandni; Kansra, Pooja
  • Thumbnail Image
    Book
    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.