Browsing by Author "Koy, Ayben"

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Federal Reserve Interest Rates, Investment Behavior, and Arbitrage in Exchange Traded-Funds
(Conscientia Beam, 2026) Sobati, Pegah; Koy, Ayben; Colak, Andac Batur
This study investigates the influence of U.S. Federal Reserve interest rate policy on investor behavior, liquidity, and arbitrage efficiency in 29 iShares exchange-traded funds (ETFs) spanning large-, mid-, and small-cap benchmarks from 2013 to 2024. Using weekly data and econometric techniques combining time-series and panel approaches, the analysis incorporates key macroeconomic indicators, interest rates, the U.S. dollar index, economic activity measures, and market volatility to assess their combined effect on ETF market dynamics. Findings show that interest rate shifts significantly influence asset allocation, sector preferences, and risk tolerance, with higher rates often strengthening the dollar and increasing the appeal of fixed-income assets. Active trading is associated with narrower bid-ask spreads through enhanced liquidity, while passive investment widens spreads. ETF volatility is positively related to spreads, reflecting increased uncertainty and transaction costs in turbulent markets. The results provide empirical evidence on the behavioral channels linking monetary policy, market conditions, and trading efficiency, offering implications for policymakers, asset managers, and market participants. The major contribution of the study is to the empirical finance literature by integrating time-series and panel econometric methods to quantify the joint effects of interest rate policy, macroeconomic indicators, and investor sentiment on ETF market microstructure. Findings offer statistically robust insights into liquidity formation, volatility transmission, and arbitrage efficiency in diversified ETF markets.
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.