Browsing by Subject "Fintech"
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Item Embargo Assessing the US financial sector post three bank collapses: Signals from fintech and financial sector ETFs(Elsevier BV, 2023-10-12) Banerjee, A. K.; Pradhan, H. K.; Şensoy, Ahmet; Goodell, J. W.We investigate the effects of the collapses of Silicon Valley Bank, Signature Bank, and First Republic Bank on the US financial sector by analysing returns and second moments of traditional financial and fintech ETFs. Using a network model, we examine high-frequency data sampled at one-hour intervals for seventeen ETFs encompassing pre- and crisis periods. We find, using a time-varying parametric vector autoregressive (TVP-VAR) and volatility impulse response analysis, that traditional financial ETFs are net transmitters of returns and volatility spillovers in the network, and that this impact is more pronounced in volatility in the period coinciding with the collapse of the three big banks. We identify effects persisting through the medium term. This study is among the first to comprehensively analyze the recent crisis in the US banking sector, covering a full range of the fall of three big banks.Item Open Access Big data analytics, order imbalance and the predictability of stock returns(Elsevier, 2021-09-24) Akyildirim, E.; Şensoy, Ahmet; Gulay, G.; Corbet, S.; Salari, Hajar NovinFinancial institutions have adopted big data to a considerable extent to provide better investment decisions. Consequently, high-frequency algorithmic traders use a vast amount of historical data with various statistical models to maximize their trading profits. Until recently, high-frequency algorithmic trading was the domain of institutional traders with access to supercomputers. Nowadays, any investor can potentially make high-frequency trades because of easy access to big data and software to analyze and execute trades. With that in mind, Borsa Istanbul introduced real time big data analytics as a product to its customers. These analytics are derived in real time from order book and trade data and aim to level the playing field between investment firms and retail traders. Using classical benchmark models in the literature, we show that Borsa Istanbul’s order imbalance-based data analytics are useful in predicting both time-series and cross-sectional intraday excess future returns, proving that this product is extremely beneficial to market participants, particularly day traders.