Browsing by Subject "Algorithmic trading"

Now showing 1 - 3 of 3

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 Novin
Financial 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.
Open Access
Forecasting high-frequency stock returns: a comparison of alternative methods
(Springer, 2022-06) Akyıldırım, E.; Bariviera, A.; Nguyen, D. K.; Şensoy, Ahmet
We compare the performance of various advanced forecasting techniques, namely artificial neural networks, k-nearest neighbors, logistic regression, Naïve Bayes, random forest classifier, support vector machine, and extreme gradient boosting classifier to predict stock price movements based on past prices. We apply these methods with the high frequency data of 27 blue-chip stocks traded in the Istanbul Stock Exchange. Our findings reveal that among the selected methodologies, random forest and support vector machine are able to capture both future price directions and percentage changes at a satisfactory level. Moreover, consistent ranking of the methodologies across different time frequencies and train/test set partitions prove the robustness of our empirical findings.
Open Access
Intraday efficiency-frequency nexus in the cryptocurrency markets
(Elsevier, 2020) Aslan, Aylin; Şensoy, Ahmet
This study investigates the nexus between weak-form efficiency and intraday sampling frequency for the highest capitalized cryptocurrencies. Applying a battery of long memory tests, we provide evidence of major discrepancies on the predictability of cryptocurrency returns for alternative high frequency intervals. Accordingly, efficiency demonstrates a U-shaped pattern with respect to alternative sampling frequencies, hence there exists an optimal intraday sampling frequency that maximizes the market efficiency. These findings have important implications for portfolio analysis, risk management, regulations and administrative rulings in the cryptocurrency markets.