In this study, deep learning-based binary classification models were developed and compared in order to predict the closing direction (up/down) of Delta Air Lines (DAL) stock on the next business day. Three feature sets including technical indicators, competitor airline stocks, and market/sector representatives were derived from daily data for the period of April 30, 2015–April 17, 2026. Nine experiments were conducted with Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) architectures. The highest accuracy and F1 score were obtained by the MLP model using only DAL technical indicators (accuracy = 52.74%; F1 = 0.489), whereas the GRU model using only DAL technical indicators produced the marginally highest ROC-AUC (0.525). However, the performance values remained only slightly above the simple comparison benchmark; competitor and market variables did not provide a consistent improvement. The findings indicate that past price/volume-based technical indicators had limited predictive value for next-day direction prediction. Within the examined stock, period, feature space, and experimental design, the results provide no evidence against the weak-form Efficient Market Hypothesis. Thus, the study points to the limits of the examined technical analysis-based approach.
Alperen Aydın, Murat Işık, Mehmet Ali Yalçınkaya· Information Technology in Ec...· 0 citations
Urban noise pollution is a critical public health problem affecting millions of people around the world. Current acoustic monitoring systems largely focus on optimizing classification accuracy but fail to meet operational requirements such as calibrated probability outputs, deployment formats, and spatiotemporal integration. This study presents a comprehensive urban noise monitoring pipeline extending from raw sensor data to city-scale policy support. The proposed framework uses a CNN14-based ensemble architecture on the SONYC-UST dataset collected from 56 acoustic sensors deployed across New York City. The system performs simultaneous multi-source detection across 35 hierarchical acoustic presence labels and provides reliable probability calibration validated by a Brier score of 0.1269. In terms of technical infrastructure, the pipeline offers export in TorchScript format that can run on edge devices without Python dependency, processing capacity of more than 2,000 audio segments per second on an NVIDIA GeForce RTX 3060 and training time of less than six hours per fold. Integrated 250-meter spatial grid mapping and hourly-daily temporal aggregation modules enable direct integration of predictions into urban planning workflows. Independent external corroboration was performed using NYC 311 complaint records across 3,460 matched grid-day observations. Permutation testing revealed a statistically significant relationship between predicted noise scores and community-reported disturbance. This study demonstrates that model accuracy alone is not sufficient for deployable urban noise monitoring systems, and that a holistic approach encompassing calibration, deployment, and spatial integration is required.
Mehmet Ali Yalçınkaya· Scientific Reports· 0 citations
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