arXiv subjects

Aliaa Alnaggar

Publications and source records attributed to Aliaa Alnaggar.

3 recordsLinked to original sources

arXiv2026

Forecasting Intraday USD/CAD Exchange Rate with News-Derived Monetary-Policy Signals

Monetary-policy announcements and central-bank communications play a central role in foreign exchange markets, yet their qualitative, unstructured form makes their forecasting value difficult to quantify. While prior research has largely focused on sentiment extracted from financial news, comparatively little is known about the relative contribution of different dimensions of monetary-policy communication. Existing studies primarily evaluate whether textual information improves overall forecasting performance but provide limited insight into which communication channels drive such improvements. To address this gap, this paper introduces a statistical attribution methodology that decomposes monetary-policy communication into interpretable channels and quantifies their incremental forecasting contribution under false-discovery-rate control. Monetary-policy news is transformed into structured communication signals using large language models (LLMs) and temporal feature engineering. These signals are evaluated using rolling-window experiments with tree-based machine-learning models. The results show that monetary-policy communication contains measurable predictive information. Attribution analysis shows that predictive value is concentrated in a small subset of signals, with communication timing providing the strongest individual feature-level contribution, targeted communication-activity measures also contributing positively, and LLM-derived sentiment providing complementary information at the group level. The findings indicate that communication-based forecasting value extends beyond sentiment alone and that attribution, rather than aggregate accuracy alone, is central to evaluating news-derived signals.

cs.AI↗

arXiv2026

Dynamic Dispatching for Time-Sensitive Blood Sample Collection and Delivery

Hospitals and diagnostic laboratories rely on couriers to collect blood samples from geographically dispersed collection centres and deliver them for analysis before their short viability windows expire; late deliveries force costly re-collection and can delay diagnosis. We study the real-time dispatching of such a courier fleet, in which sample requests arrive stochastically at the centres throughout the day and a central dispatcher must repeatedly decide which vehicles to send, which centres each should visit, and whether to collect urgent samples immediately or consolidate them into later trips, subject to hard delivery deadlines and vehicle capacity limits. Unlike static planning models, which fix routes before demand is known, and reactive heuristics, which respond only to the current backlog, our approach anticipates future arrivals when weighing immediate collection against consolidation. We formulate the problem as a Markov decision process and develop a neural approximate dynamic programming framework for centralized dispatch. The method introduces a dual value function decomposition that separately represents vehicle states and collection-centre states through neural networks trained on post-decision states. These learned estimates are integrated through a matching formulation that selects dispatch actions while balancing supply availability, demand urgency, and downstream opportunity cost. Computational experiments on a realistic Greater Toronto Area network compare the proposed policy with myopic baselines and ablation variants. Results show that the proposed dual value function policy raises the share of sample volume delivered on time by 1 to 9 percentage points over myopic baselines, with the largest gains under tight fleet, capacity, deadline, and routing constraints; these on-time gains, in turn, reduce reliance on costly external couriers.

math.OC↗

arXiv2024

Distributionally Robust Hospital Capacity Expansion Planning under Stochastic and Correlated Patient Demand

This paper investigates the optimal locations and capacities of hospital expansion facilities under uncertain future patient demands, considering both spatial and temporal correlations. We propose a novel two-stage distributionally robust optimization (DRO) model that integrates a Spatio-Temporal Neural Network (STNN). Specifically, we develop an STNN model that predicts future hospital occupancy levels considering spatial and temporal patterns in time-series datasets over a network of hospitals. The predictions of the STNN model are then used in the construction of the ambiguity set of the DRO model. To address computational challenges associated with two-stage DRO, we employ the linear-decision-rules technique to derive a tractable mixed-integer linear programming approximation. Extensive computational experiments conducted on real-world data demonstrate the superiority of the STNN model in minimizing forecast errors. Compared to neural network models built for each individual hospital, the proposed STNN model achieves a 53% improvement in average RMSE. Furthermore, the results demonstrate the value of incorporating spatiotemporal dependencies of demand uncertainty in the DRO model, as evidenced by out-of-sample analysis conducted with both ground truth data and under perfect information scenarios.

math.OC↗