Author

Sara Elhishi

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Open access Sep 2026

Integrating macroeconomic and technical indicators into forecasting the stock market: a hybrid approach for efficient feature selection

Stock market prediction is a significant research topic in the financial sector and has been widely investigated by researchers and investors. Forecasting stock markets is challenging because of the nonlinearity, volatility, and dynamics of the data. Additionally, stock markets are affected by various internal and external factors. While most previous studies relied on historical prices and technical indicators (TIs), they ignored the influence of overall economic factors on stock markets. In contrast, this study introduces a robust framework to predict daily log returns by leveraging a combined dataset comprising historical data, TIs, and macroeconomic data, including gold and oil prices, the volatility index, the dollar index, the interest rate, the 10-year Treasury yield, the term spread, and the dividend yield. Moreover, we propose a hybrid feature selection (FS) approach that combines filter and wrapper methods, unlike in previous studies, which relied primarily on a single FS approach or neglected it entirely. The dataset represents diverse sectors and firm sizes, covering five companies: Apple (AAPL Inc.), Exxon Mobil Corporation (XOM), Goldman Sachs (GS), Pfizer Inc. (PFE), and Ford Motor Company (F). We apply a hybrid evaluation approach that incorporates 5-fold time series cross-validation (TSCV) with a holdout test set to evaluate the efficiency of the proposed models. The results revealed that despite modest improvements in statistical metrics, FS significantly enhanced the economic performance of the trading strategy, as demonstrated by better returns and Sharpe ratios. The results show that deep learning (DL) models that combine macroeconomic data and the FS process, in addition to historical data and TIs, achieved higher trading returns and produced superior risk-adjusted performance, although they demonstrated slightly higher forecast errors than traditional models. This confirms that statistical accuracy alone is insufficient for evaluating financial forecasting models. This work highlights the benefits of the proposed framework for predicting stock returns across different markets, offering significant insights for financial analysts.

Aya Nabil, S. Barakat, Ahmed Aboelfetouh et al. · Scientific Reports · 0 citations

Open access 2026

A Systematic Ablation Study of Multilingual Transformer Architectures for Sarcasm Detection: Architectural Design Versus Feature Engineering

Sarcasm detection remains a challenging task in natural language processing due to the complex interaction between linguistic and pragmatic cues. While transformer-based models have shown encouraging performance, it is not yet clear whether improvements stem primarily from architectural design or explicit feature engineering. This work introduces a systematic ablation study of three multilingual transformer architectures (mBERT, mDeBERTa-v3, and XLM-RoBERTa) evaluated on HeteroSarc-47K, a curated multi-domain dataset containing 47,694 instances across six heterogeneous domains, including social media and dialectal Arabic text. We isolate the empirical impact of two feature augmentation techniques: emoji integration and contrastive pre-training. Our experiments reveal that while contrastive pre-training increases sarcasm recall for mBERT, it induces a significant drop in its overall accuracy, demonstrating architecture-specific sensitivities. In contrast, architectural upgrades alone consistently yield robust improvements; mDeBERTa-v3 achieves the highest performance (72.79% sarcasm recall and 87.20% accuracy) without requiring any feature augmentations, outperforming the baseline mBERT. Furthermore, per-class analysis reveals a persistent performance gap between non-sarcastic and sarcastic classes across all domains, highlighting that class imbalance remains a critical challenge independent of architecture. Ultimately, these findings provide a vital practical takeaway for practitioners: upgrading to inherently stronger multilingual architectures yields significantly higher and more robust performance returns compared to investing in extensive feature-engineering pipelines.

A. G. El-Belgehy, Hazem M. El-Bakry, Samir Abdelrazek et al. · IEEE Access · 0 citations