Browsing by Author "Benli, Harun"

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    Attention-Enhanced Dual-Head LSTM With Rich Feature Engineering for Risk-Adjusted Stock Return Forecasting
    (Institute of Electrical and Electronics Engineers Inc., 2025) Patel J.; Gunes P.; Ertugrul S.; Sayar A.; Benli H.; Makaroglu D.; Cakar T.; Benli, Harun; Gunes, Peri; Patel, Jay; Makaroglu, Didem; Sayar, Alperen; Cakar, Tuna; Ertugrul, Seyit
    Stock return forecasting is a challenging task due to the complex, nonlinear, and volatile nature of financial markets. In this paper, we propose a comprehensive deep learning framework that integrates: a two-layer Long Short-Term Memory (LSTM) network augmented with a learnable attention mechanism, a dual-head output for simultaneous regression of next-day returns and classification of price direction, with an extensive suite of technical and macro-financial features. Our feature set comprises lagged log-returns, trend indicators (simple and exponential moving averages), momentum oscillators (RSI, MACD), volatility measures (rolling variance and GARCH conditional volatility), price bands (Bollinger Bands, Donchian channels), volume metrics (On-Balance Volume, Volume Rate of Change), Hidden Markov Model regime states, market index returns, and calendar effects. We train and validate the model using a rolling-window cross-validation scheme with early stopping and hyperparameter tuning to ensure temporal robustness. Empirical results on a large multi-stock dataset demonstrate that our attention-enhanced, dual-task LSTM outperforms single-task LSTMs and traditional machine learning benchmarks, achieving lower forecasting error and more stable generalization. © 2025 IEEE.
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    Financial Inputs Prediction with Machine Learning and Covariance Matrix Applications
    (Institute of Electrical and Electronics Engineers Inc., 2025) Benli, Harun; Gunes, Peri; Ulkgun, Mert; Cakar, Tuna
    Reliable estimation of the time-varying covariance matrix of asset returns is indispensable for portfolio construction, risk budgeting, and automated advisory services. Conventional estimators-rolling-window sample covariances, EWMA filters, and GARCH families-remain anchored to historical prices and therefore adapt slowly when market conditions pivot. To overcome this latency, we propose an end-to-end, machine-learning-driven framework that forecasts future covariances directly from high-frequency equity data, largely decoupling risk estimation from past observations. Our pipeline ingests heterogeneous stock feeds through a real-time API, applies error-minimising imputation (forward/backward fill, spline, VAR, wavelet, and co-kriging), and standardises returns via empirically selected scaling schemes. The processed features are then fed to a model zoo comprising linear and penalised regressions, tree ensembles (Random Forest, XGBoost, LightGBM, CatBoost), and kernel-based Support Vector Regression. Weekly walk-forward evaluation on a universe of Turkish insurance equities shows that LightGBM and SVR cut out-of-sample covariance prediction error by up to 35 % versus classical benchmarks. We embed the predicted matrices into five allocation engines-Markowitz mean-variance, Black-Litterman, minimum-variance, Risk Parity, and CVaR optimisation-demonstrating that Risk Parity delivers the most consistent variance reduction across 15 stock pairs, while Black-Litterman excels for idiosyncratic combinations such as ANSGR-AKGRT. A granular analysis reveals that day-to-day sign changes in returns create structural breaks that generic regressors miss; augmenting the architecture with a volatility-state classifier and regime-specific learners markedly sharpens turning-point detection. Beyond statistical gains, the framework is production-ready: it is fully implemented in Python, runs on cloud notebooks, and plugs into robo-advisory dashboards. The study thus bridges academic advances in covariance prediction with operational portfolio management, paving the way for broader cross-sector deployment and future research on deep sequential models, transaction-cost awareness, and multi-asset scalability. © 2025 Elsevier B.V., All rights reserved.