Rolling intrinsic for battery valuation in day-ahead and intraday markets ArXiv ID: 2510.01956 “View on arXiv” Authors: Daniel Oeltz, Tobias Pfingsten Abstract Battery Energy Storage Systems (BESS) are a cornerstone of the energy transition, as their ability to shift electricity across time enables both grid stability and the integration of renewable generation. This paper investigates the profitability of different market bidding strategies for BESS in the Central European wholesale power market, focusing on the day-ahead auction and intraday trading at EPEX Spot. We employ the rolling intrinsic approach as a realistic trading strategy for continuous intraday markets, explicitly incorporating bid–ask spreads to account for liquidity constraints. Our analysis shows that multi-market bidding strategies consistently outperform single-market participation. Furthermore, we demonstrate that maximum cycle limits significantly affect profitability, indicating that more flexible strategies which relax daily cycling constraints while respecting annual limits can unlock additional value. ...

Can Machine Learning Algorithms Outperform Traditional Models for Option Pricing? ArXiv ID: 2510.01446 “View on arXiv” Authors: Georgy Milyushkov Abstract This study investigates the application of machine learning techniques, specifically Neural Networks, Random Forests, and CatBoost for option pricing, in comparison to traditional models such as Black-Scholes and Heston Model. Using both synthetically generated data and real market option data, each model is evaluated in predicting the option price. The results show that machine learning models can capture complex, non-linear relationships in option prices and, in several cases, outperform both Black-Scholes and Heston models. These findings highlight the potential of data-driven methods to improve pricing accuracy and better reflect market dynamics. ...

Non-conservative optimal transport ArXiv ID: 2510.03332 “View on arXiv” Authors: Gabriela Kováčová, Georg Menz, Niket Patel Abstract Motivated by optimal re-balancing of a portfolio, we formalize an optimal transport problem in which the transported mass is scaled by a mass-change factor depending on the source and destination. This allows direct modeling of the creation or destruction of mass. We discuss applications and position the framework alongside unbalanced, entropic, and unnormalized optimal transport. The existence of optimal transport plans and strong duality are established. The existence of optimal maps are deduced in two central regimes, i.e., perturbative mass-change and quadratic mass-loss. For $\ell_p$ costs we derive the analogue of the Benamou-Brenier dynamic formulation. ...

One More Question is Enough, Expert Question Decomposition (EQD) Model for Domain Quantitative Reasoning ArXiv ID: 2510.01526 “View on arXiv” Authors: Mengyu Wang, Sotirios Sabanis, Miguel de Carvalho, Shay B. Cohen, Tiejun Ma Abstract Domain-specific quantitative reasoning remains a major challenge for large language models (LLMs), especially in fields requiring expert knowledge and complex question answering (QA). In this work, we propose Expert Question Decomposition (EQD), an approach designed to balance the use of domain knowledge with computational efficiency. EQD is built on a two-step fine-tuning framework and guided by a reward function that measures the effectiveness of generated sub-questions in improving QA outcomes. It requires only a few thousand training examples and a single A100 GPU for fine-tuning, with inference time comparable to zero-shot prompting. Beyond its efficiency, EQD outperforms state-of-the-art domain-tuned models and advanced prompting strategies. We evaluate EQD in the financial domain, characterized by specialized knowledge and complex quantitative reasoning, across four benchmark datasets. Our method consistently improves QA performance by 0.6% to 10.5% across different LLMs. Our analysis reveals an important insight: in domain-specific QA, a single supporting question often provides greater benefit than detailed guidance steps. ...

Spiking Neural Network for Cross-Market Portfolio Optimization in Financial Markets: A Neuromorphic Computing Approach ArXiv ID: 2510.15921 “View on arXiv” Authors: Amarendra Mohan, Ameer Tamoor Khan, Shuai Li, Xinwei Cao, Zhibin Li Abstract Cross-market portfolio optimization has become increasingly complex with the globalization of financial markets and the growth of high-frequency, multi-dimensional datasets. Traditional artificial neural networks, while effective in certain portfolio management tasks, often incur substantial computational overhead and lack the temporal processing capabilities required for large-scale, multi-market data. This study investigates the application of Spiking Neural Networks (SNNs) for cross-market portfolio optimization, leveraging neuromorphic computing principles to process equity data from both the Indian (Nifty 500) and US (S&P 500) markets. A five-year dataset comprising approximately 1,250 trading days of daily stock prices was systematically collected via the Yahoo Finance API. The proposed framework integrates Leaky Integrate-andFire neuron dynamics with adaptive thresholding, spike-timingdependent plasticity, and lateral inhibition to enable event-driven processing of financial time series. Dimensionality reduction is achieved through hierarchical clustering, while populationbased spike encoding and multiple decoding strategies support robust portfolio construction under realistic trading constraints, including cardinality limits, transaction costs, and adaptive risk aversion. Experimental evaluation demonstrates that the SNN-based framework delivers superior risk-adjusted returns and reduced volatility compared to ANN benchmarks, while substantially improving computational efficiency. These findings highlight the promise of neuromorphic computation for scalable, efficient, and robust portfolio optimization across global financial markets. ...

Quantifying Semantic Shift in Financial NLP: Robust Metrics for Market Prediction Stability ArXiv ID: 2510.00205 “View on arXiv” Authors: Zhongtian Sun, Chenghao Xiao, Anoushka Harit, Jongmin Yu Abstract Financial news is essential for accurate market prediction, but evolving narratives across macroeconomic regimes introduce semantic and causal drift that weaken model reliability. We present an evaluation framework to quantify robustness in financial NLP under regime shifts. The framework defines four metrics: (1) Financial Causal Attribution Score (FCAS) for alignment with causal cues, (2) Patent Cliff Sensitivity (PCS) for sensitivity to semantic perturbations, (3) Temporal Semantic Volatility (TSV) for drift in latent text representations, and (4) NLI-based Logical Consistency Score (NLICS) for entailment coherence. Applied to LSTM and Transformer models across four economic periods (pre-COVID, COVID, post-COVID, and rate hike), the metrics reveal performance degradation during crises. Semantic volatility and Jensen-Shannon divergence correlate with prediction error. Transformers are more affected by drift, while feature-enhanced variants improve generalisation. A GPT-4 case study confirms that alignment-aware models better preserve causal and logical consistency. The framework supports auditability, stress testing, and adaptive retraining in financial AI systems. ...

A Practitioner’s Guide to AI+ML in Portfolio Investing ArXiv ID: 2509.25456 “View on arXiv” Authors: Mehmet Caner Qingliang Fan Abstract In this review, we provide practical guidance on some of the main machine learning tools used in portfolio weight formation. This is not an exhaustive list, but a fraction of the ones used and have some statistical analysis behind it. All this research is essentially tied to precision matrix of excess asset returns. Our main point is that the techniques should be used in conjunction with outlined objective functions. In other words, there should be joint analysis of Machine Learning (ML) technique with the possible portfolio choice-objective functions in terms of test period Sharpe Ratio or returns. The ML method with the best objective function should provide the weight for portfolio formation. Empirically we analyze five time periods of interest, that are out-sample and show performance of some ML-Artificial Intelligence (AI) methods. We see that nodewise regression with Global Minimum Variance portfolio based weights deliver very good Sharpe Ratio and returns across five time periods in this century we analyze. We cover three downturns, and 2 long term investment spans. ...

AlphaSAGE: Structure-Aware Alpha Mining via GFlowNets for Robust Exploration ArXiv ID: 2509.25055 “View on arXiv” Authors: Binqi Chen, Hongjun Ding, Ning Shen, Jinsheng Huang, Taian Guo, Luchen Liu, Ming Zhang Abstract The automated mining of predictive signals, or alphas, is a central challenge in quantitative finance. While Reinforcement Learning (RL) has emerged as a promising paradigm for generating formulaic alphas, existing frameworks are fundamentally hampered by a triad of interconnected issues. First, they suffer from reward sparsity, where meaningful feedback is only available upon the completion of a full formula, leading to inefficient and unstable exploration. Second, they rely on semantically inadequate sequential representations of mathematical expressions, failing to capture the structure that determine an alpha’s behavior. Third, the standard RL objective of maximizing expected returns inherently drives policies towards a single optimal mode, directly contradicting the practical need for a diverse portfolio of non-correlated alphas. To overcome these challenges, we introduce AlphaSAGE (Structure-Aware Alpha Mining via Generative Flow Networks for Robust Exploration), a novel framework is built upon three cornerstone innovations: (1) a structure-aware encoder based on Relational Graph Convolutional Network (RGCN); (2) a new framework with Generative Flow Networks (GFlowNets); and (3) a dense, multi-faceted reward structure. Empirical results demonstrate that AlphaSAGE outperforms existing baselines in mining a more diverse, novel, and highly predictive portfolio of alphas, thereby proposing a new paradigm for automated alpha mining. Our code is available at https://github.com/BerkinChen/AlphaSAGE. ...

Efficient simulation of prices for European call options under Heston stochastic-local volatility model: a comparison of methods ArXiv ID: 2509.24449 “View on arXiv” Authors: Meng cai, Tianze Li Abstract The Heston stochastic-local volatility model, consisting of a asset price process and a Cox–Ingersoll–Ross-type variance process, offers a wide range of applications in the financial industry. The pursuit for efficient model evaluation has been assiduously ongoing and central to which is the numerical simulation of CIR process. Different from the weakly convergent noncentral chi-squared approximation used in 25, this paper considers two strongly convergent and positivity-preserving methods for CIR process under Lamperti transformation, namely, the truncated Euler method and the backward Euler method. It should be noted that these two methods are completely different. The explicit truncated Euler method is computationally effective and remains robust under high volatility, while the implicit backward Euler method provides high computational accuracy and stable performance. Numerical experiments on European call options are presented to show the superiority of different methods. ...

Exponential Hedging for the Ornstein-Uhlenbeck Process in the Presence of Linear Price Impact ArXiv ID: 2509.25472 “View on arXiv” Authors: Yan Dolinsky Abstract In this work we study a continuous time exponential utility maximization problem in the presence of a linear temporary price impact. More precisely, for the case where the risky asset is given by the Ornstein-Uhlenbeck diffusion process we compute the optimal portfolio strategy and the corresponding value. Our method of solution relies on duality, and it is purely probabilistic. ...