Hybrid Quantum-Classical Ensemble Learning for S&P 500 Directional Prediction ArXiv ID: 2512.15738 “View on arXiv” Authors: Abraham Itzhak Weinberg Abstract Financial market prediction is a challenging application of machine learning, where even small improvements in directional accuracy can yield substantial value. Most models struggle to exceed 55–57% accuracy due to high noise, non-stationarity, and market efficiency. We introduce a hybrid ensemble framework combining quantum sentiment analysis, Decision Transformer architecture, and strategic model selection, achieving 60.14% directional accuracy on S&P 500 prediction, a 3.10% improvement over individual models. Our framework addresses three limitations of prior approaches. First, architecture diversity dominates dataset diversity: combining different learning algorithms (LSTM, Decision Transformer, XGBoost, Random Forest, Logistic Regression) on the same data outperforms training identical architectures on multiple datasets (60.14% vs.\ 52.80%), confirmed by correlation analysis ($r>0.6$ among same-architecture models). Second, a 4-qubit variational quantum circuit enhances sentiment analysis, providing +0.8% to +1.5% gains per model. Third, smart filtering excludes weak predictors (accuracy $<52%$), improving ensemble performance (Top-7 models: 60.14% vs.\ all 35 models: 51.2%). We evaluate on 2020–2023 market data across seven instruments, covering diverse regimes including the COVID-19 crash and inflation-driven correction. McNemar’s test confirms statistical significance ($p<0.05$). Preliminary backtesting with confidence-based filtering (6+ model consensus) yields a Sharpe ratio of 1.2 versus buy-and-hold’s 0.8, demonstrating practical trading potential. ...

Market Reactions and Information Spillovers in Bank Mergers: A Multi-Method Analysis of the Japanese Banking Sector ArXiv ID: 2512.06550 “View on arXiv” Authors: Haibo Wang, Takeshi Tsuyuguchi Abstract Major bank mergers and acquisitions (M&A) transform the financial market structure, but their valuation and spillover effects remain open to question. This study examines the market reaction to two M&A events: the 2005 creation of Mitsubishi UFJ Financial Group following the Financial Big Bang in Japan, and the 2018 merger involving Resona Holdings after the global financial crisis. The multi-method analysis in this research combines several distinct methods to explore these M&A events. An event study using the market model, the capital asset pricing model (CAPM), and the Fama-French three-factor model is implemented to estimate cumulative abnormal returns (CAR) for valuation purposes. Vector autoregression (VAR) models are used to test for Granger causality and map dynamic effects using impulse response functions (IRFs) to investigate spillovers. Propensity score matching (PSM) helps provide a causal estimate of the average treatment effect on the treated (ATT). The analysis detected a significant positive market reaction to the mergers. The findings also suggest the presence of prolonged positive spillovers to other banks, which may indicate a synergistic effect among Japanese banks. Combining these methods provides a unique perspective on M&A events in the Japanese banking sector, offering valuable insights for investors, managers, and regulators concerned with market efficiency and systemic stability ...

Thermodynamic description of world GDP distribution over countries ArXiv ID: 2512.06420 “View on arXiv” Authors: Klaus M. Frahm, Dima L. Shepelyansky Abstract We apply the concept of Rayleigh-Jeans thermalization of classical fields for a description of the world Gross Domestic Product (GDP) distribution over countries. The thermalization appears due to a variety of interactions between countries with conservation of two integrals being total GDP and probability (norm). In such a case there is an emergence of Rayleigh-Jeans condensation at states with low GDP. This phenomenon has been studied theoretically and experimentally in multimode optical fibers and we argue that it is at the origin of emergence of poverty and oligarchic phases for GDP of countries. A similar phenomenon has been discussed recently in the framework of the Wealth Thermalization Hypothesis to explain the high inequality of wealth distribution in human society and companies at Stock Exchange markets. We show that the Rayleigh-Jeans thermalization well describes the GDP distribution during the last 50 years. ...

Wealth or Stealth? The Camouflage Effect in Insider Trading ArXiv ID: 2512.06309 “View on arXiv” Authors: Jin Ma, Weixuan Xia, Jianfeng Zhang Abstract We consider a Kyle-type model where insider trading takes place among a potentially large population of liquidity traders and is subject to legal penalties. Insiders exploit the liquidity provided by the trading masses to “camouflage” their actions and balance expected wealth with the necessary stealth to avoid detection. Under a diverse spectrum of prosecution schemes, we establish the existence of equilibria for arbitrary population sizes and a unique limiting equilibrium. A convergence analysis determines the scale of insider trading by a stealth index $γ$, revealing that the equilibrium can be closely approximated by a simple limit due to diminished price informativeness. Empirical aspects are derived from two calibration experiments using non-overlapping data sets spanning from 1980 to 2018, which underline the indispensable role of a large population in insider trading models with legal risk, along with important implications for the incidence of stealth trading and the deterrent effect of legal enforcement. ...

A Unified AI System For Data Quality Control and DataOps Management in Regulated Environments ArXiv ID: 2512.05559 “View on arXiv” Authors: Devender Saini, Bhavika Jain, Nitish Ujjwal, Philip Sommer, Dan Romuald Mbanga, Dhagash Mehta Abstract In regulated domains such as finance, the integrity and governance of data pipelines are critical - yet existing systems treat data quality control (QC) as an isolated preprocessing step rather than a first-class system component. We present a unified AI-driven Data QC and DataOps Management framework that embeds rule-based, statistical, and AI-based QC methods into a continuous, governed layer spanning ingestion, model pipelines, and downstream applications. Our architecture integrates open-source tools with custom modules for profiling, audit logging, breach handling, configuration-driven policies, and dynamic remediation. We demonstrate deployment in a production-grade financial setup: handling streaming and tabular data across multiple asset classes and transaction streams, with configurable thresholds, cloud-native storage interfaces, and automated alerts. We show empirical gains in anomaly detection recall, reduction of manual remediation effort, and improved auditability and traceability in high-throughput data workflows. By treating QC as a system concern rather than an afterthought, our framework provides a foundation for trustworthy, scalable, and compliant AI pipelines in regulated environments. ...

Convolution-FFT for option pricing in the Heston model ArXiv ID: 2512.05326 “View on arXiv” Authors: Xiang Gao, Cody Hyndman Abstract We propose a convolution-FFT method for pricing European options under the Heston model that leverages a continuously differentiable representation of the joint characteristic function. Unlike existing Fourier-based methods that rely on branch-cut adjustments or empirically tuned damping parameters, our approach yields a stable integrand even under large frequency oscillations. Crucially, we derive fully analytical error bounds that quantify both truncation error and discretization error in terms of model parameters and grid settings. To the best of our knowledge, this is the first work to provide such explicit, closed-form error estimates for an FFT-based convolution method specialized to the Heston model. Numerical experiments confirm the theoretical rates and illustrate robust, high-accuracy option pricing at modest computational cost. ...

Predicting Price Movements in High-Frequency Financial Data with Spiking Neural Networks ArXiv ID: 2512.05868 “View on arXiv” Authors: Brian Ezinwoke, Oliver Rhodes Abstract Modern high-frequency trading (HFT) environments are characterized by sudden price spikes that present both risk and opportunity, but conventional financial models often fail to capture the required fine temporal structure. Spiking Neural Networks (SNNs) offer a biologically inspired framework well-suited to these challenges due to their natural ability to process discrete events and preserve millisecond-scale timing. This work investigates the application of SNNs to high-frequency price-spike forecasting, enhancing performance via robust hyperparameter tuning with Bayesian Optimization (BO). This work converts high-frequency stock data into spike trains and evaluates three architectures: an established unsupervised STDP-trained SNN, a novel SNN with explicit inhibitory competition, and a supervised backpropagation network. BO was driven by a novel objective, Penalized Spike Accuracy (PSA), designed to ensure a network’s predicted price spike rate aligns with the empirical rate of price events. Simulated trading demonstrated that models optimized with PSA consistently outperformed their Spike Accuracy (SA)-tuned counterparts and baselines. Specifically, the extended SNN model with PSA achieved the highest cumulative return (76.8%) in simple backtesting, significantly surpassing the supervised alternative (42.54% return). These results validate the potential of spiking networks, when robustly tuned with task-specific objectives, for effective price spike forecasting in HFT. ...

The Red Queen’s Trap: Limits of Deep Evolution in High-Frequency Trading ArXiv ID: 2512.15732 “View on arXiv” Authors: Yijia Chen Abstract The integration of Deep Reinforcement Learning (DRL) and Evolutionary Computation (EC) is frequently hypothesized to be the “Holy Grail” of algorithmic trading, promising systems that adapt autonomously to non-stationary market regimes. This paper presents a rigorous post-mortem analysis of “Galaxy Empire,” a hybrid framework coupling LSTM/Transformer-based perception with a genetic “Time-is-Life” survival mechanism. Deploying a population of 500 autonomous agents in a high-frequency cryptocurrency environment, we observed a catastrophic divergence between training metrics (Validation APY $>300%$) and live performance (Capital Decay $>70%$). We deconstruct this failure through a multi-disciplinary lens, identifying three critical failure modes: the overfitting of \textit{“Aleatoric Uncertainty”} in low-entropy time-series, the \textit{“Survivor Bias”} inherent in evolutionary selection under high variance, and the mathematical impossibility of overcoming microstructure friction without order-flow data. Our findings provide empirical evidence that increasing model complexity in the absence of information asymmetry exacerbates systemic fragility. ...

Continuous-time reinforcement learning for optimal switching over multiple regimes ArXiv ID: 2512.04697 “View on arXiv” Authors: Yijie Huang, Mengge Li, Xiang Yu, Zhou Zhou Abstract This paper studies the continuous-time reinforcement learning (RL) for optimal switching problems across multiple regimes. We consider a type of exploratory formulation under entropy regularization where the agent randomizes both the timing of switches and the selection of regimes through the generator matrix of an associated continuous-time finite-state Markov chain. We establish the well-posedness of the associated system of Hamilton-Jacobi-Bellman (HJB) equations and provide a characterization of the optimal policy. The policy improvement and the convergence of the policy iterations are rigorously established by analyzing the system of equations. We also show the convergence of the value function in the exploratory formulation towards the value function in the classical formulation as the temperature parameter vanishes. Finally, a reinforcement learning algorithm is devised and implemented by invoking the policy evaluation based on the martingale characterization. Our numerical examples with the aid of neural networks illustrate the effectiveness of the proposed RL algorithm. ...

FX Market Making with Internal Liquidity ArXiv ID: 2512.04603 “View on arXiv” Authors: Alexander Barzykin, Robert Boyce, Eyal Neuman Abstract As the FX markets continue to evolve, many institutions have started offering passive access to their internal liquidity pools. Market makers act as principal and have the opportunity to fill those orders as part of their risk management, or they may choose to adjust pricing to their external OTC franchise to facilitate the matching flow. It is, a priori, unclear how the strategies managing internal liquidity should depend on market condions, the market maker’s risk appetite, and the placement algorithms deployed by participating clients. The market maker’s actions in the presence of passive orders are relevant not only for their own objectives, but also for those liquidity providers who have certain expectations of the execution speed. In this work, we investigate the optimal multi-objective strategy of a market maker with an option to take liquidity on an internal exchange, and draw important qualitative insights for real-world trading. ...