Visualization of The Content of Surah al Fiil using Marker-Based Augmented Reality ArXiv ID: 2512.17895 “View on arXiv” Authors: Wisnu Uriawan, Ahmad Badru Al Husaeni, Dzakwanfaiq Nauval, Farid Muhtar Fathir, Mahesa Adlan Falah, Muhammad Miftahur Rizki Awalin Abstract This study presents the development of a marker-based augmented reality (AR) application designed to visualize the content of Surah al-Fil as an interactive and context-rich medium for Islamic education. Using a research and development approach, the system was developed through structured stages including data collection, user requirement analysis, interface design, 3D asset creation using Blender, and integration of Unity 3D with the Vuforia SDK. The application features key visual elements such as the elephant army, the Kaaba, and the Ababil birds, which were modeled in detail and linked to high-contrast image markers to ensure accurate and stable AR tracking. Functional testing demonstrated strong technical performance, achieving a 95 percent marker detection accuracy at an optimal distance of 30-40 cm with consistent real-time rendering across multiple Android devices. User evaluations involving students and Islamic education teachers indicated high acceptance, with an overall satisfaction score of 4.7 out of 5 in terms of usability, visual appeal, interactivity, and learning effectiveness. These findings indicate that AR-based learning media can enhance learner engagement, deepen understanding of Quranic narratives, and provide immersive insights into historical and spiritual contexts. Overall, this study demonstrates that marker-based AR technology has significant potential to support innovation in digital Islamic education by enriching traditional learning with interactive and visually intuitive experiences. ...

An Efficient Machine Learning Framework for Option Pricing via Fourier Transform ArXiv ID: 2512.16115 “View on arXiv” Authors: Liying Zhang, Ying Gao Abstract The increasing need for rapid recalibration of option pricing models in dynamic markets places stringent computational demands on data generation and valuation algorithms. In this work, we propose a hybrid algorithmic framework that integrates the smooth offset algorithm (SOA) with supervised machine learning models for the fast pricing of multiple path-independent options under exponential Lévy dynamics. Building upon the SOA-generated dataset, we train neural networks, random forests, and gradient boosted decision trees to construct surrogate pricing operators. Extensive numerical experiments demonstrate that, once trained, these surrogates achieve order-of-magnitude acceleration over direct SOA evaluation. Importantly, the proposed framework overcomes key numerical limitations inherent to fast Fourier transform-based methods, including the consistency of input data and the instability in deep out-of-the-money option pricing. ...

Asymptotic and finite-sample distributions of one- and two-sample empirical relative entropy, with application to change-point detection ArXiv ID: 2512.16411 “View on arXiv” Authors: Matthieu Garcin, Louis Perot Abstract Relative entropy, as a divergence metric between two distributions, can be used for offline change-point detection and extends classical methods that mainly rely on moment-based discrepancies. To build a statistical test suitable for this context, we study the distribution of empirical relative entropy and derive several types of approximations: concentration inequalities for finite samples, asymptotic distributions, and Berry-Esseen bounds in a pre-asymptotic regime. For the latter, we introduce a new approach to obtain Berry-Esseen inequalities for nonlinear functions of sum statistics under some convexity assumptions. Our theoretical contributions cover both one- and two-sample empirical relative entropies. We then detail a change-point detection procedure built on relative entropy and compare it, through extensive simulations, with classical methods based on moments or on information criteria. Finally, we illustrate its practical relevance on two real datasets involving temperature series and volatility of stock indices. ...

Design of a Decentralized Fixed-Income Lending Automated Market Maker Protocol Supporting Arbitrary Maturities ArXiv ID: 2512.16080 “View on arXiv” Authors: Tianyi Ma Abstract In decentralized finance (DeFi), designing fixed-income lending automated market makers (AMMs) is extremely challenging due to time-related complexities. Moreover, existing protocols only support single-maturity lending. Building upon the BondMM protocol, this paper argues that its mathematical invariants are sufficiently elegant to be generalized to arbitrary maturities. This paper thus propose an improved design, BondMM-A, which supports lending activities of any maturity. By integrating fixed-income instruments of varying maturities into a single smart contract, BondMM-A offers users and liquidity providers (LPs) greater operational freedom and capital efficiency. Experimental results show that BondMM-A performs excellently in terms of interest rate stability and financial robustness. ...

Adaptive Partitioning and Learning for Stochastic Control of Diffusion Processes ArXiv ID: 2512.14991 “View on arXiv” Authors: Hanqing Jin, Renyuan Xu, Yanzhao Yang Abstract We study reinforcement learning for controlled diffusion processes with unbounded continuous state spaces, bounded continuous actions, and polynomially growing rewards: settings that arise naturally in finance, economics, and operations research. To overcome the challenges of continuous and high-dimensional domains, we introduce a model-based algorithm that adaptively partitions the joint state-action space. The algorithm maintains estimators of drift, volatility, and rewards within each partition, refining the discretization whenever estimation bias exceeds statistical confidence. This adaptive scheme balances exploration and approximation, enabling efficient learning in unbounded domains. Our analysis establishes regret bounds that depend on the problem horizon, state dimension, reward growth order, and a newly defined notion of zooming dimension tailored to unbounded diffusion processes. The bounds recover existing results for bounded settings as a special case, while extending theoretical guarantees to a broader class of diffusion-type problems. Finally, we validate the effectiveness of our approach through numerical experiments, including applications to high-dimensional problems such as multi-asset mean-variance portfolio selection. ...

Adaptive Weighted Genetic Algorithm-Optimized SVR for Robust Long-Term Forecasting of Global Stock Indices for investment decisions ArXiv ID: 2512.15113 “View on arXiv” Authors: Mohit Beniwal Abstract Long-term price forecasting remains a formidable challenge due to the inherent uncertainty over the long term, despite some success in short-term predictions. Nonetheless, accurate long-term forecasts are essential for high-net-worth individuals, institutional investors, and traders. The proposed improved genetic algorithm-optimized support vector regression (IGA-SVR) model is specifically designed for long-term price prediction of global indices. The performance of the IGA-SVR model is rigorously evaluated and compared against the state-of-the-art baseline models, the Long Short-Term Memory (LSTM), and the forward-validating genetic algorithm optimized support vector regression (OGA-SVR). Extensive testing was conducted on the five global indices, namely Nifty, Dow Jones Industrial Average (DJI), DAX Performance Index (DAX), Nikkei 225 (N225), and Shanghai Stock Exchange Composite Index (SSE) from 2021 to 2024 of daily price prediction up to a year. Overall, the proposed IGA-SVR model achieved a reduction in MAPE by 19.87% compared to LSTM and 50.03% compared to OGA-SVR, demonstrating its superior performance in long-term daily price forecasting of global indices. Further, the execution time for LSTM was approximately 20 times higher than that of IGA-SVR, highlighting the high accuracy and computational efficiency of the proposed model. The genetic algorithm selects the optimal hyperparameters of SVR by minimizing the arithmetic mean of the Mean Absolute Percentage Error (MAPE) calculated over the full training dataset and the most recent five years of training data. This purposefully designed training methodology adjusts for recent trends while retaining long-term trend information, thereby offering enhanced generalization compared to the LSTM and rolling-forward validation approach employed by OGA-SVR, which forgets long-term trends and suffers from recency bias. ...

Multi-Objective Bayesian Optimization of Deep Reinforcement Learning for Environmental, Social, and Governance (ESG) Financial Portfolio Management ArXiv ID: 2512.14992 “View on arXiv” Authors: M. Coronado-Vaca Abstract DRL agents circumvent the issue of classic models in the sense that they do not make assumptions like the financial returns being normally distributed and are able to deal with any information like the ESG score if they are configured to gain a reward that makes an objective better. However, the performance of DRL agents has high variability and it is very sensible to the value of their hyperparameters. Bayesian optimization is a class of methods that are suited to the optimization of black-box functions, that is, functions whose analytical expression is unknown, are noisy and expensive to evaluate. The hyperparameter tuning problem of DRL algorithms perfectly suits this scenario. As training an agent just for one objective is a very expensive period, requiring millions of timesteps, instead of optimizing an objective being a mixture of a risk-performance metric and an ESG metric, we choose to separate the objective and solve the multi-objective scenario to obtain an optimal Pareto set of portfolios representing the best tradeoff between the Sharpe ratio and the ESG mean score of the portfolio and leaving to the investor the choice of the final portfolio. We conducted our experiments using environments encoded within the OpenAI Gym, adapted from the FinRL platform. The experiments are carried out in the Dow Jones Industrial Average (DJIA) and the NASDAQ markets in terms of the Sharpe ratio achieved by the agent and the mean ESG score of the portfolio. We compare the performance of the obtained Pareto sets in hypervolume terms illustrating how portfolios are the best trade-off between the Sharpe ratio and mean ESG score. Also, we show the usefulness of our proposed methodology by comparing the obtained hypervolume with one achieved by a Random Search methodology on the DRL hyperparameter space. ...

Deep Learning and Elicitability for McKean-Vlasov FBSDEs With Common Noise ArXiv ID: 2512.14967 “View on arXiv” Authors: Felipe J. P. Antunes, Yuri F. Saporito, Sebastian Jaimungal Abstract We present a novel numerical method for solving McKean-Vlasov forward-backward stochastic differential equations (MV-FBSDEs) with common noise, combining Picard iterations, elicitability and deep learning. The key innovation involves elicitability to derive a path-wise loss function, enabling efficient training of neural networks to approximate both the backward process and the conditional expectations arising from common noise - without requiring computationally expensive nested Monte Carlo simulations. The mean-field interaction term is parameterized via a recurrent neural network trained to minimize an elicitable score, while the backward process is approximated through a feedforward network representing the decoupling field. We validate the algorithm on a systemic risk inter-bank borrowing and lending model, where analytical solutions exist, demonstrating accurate recovery of the true solution. We further extend the model to quantile-mediated interactions, showcasing the flexibility of the elicitability framework beyond conditional means or moments. Finally, we apply the method to a non-stationary Aiyagari–Bewley–Huggett economic growth model with endogenous interest rates, illustrating its applicability to complex mean-field games without closed-form solutions. ...

Sources and Nonlinearity of High Volume Return Premium: An Empirical Study on the Differential Effects of Investor Identity versus Trading Intensity (2020-2024) ArXiv ID: 2512.14134 “View on arXiv” Authors: Sungwoo Kang Abstract Chae and Kang (2019, \textit{“Pacific-Basin Finance Journal”}) documented a puzzling Low Volume Return Premium (LVRP) in Korea – contradicting global High Volume Return Premium (HVRP) evidence. We resolve this puzzle. Using Korean market data (2020-2024), we demonstrate that HVRP exists in Korea but is masked by (1) pooling heterogeneous investor types and (2) using inappropriate intensity normalization. When institutional buying intensity is normalized by market capitalization rather than trading value, a perfect monotonic relationship emerges: highest-conviction institutional buying (Q4) generates +\institutionLedQFourDayPlusFiftyCAR\ cumulative abnormal returns over 50 days, while lowest-intensity trades (Q1) yield modest returns (+\institutionLedQOneDayPlusFiftyCAR). Retail investors exhibit a flat pattern – their trading generates near-zero returns regardless of conviction level – confirming the pure noise trader hypothesis. During the Donghak Ant Movement (2020-2021), however, coordinated retail investors temporarily transformed from noise traders to liquidity providers, generating returns comparable to institutional trading. Our findings reconcile conflicting international evidence and demonstrate that detecting informed trading signals requires investor-type decomposition, nonlinear quartile analysis, and conviction-based (market cap) rather than participation-based (trading value) measurement. ...

Interpretable Hypothesis-Driven Trading:A Rigorous Walk-Forward Validation Framework for Market Microstructure Signals ArXiv ID: 2512.12924 “View on arXiv” Authors: Gagan Deep, Akash Deep, William Lamptey Abstract We develop a rigorous walk-forward validation framework for algorithmic trading designed to mitigate overfitting and lookahead bias. Our methodology combines interpretable hypothesis-driven signal generation with reinforcement learning and strict out-of-sample testing. The framework enforces strict information set discipline, employs rolling window validation across 34 independent test periods, maintains complete interpretability through natural language hypothesis explanations, and incorporates realistic transaction costs and position constraints. Validating five market microstructure patterns across 100 US equities from 2015 to 2024, the system yields modest annualized returns (0.55%, Sharpe ratio 0.33) with exceptional downside protection (maximum drawdown -2.76%) and market-neutral characteristics (beta = 0.058). Performance exhibits strong regime dependence, generating positive returns during high-volatility periods (0.60% quarterly, 2020-2024) while underperforming in stable markets (-0.16%, 2015-2019). We report statistically insignificant aggregate results (p-value 0.34) to demonstrate a reproducible, honest validation protocol that prioritizes interpretability and extends naturally to advanced hypothesis generators, including large language models. The key empirical finding reveals that daily OHLCV-based microstructure signals require elevated information arrival and trading activity to function effectively. The framework provides complete mathematical specifications and open-source implementation, establishing a template for rigorous trading system evaluation that addresses the reproducibility crisis in quantitative finance research. For researchers, practitioners, and regulators, this work demonstrates that interpretable algorithmic trading strategies can be rigorously validated without sacrificing transparency or regulatory compliance. ...