Multi-Asset

Handbook of SustainableFinance ArXiv ID: ssrn-4277875 “View on arXiv” Authors: Unknown Abstract This handbook in Sustainable Finance corresponds to the lecture notes of the course given at University Paris-Saclay, ENSAE and Sorbonne University. It covers t Keywords: Sustainable Finance, ESG, Climate Risk, Green Bonds, Multi-Asset Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The handbook provides comprehensive definitions, historical context, and regulatory frameworks of sustainable finance with moderate mathematical modeling (portfolio theory, scoring methods) but lacks explicit backtests, code implementations, or performance metrics, positioning it as a theoretical and policy-oriented text rather than an empirical trading strategy. flowchart TD A["Research Goal: Define Sustainable Finance Frameworks & Metrics"] --> B{"Data & Inputs"} B --> C["Methodology: ESG Integration & Climate Risk Modeling"] B --> D["Data: ESG Ratings, Climate Data, Multi-Asset Returns"] C & D --> E{"Computational Processes"} E --> F["Analysis: Green Bond Valuation & Portfolio Optimization"] F --> G["Key Outcomes: Risk-Adjusted Returns & Impact Metrics"]

Robo-Advising in Motion: A Model Predictive Control Approach ArXiv ID: 2601.09127 “View on arXiv” Authors: Tomasz R. Bielecki, Igor Cialenco Abstract Robo-advisors (RAs) are automated portfolio management systems that complement traditional financial advisors by offering lower fees and smaller initial investment requirements. While most existing RAs rely on static, one-period allocation methods, we propose a dynamic, multi-period asset-allocation framework that leverages Model Predictive Control (MPC) to generate suboptimal but practically effective strategies. Our approach combines a Hidden Markov Model with Black-Litterman (BL) methodology to forecast asset returns and covariances, and incorporates practically important constraints, including turnover limits, transaction costs, and target portfolio allocations. We study two predominant optimality criteria in wealth management: dynamic mean-variance (MV) and dynamic risk-budgeting (MRB). Numerical experiments demonstrate that MPC-based strategies consistently outperform myopic approaches, with MV providing flexible and diversified portfolios, while MRB delivers smoother allocations less sensitive to key parameters. These findings highlight the trade-offs between adaptability and stability in practical robo-advising design. ...

Enhancing Portfolio Optimization with Deep Learning Insights ArXiv ID: 2601.07942 “View on arXiv” Authors: Brandon Luo, Jim Skufca Abstract Our work focuses on deep learning (DL) portfolio optimization, tackling challenges in long-only, multi-asset strategies across market cycles. We propose training models with limited regime data using pre-training techniques and leveraging transformer architectures for state variable inclusion. Evaluating our approach against traditional methods shows promising results, demonstrating our models’ resilience in volatile markets. These findings emphasize the evolving landscape of DL-driven portfolio optimization, stressing the need for adaptive strategies to navigate dynamic market conditions and improve predictive accuracy. ...

Breaking the Dimensional Barrier: Dynamic Portfolio Choice with Parameter Uncertainty via Pontryagin Projection ArXiv ID: 2601.03175 “View on arXiv” Authors: Jeonggyu Huh, Hyeng Keun Koo Abstract We study continuous-time portfolio choice in diffusion markets with parameter $θ\in Θ$ and uncertainty law $q(dθ)$. Nature draws latent $θ\sim q$ at time 0; the investor cannot observe it and must deploy a single $θ$-blind feedback policy maximizing an ex-ante CRRA objective averaged over diffusion noise and $θ$. Our methods access $q$ only by sampling and assume no parametric form. We extend Pontryagin-Guided Direct Policy Optimization (PG-DPO) by sampling $θ$ inside the simulator and computing discrete-time gradients via backpropagation through time (BPTT), and we propose projected PG-DPO (P-PGDPO) that projects costate estimates to satisfy the $q$-aggregated Pontryagin first-order condition, yielding a deployable rule. We prove a BPTT-PMP correspondence uniform on compacts and a residual-based $θ$-blind policy-gap bound under local stability with explicit discretization/Monte Carlo errors; experiments show projection-driven stability and accurate decision-time benchmark recovery in high dimensions. ...

Uni-FinLLM: A Unified Multimodal Large Language Model with Modular Task Heads for Micro-Level Stock Prediction and Macro-Level Systemic Risk Assessment ArXiv ID: 2601.02677 “View on arXiv” Authors: Gongao Zhang, Haijiang Zeng, Lu Jiang Abstract Financial institutions and regulators require systems that integrate heterogeneous data to assess risks from stock fluctuations to systemic vulnerabilities. Existing approaches often treat these tasks in isolation, failing to capture cross-scale dependencies. We propose Uni-FinLLM, a unified multimodal large language model that uses a shared Transformer backbone and modular task heads to jointly process financial text, numerical time series, fundamentals, and visual data. Through cross-modal attention and multi-task optimization, it learns a coherent representation for micro-, meso-, and macro-level predictions. Evaluated on stock forecasting, credit-risk assessment, and systemic-risk detection, Uni-FinLLM significantly outperforms baselines. It raises stock directional accuracy to 67.4% (from 61.7%), credit-risk accuracy to 84.1% (from 79.6%), and macro early-warning accuracy to 82.3%. Results validate that a unified multimodal LLM can jointly model asset behavior and systemic vulnerabilities, offering a scalable decision-support engine for finance. ...

Order-Constrained Spectral Causality in Multivariate Time Series ArXiv ID: 2601.01216 “View on arXiv” Authors: Alejandro Rodriguez Dominguez Abstract We introduce an operator-theoretic framework for causal analysis in multivariate time series based on order-constrained spectral non-invariance. Directional influence is defined as sensitivity of second-order dependence operators to admissible, order-preserving temporal deformations of a designated source component, yielding an intrinsically multivariate causal notion summarized through orthogonally invariant spectral functionals. Under linear Gaussian assumptions, the criterion coincides with linear Granger causality, while beyond this regime it captures collective and nonlinear directional dependence not reflected in pairwise predictability. We establish existence, uniform consistency, and valid inference for the resulting non-smooth supremum–infimum statistics using shift-based randomization that exploits order-induced group invariance, yielding finite-sample exactness under exact invariance and asymptotic validity under weak dependence without parametric assumptions. Simulations demonstrate correct size and strong power against distributed and bulk-dominated alternatives, including nonlinear dependence missed by linear Granger tests with appropriate feature embeddings. An empirical application to a high-dimensional panel of daily financial return series spanning major asset classes illustrates system-level causal monitoring in practice. Directional organization is episodic and stress-dependent, causal propagation strengthens while remaining multi-channel, dominant causal hubs reallocate rapidly, and statistically robust transmission channels are sparse and horizon-heterogeneous even when aggregate lead–lag asymmetry is weak. The framework provides a scalable and interpretable complement to correlation-, factor-, and pairwise Granger-style analyses for complex systems. ...

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. ...

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. ...

Basis Immunity: Isotropy as a Regularizer for Uncertainty ArXiv ID: 2511.13334 “View on arXiv” Authors: Florent Segonne Abstract Diversification is a cornerstone of robust portfolio construction, yet its application remains fraught with challenges due to model uncertainty and estimation errors. Practitioners often rely on sophisticated, proprietary heuristics to navigate these issues. Among recent advancements, Agnostic Risk Parity introduces eigenrisk parity (ERP), an innovative approach that leverages isotropy to evenly allocate risk across eigenmodes, enhancing portfolio stability. In this paper, we review and extend the isotropy-enforced philosophy of ERP proposing a versatile framework that integrates mean-variance optimization with an isotropy constraint acting as a geometric regularizer against signal uncertainty. The resulting allocations decompose naturally into canonical portfolios, smoothly interpolating between full isotropy (closed-form isotropic-mean allocation) and pure mean-variance through a tunable isotropy penalty. Beyond methodology, we revisit fundamental concepts and clarify foundational links between isotropy, canonical portfolios, principal portfolios, primal versus dual representations, and intrinsic basis-invariant metrics for returns, risk, and isotropy. Applied to sector trend-following, the isotropy constraint systematically induces negative average-signal exposure – a structural, parameter-robust crash hedge. This work offers both a practical, theoretically grounded tool for resilient allocation under signal uncertainty and a pedagogical synthesis of modern portfolio concepts. ...

Aligning Language Models with Investor and Market Behavior for Financial Recommendations ArXiv ID: 2510.15993 “View on arXiv” Authors: Fernando Spadea, Oshani Seneviratne Abstract Most financial recommendation systems often fail to account for key behavioral and regulatory factors, leading to advice that is misaligned with user preferences, difficult to interpret, or unlikely to be followed. We present FLARKO (Financial Language-model for Asset Recommendation with Knowledge-graph Optimization), a novel framework that integrates Large Language Models (LLMs), Knowledge Graphs (KGs), and Kahneman-Tversky Optimization (KTO) to generate asset recommendations that are both profitable and behaviorally aligned. FLARKO encodes users’ transaction histories and asset trends as structured KGs, providing interpretable and controllable context for the LLM. To demonstrate the adaptability of our approach, we develop and evaluate both a centralized architecture (CenFLARKO) and a federated variant (FedFLARKO). To our knowledge, this is the first demonstration of combining KTO for fine-tuning of LLMs for financial asset recommendation. We also present the first use of structured KGs to ground LLM reasoning over behavioral financial data in a federated learning (FL) setting. Evaluated on the FAR-Trans dataset, FLARKO consistently outperforms state-of-the-art recommendation baselines on behavioral alignment and joint profitability, while remaining interpretable and resource-efficient. ...