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版本更新 · v2arXiv:2608.06618

Beyond Co-Movement: Locality by Exposures Enables a Joint Factor-Graph Framework for Portfolio Diversification

Sara Chehab, Giorgos Iacovides, Parisa Yazdanparast, Danilo Mandic

原文摘要

Current portfolio construction methods are either agnostic to the effects of idiosyncratic shocks (standard factor models) or to the latent data structure driving systematic returns (recent graph-based approaches). This presents an opportunity to combine the complementary market aspects captured by the factor and graph domains, allowing asset allocations to operate directly on the underlying market structure, rather than on its observed co-movement or its finite-sample artefacts. In this work, we introduce the Mutually-INformed Graph-Locality and Exposures framework (MINGLE), which mutually regularises the factor and graph domains by redefining graph locality through systematic factor exposure profiles, rather than via observed co-movements. This is formalised through a unified Alternating Direction Method of Multipliers (ADMM) framework that jointly learns a latent factor representation and its induced graph topology directly from market returns. The resulting exposure-similarity graph aligns more closely with established economic sectors than conventional correlation-based graphs. Portfolios constructed from this representation are shown to consistently outperform their correlation-based counterparts across a range of volatility regimes and transaction cost levels. For rigour, paired statistical testing confirms that these gains stem from the reconciliation of the graph and factor domains.

q-fin.PM · cs.LG · q-fin.ST

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2412.16175

Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study

Yilie Huang, Yanwei Jia, Xun Yu Zhou

原文摘要

We study continuous-time mean--variance portfolio selection in markets where stock prices are diffusion processes driven by observable factors that are also diffusion processes, yet the coefficients of these processes are unknown. Based on the recently developed reinforcement learning (RL) theory for diffusion processes, we present a general data-driven RL approach that learns the pre-committed investment strategy directly without attempting to learn or estimate the market coefficients. For multi-stock Black--Scholes markets without factors, we further devise an algorithm and prove its performance guarantee by deriving a sublinear regret bound in terms of the Sharpe ratio. We then carry out an extensive empirical study implementing this algorithm to compare its performance and trading characteristics, evaluated under a host of common metrics, with a large number of widely employed portfolio allocation strategies on S\&P 500 constituents. The results demonstrate that the proposed continuous-time RL strategy is consistently among the best, especially in a volatile bear market, and decisively outperforms the model-based continuous-time counterparts by significant margins.

q-fin.PM · cs.LG · eess.SY · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14699

Conformal Risk Certificates for Portfolio Optimization Under Uncertainty: A Gated Reinforcement-Learning Approach

Mohammad Zarei, Amirhossein MohammadpourAzari, Moslem Habibi

原文摘要

Reinforcement-learning portfolio policies can optimize allocations from prices, covariances, and prior positions, yet they lack a calibrated, replayable mechanism for certifying that a proposed allocation respects a bounded risk envelope before execution. Regulators increasingly expect automated portfolio systems to provide exactly this kind of pre-trade audit trail, interpretable risk limit, and human-oversight mechanism. Gated Conformal Semantic Risk Control addresses this gap by separating return-seeking allocation from text-conditioned downside-risk verification. An instruction-tuned language model maps timestamped disclosures into schema-validated semantic risk states over macro, liquidity, earnings, and sector channels. These states induce correlated ellipsoidal factor-shock uncertainty sets whose conformal radii are calibrated by rolling finite-sample prediction over localized semantic nonconformity buckets. An asymmetric second-order cone gate then checks each raw PPO proposal against a certified worst-case semantic loss budget. The gate stays inactive for compliant proposals and intervenes only when that budget is exceeded. In walk-forward out-of-sample tests on nine liquid U.S. Select Sector SPDR ETFs, Raw PPO earns a 12.7% annual return while incurring a 4.05% semantic violation rate. Gated-CSRC removes all observed out-of-sample semantic limit breaches while retaining an 11.7% annual return. This 100-basis-point auditable safety premium is smaller than the yield loss imposed by unconditional hard projection, and exact McNemar testing confirms the compliance gain at $p < 10^{-15}$. Loughran-McDonald lexicon controls and time-shuffled text controls fail to reproduce this certificate.

q-fin.PM · cs.LG · math.OC · q-fin.RM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.24490

Market Regime Council for Dynamic Credit Assignment in Multi-Agent LLM Decision Systems

Yunhua Pei, Zerui Ge, Jin Zheng, John Cartlidge

原文摘要

Multi-agent LLM decision systems for portfolio management still lack a principled way to assign credit across specialist agents, remain vulnerable to cold-start dominance under regime shifts, and offer limited transparency into how final allocations are formed. We propose Market Regime Council (MRC), an online credit-assignment mechanism over coalitions of specialist agents, with portfolio management as the downstream task. MRC computes exact Shapley credits across all single, pairwise, and Grand-coalition outputs for online agent weighting. Instantiated with N=3 specialist agents, at each trading period, MRC recomputes coalition-based Shapley weights from exponentially weighted performance histories, uses a Bayesian adaptive mixture to stabilize early periods, applies regime-dependent multipliers to adjust agent authority, and records each rebalance through a five-layer causal trace. Over 1,037 trading days across 13 crypto assets and five seeds, MRC achieves a Sharpe ratio of 1.51 and a cumulative return of 440.1%, ranking first on CR, SR, and IR among active baselines and attaining the lowest MDD among active methods. Ablation results show that the gains come from Shapley-weighted integration across coalition outputs rather than from any single stage in isolation. Code and demo data are available open source.

cs.AI · cs.LG · q-fin.PM

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2307.04754

Dynamic Strategy Selection

Francesco Cordoni, Alessio Sancetta, Yuqian Zhao

原文摘要

We consider the problem of dynamically selecting among competing strategies using observable predictors. To mitigate estimation noise and excessive switching, we regularise the estimated policy with a switching cost, which may exceed actual transaction costs when these are too small to provide sufficient regularisation. Since switching costs make current choices affect future decisions, we formulate the problem using dynamic programming. The optimal policy is estimated using a reinforcement learning algorithm based on iterated regressions, making the procedure simple to implement. We apply the method to monthly rotation among momentum, value, profitability, and defensive U.S. equity factors over a sample of about 60 years. Using a parsimonious set of state variables, the estimated policy outperforms both the myopic dynamic policy and the best fixed factor in hindsight in out-of-sample evaluation. Under weak conditions, we show theoretically that the decision rule estimated from empirical data is asymptotically consistent for the unknown optimal decision rule. Simulation results illustrate the finite-sample relevance of these asymptotic results.

cs.LG · q-fin.PM · stat.ME · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04826

Safe Portfolio Learning from Untrusted Predictions: Signed Trust, Abstention, and Cross-Universe Polarity Failure

Amirhossein MohammadpourAzari, Mohammad Zarei, MohammadAmin Fazli

原文摘要

Portfolio use of machine predictions must often proceed while the predictions' economic direction, reliability, and timing remain untrusted, and while feedback from overlapping multi-period positions arrives with delay. A controller chooses among Safe, Follow, and Reverse portfolio actions, so signal orientation is learned rather than assumed. A share-adaptive master updates action probabilities from matured Safe-relative rewards, an evidence gate can abstain by transferring unsupported directional probability to Safe, a delayed collateral budget reserves worst-case loss capacity before exposure is funded, and a protected split account caps the capital adaptive decisions may touch. The architecture carries a dual certificate, tracking regret against switching action sequences under delay and one-sided intervention, together with a pathwise collateral safety bound that holds when predictions fail. On a 24-stock development universe a conservative prediction-assisted overlay stays near equal-weight performance at a fraction of the turnover. On a frozen 82-stock holdout, the pre-specified Reverse orientation fails to outperform Equal Weight; a separately labeled post-holdout diagnostic shows that permanently flipping the sign to Follow also does not recover a return edge. Under the post-holdout controller, protected wealth remains within 15.9 basis points of the Safe benchmark throughout. The contribution is safe adaptive use of untrusted predictions, not demonstrated alpha.

q-fin.MF · cs.CE · q-fin.CP · q-fin.PM

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.25740

A mathematical study of the excess growth rate

Steven Campbell, Ting-Kam Leonard Wong

原文摘要

The excess growth rate, defined as the gap in Jensen's inequality for the logarithm, is a fundamental functional in portfolio theory. In this paper, we present a mathematical study motivated by information theory. We begin by establishing its properties and showing that it has rich connections with information-theoretic concepts such as the Helmholtz free energy, L. Campbell's measure of average code length and large deviations. Our main results consist of three axiomatic characterization theorems of the excess growth rate, in terms of (i) the relative entropy, (ii) the gap in Jensen's inequality, and (iii) the logarithmic divergence that generalizes the Bregman divergence. Furthermore, we study maximization of the excess growth rate and compare it with Kelly's growth optimal portfolio. Our results not only provide theoretical justifications of the significance of the excess growth rate, but also establish new connections between information theory and quantitative finance.

cs.IT · math.PR · q-fin.MF · q-fin.PM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03022

Interpretable Allocation and Model-Predictive Rebalancing of Equity Portfolios, Tested on Real and Synthetic Markets

Jeffrey D. Varner

原文摘要

A live equity portfolio poses three problems: which stocks to hold, when to trade, and whether the strategy holds up beyond the observed market. We built one system for all three and tested it on 373 US equities, calibrated on 2014-2024 and traded from January 2025 to September 2026. A selection rule chose stocks by score, by a bandit, or at random, and one of seven weighting rules, four consumer-theory utilities, and three standard portfolios, set share counts under limits on budget, single-stock weight, portfolio volatility, and turnover, with each stock's preference weight a bounded score of its trend, market beta, and the market's recent direction. A model-predictive controller forecasts portfolio value with a multi-asset geometric Brownian motion and re-planned at its horizon, when the value left its forecast band, or at a drawdown stop. To probe deeper declines than the real window held, we also ran the system on simulated markets from per-asset hidden Markov generators with jumps, linked by market and sector factors. On real prices, the stocks chosen mattered more to terminal wealth than the weighting rule, and the early re-plans shrank the worst drawdown in most rule combinations against a fixed calendar without consistently raising wealth. In the deepest simulated declines, every combination, random picks included, ended above simulated SPY and the equal-weight universe in the median, even without early re-plans, so the margin came from shared parts of the system, not from stock selection or re-plan timing. A market hedge that tightened a beta cap as the market weakened cost wealth on real prices but paid off in the most sustained simulated declines, and the generator's persistent trends favored score-based selection more than the real market did, which suggests a synthetic test judges stock selection fairly only if its generator does not build in that persistence.

q-fin.PM · cs.CE · q-fin.RM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39205

Sparse Autoencoders as Implicit Diversification Regularisers for Cross-Sectional Cryptocurrency Returns

Weijia Han, Lisha Qu

原文摘要

Factor models are central to asset pricing and portfolio construction, but neural factors are often difficult to interpret, and their effect on portfolio risk is unclear. A sparse autoencoder (SAE) may address both concerns by describing daily returns across many assets with only a few active features. We ask whether this sparse representation produces stable financial factors and better-diversified portfolios. We study cryptocurrency returns, use equities as a secondary test, and compare the SAE with common benchmarks in a time-ordered evaluation. We find consistently lower volatility in SAE portfolios and link this result to lower correlations among the selected assets. However, the SAE does not outperform a simple contrarian strategy on risk-adjusted return, and its factor identities are not stable across training runs. Our contribution is to show that sparse coding can support portfolio diversification, while clearly separating this risk effect from claims about alpha or stable factor identities.

cs.LG · q-fin.PM · q-fin.ST

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.02677

Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization

Giovanni Dispoto, Marcello Restelli, Carmine Ventre

原文摘要

Modern portfolio management increasingly demands a balance between traditional risk-adjusted returns and strict Environmental, Social, and Governance (ESG) mandates. Current Reinforcement Learning (RL) approaches typically optimize for a single ESG provider, neglecting the significant divergence in rating methodologies across the industry and the unintuitive nature of manually weighting conflicting objectives. This paper addresses these limitations by formulating ESG-aware portfolio optimization as a Multi-Objective Reinforcement Learning (MORL) problem that simultaneously incorporates ratings from three distinct ESG agencies. To bridge the gap between high-dimensional algorithmic trade-offs and human decision-making, we integrate a Preference Elicitation framework using Gaussian Processes. This system enables practitioners to align the RL policy behavior with their preferences through intuitive pairwise comparisons of candidate portfolios based on their Sharpe ratios and aggregate ESG scores. We systematically evaluate our framework by employing prompt-conditioned LLMs as synthetic preference oracles under varied regional conditions and assess how the methodology adapts to heterogeneous investment behaviors and sustainability preferences. The experiments demonstrate feasibility in a controlled synthetic setting and characterize sensitivity to oracle and prompt design.

q-fin.PM · cs.CE · cs.LG

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arXiv ↗PDF ↗