Diffolio: A Diffusion Model for Multivariate Probabilistic Financial Time-Series Forecasting and Portfolio Construction

• URL: http://arxiv.org/abs/2511.07014v1
• Date: Mon, 10 Nov 2025 12:05:32 GMT
• Title: Diffolio: A Diffusion Model for Multivariate Probabilistic Financial Time-Series Forecasting and Portfolio Construction
• Authors: So-Yoon Cho, Jin-Young Kim, Kayoung Ban, Hyeng Keun Koo, Hyun-Gyoon Kim,
• Abstract summary: We propose Diffolio, a diffusion model designed for multivariate financial time-series forecasting and portfolio construction.<n>Diffolio employs a denoising network with a hierarchical attention architecture, comprising both asset-level and market-level layers.
• Score: 7.0782219254786725
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Probabilistic forecasting is crucial in multivariate financial time-series for constructing efficient portfolios that account for complex cross-sectional dependencies. In this paper, we propose Diffolio, a diffusion model designed for multivariate financial time-series forecasting and portfolio construction. Diffolio employs a denoising network with a hierarchical attention architecture, comprising both asset-level and market-level layers. Furthermore, to better reflect cross-sectional correlations, we introduce a correlation-guided regularizer informed by a stable estimate of the target correlation matrix. This structure effectively extracts salient features not only from historical returns but also from asset-specific and systematic covariates, significantly enhancing the performance of forecasts and portfolios. Experimental results on the daily excess returns of 12 industry portfolios show that Diffolio outperforms various probabilistic forecasting baselines in multivariate forecasting accuracy and portfolio performance. Moreover, in portfolio experiments, portfolios constructed from Diffolio's forecasts show consistently robust performance, thereby outperforming those from benchmarks by achieving higher Sharpe ratios for the mean-variance tangency portfolio and higher certainty equivalents for the growth-optimal portfolio. These results demonstrate the superiority of our proposed Diffolio in terms of not only statistical accuracy but also economic significance.

Related papers

• A Novel approach to portfolio construction [0.0]
  This paper proposes a machine learning-based framework for asset selection and portfolio construction.<n>It is called the Best-Path Algorithm Sparse Graphical Model (BPASGM)<n>Monte Carlo simulations show BPASGM-based portfolios achieve more stable risk-return profiles, lower realized volatility, and superior risk-adjusted performance.
  arXiv  Detail & Related papers  (2026-02-03T09:52:06Z)
• Integrated Prediction and Multi-period Portfolio Optimization [29.582959310549594]
  Multi-period portfolio optimization accounts for transaction costs, path-dependent risks, and the intertemporal structure of trading decisions.<n>This paper introduces IPMO, a model for multi-period mean-variance portfolio optimization with turnover penalties.<n>For scalability, we introduce a mirror-descent fixed-point (MDFP) differentiation scheme that avoids factorizing the Karush-Kuhn-Tucker (KKT) systems.
  arXiv  Detail & Related papers  (2025-12-12T04:31:22Z)
• STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading [55.02735046724146]
  In financial trading, factor models are widely used to price assets and capture excess returns from mispricing.<n>We propose a Spatio-Temporal factOR Model based on dual vector quantized variational autoencoders, named STORM.<n>Storm extracts features of stocks from temporal and spatial perspectives, then fuses and aligns these features at the fine-grained and semantic level, and represents the factors as multi-dimensional embeddings.
  arXiv  Detail & Related papers  (2024-12-12T17:15:49Z)
• Conformal Predictive Portfolio Selection [10.470114319701576]
  We propose a framework for predictive portfolio selection via conformal prediction.<n>Our approach forecasts future portfolio returns, computes the corresponding prediction intervals, and selects the portfolio of interest based on these intervals.<n>We demonstrate the effectiveness of the CPPS framework by applying it to an AR model and validate its performance through empirical studies.
  arXiv  Detail & Related papers  (2024-10-19T15:42:49Z)
• Large-scale Time-Varying Portfolio Optimisation using Graph Attention Networks [4.2056926734482065]
  This study utilise 30 years of data on mid-cap firms, creating graphs of firms using distance correlation and the Triangulated Maximally Filtered Graph approach.<n>We show that the portfolio produced by the GAT-based model outperforms all benchmarks and is consistently superior to other strategies over a long period.
  arXiv  Detail & Related papers  (2024-07-22T10:50:47Z)
• Learning Graph Structures and Uncertainty for Accurate and Calibrated Time-series Forecasting [65.40983982856056]
  We introduce STOIC, that leverages correlations between time-series to learn underlying structure between time-series and to provide well-calibrated and accurate forecasts. Over a wide-range of benchmark datasets STOIC provides 16% more accurate and better-calibrated forecasts.
  arXiv  Detail & Related papers  (2024-07-02T20:14:32Z)
• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• Developing An Attention-Based Ensemble Learning Framework for Financial Portfolio Optimisation [0.0]
  We propose a multi-agent and self-adaptive portfolio optimisation framework integrated with attention mechanisms and time series, namely the MASAAT. By reconstructing the tokens of financial data in a sequence, the attention-based cross-sectional analysis module and temporal analysis module of each agent can effectively capture the correlations between assets and the dependencies between time points. The experimental results clearly demonstrate that the MASAAT framework achieves impressive enhancement when compared with many well-known portfolio optimsation approaches.
  arXiv  Detail & Related papers  (2024-04-13T09:10:05Z)
• DiffSTOCK: Probabilistic relational Stock Market Predictions using Diffusion Models [1.9662978733004601]
  We develop an architecture for providing better market predictions conditioned on the historical financial indicators and inter-stock relations. We also provide a novel deterministic architecture MaTCHS which uses Masked Transformer(RTM) to exploit inter-stock relations along with historical stock features.
  arXiv  Detail & Related papers  (2024-03-21T01:20:32Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.