Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints

• URL: http://arxiv.org/abs/2507.20039v1
• Date: Sat, 26 Jul 2025 18:53:39 GMT
• Title: Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints
• Authors: Zihan Lin, Haojie Liu, Randall R. Rojas,
• Abstract summary: This study proposes a novel portfolio optimization framework that integrates statistical social network analysis with time series forecasting and risk management.<n>Using daily stock data from the S&P 500 ( 2020-2024), we construct dependency networks via Vector Autoregression ( VAR) and Forecast Error Variance Decomposition (FEVD)<n>FEVD breaks down the VAR's forecast error variance to quantify how much each stock's shocks contribute to another's uncertainty information we invert to form influence-based edge weights in our network.<n>A dynamic portfolio is constructed using the top-ranked stocks, with capital allocated based on Value at Risk (
• Score: 8.107171581224312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study proposes a novel portfolio optimization framework that integrates statistical social network analysis with time series forecasting and risk management. Using daily stock data from the S&P 500 (2020-2024), we construct dependency networks via Vector Autoregression (VAR) and Forecast Error Variance Decomposition (FEVD), transforming influence relationships into a cost-based network. Specifically, FEVD breaks down the VAR's forecast error variance to quantify how much each stock's shocks contribute to another's uncertainty information we invert to form influence-based edge weights in our network. By applying the Minimum Spanning Tree (MST) algorithm, we extract the core inter-stock structure and identify central stocks through degree centrality. A dynamic portfolio is constructed using the top-ranked stocks, with capital allocated based on Value at Risk (VaR). To refine stock selection, we incorporate forecasts from ARIMA and Neural Network Autoregressive (NNAR) models. Trading simulations over a one-year period demonstrate that the MST-based strategies outperform a buy-and-hold benchmark, with the tuned NNAR-enhanced strategy achieving a 63.74% return versus 18.00% for the benchmark. Our results highlight the potential of combining network structures, predictive modeling, and risk metrics to improve adaptive financial decision-making.

Related papers

• Conditional Forecasting of Margin Calls using Dynamic Graph Neural Networks [0.0]
  We introduce a novel Dynamic Graph Neural Network architecture for solving conditional $m$-steps ahead forecasting problems in temporal financial networks. Our work shows that the network dynamics can be successfully incorporated into stress-testing practices, thus providing regulators and policymakers with a crucial tool for systemic risk monitoring.
  arXiv  Detail & Related papers  (2024-10-30T17:55:41Z)
• MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
  We propose an immersion-aware model trading framework that facilitates data provision for services while ensuring privacy through federated learning (FL)<n>We design an incentive mechanism to incentivize metaverse users (MUs) to contribute high-value models under resource constraints.<n>We develop a fully distributed dynamic reward algorithm based on deep reinforcement learning, without accessing any private information about MUs and other MSPs.
  arXiv  Detail & Related papers  (2024-10-25T16:20:46Z)
• GARCH-Informed Neural Networks for Volatility Prediction in Financial Markets [0.0]
  We present a new, hybrid Deep Learning model that captures and forecasting market volatility more accurately than either class of models are capable of on their own. When compared to other time series models, GINN showed superior out-of-sample prediction performance in terms of the Coefficient of Determination ($R2$), Mean Squared Error (MSE), and Mean Absolute Error (MAE)
  arXiv  Detail & Related papers  (2024-09-30T23:53:54Z)
• Application of Deep Learning for Factor Timing in Asset Management [21.212548040046133]
  More flexible models have better performance in explaining the variance in factor premium of the unseen period. For flexible models like neural networks, the optimal weights based on their prediction tend to be unstable. We verify that tilting down the rebalance frequency according to the historical optimal rebalancing scheme can help reduce the transaction costs.
  arXiv  Detail & Related papers  (2024-04-27T21:57:17Z)
• Towards Robust Federated Learning via Logits Calibration on Non-IID Data [49.286558007937856]
  Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks. Recent studies have shown that FL is vulnerable to adversarial examples, leading to a significant drop in its performance. In this work, we adopt the adversarial training (AT) framework to improve the robustness of FL models against adversarial example (AE) attacks.
  arXiv  Detail & Related papers  (2024-03-05T09:18:29Z)
• Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
  This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
  arXiv  Detail & Related papers  (2023-10-02T12:33:28Z)
• 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)
• $\clubsuit$ CLOVER $\clubsuit$: Probabilistic Forecasting with Coherent Learning Objective Reparameterization [42.215158938066054]
  We augment an MQForecaster neural network architecture with a modified multivariate Gaussian factor model that achieves coherence by construction.<n>We call our method the Coherent Learning Objective Reparametrization Neural Network (CLOVER)<n>In comparison to state-of-the-art coherent forecasting methods, CLOVER achieves significant improvements in scaled CRPS forecast accuracy, with average gains of 15%.
  arXiv  Detail & Related papers  (2023-07-19T07:31:37Z)
• HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE [113.47287249524008]
  It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting. We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
  arXiv  Detail & Related papers  (2023-06-05T12:58:13Z)
• Forecasting Financial Market Structure from Network Features using Machine Learning [0.6999740786886535]
  We propose a model that forecasts market correlation structure from link- and node-based financial network features using machine learning. We provide empirical evidence using three different network filtering methods to estimate market structure, namely Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST) and Dynamic Threshold Networks (DTN) Experimental results show that the proposed model can forecast market structure with high predictive performance with up to $40%$ improvement over a time-invariant correlation-based benchmark.
  arXiv  Detail & Related papers  (2021-10-22T12:51:32Z)
• WaveCorr: Correlation-savvy Deep Reinforcement Learning for Portfolio Management [1.0499611180329804]
  We present a new portfolio policy network architecture for deep reinforcement learning (DRL) WaveCorr consistently outperforms other architectures with an impressive 3%-25% improvement in terms of average annual return. We also measured an improvement of a factor of up to 5 in the stability of performance under random choices of initial asset ordering and weights.
  arXiv  Detail & Related papers  (2021-09-14T22:52:46Z)
• Deep Stock Predictions [58.720142291102135]
  We consider the design of a trading strategy that performs portfolio optimization using Long Short Term Memory (LSTM) neural networks. We then customize the loss function used to train the LSTM to increase the profit earned. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA.
  arXiv  Detail & Related papers  (2020-06-08T23:37:47Z)
• Neural Networks and Value at Risk [59.85784504799224]
  We perform Monte-Carlo simulations of asset returns for Value at Risk threshold estimation. Using equity markets and long term bonds as test assets, we investigate neural networks. We find our networks when fed with substantially less data to perform significantly worse.
  arXiv  Detail & Related papers  (2020-05-04T17:41:59Z)

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.