Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints

TL;DR

This paper introduces a new portfolio optimization method that combines dependency networks derived from VAR and FEVD, dynamic stock selection using centrality measures, and VaR-based risk management, demonstrating superior performance over traditional buy-and-hold strategies.

Contribution

It presents a novel integration of social network analysis, time series forecasting, and risk constraints for portfolio design, with empirical validation on S&P 500 data.

Findings

MST-based strategies outperform buy-and-hold benchmarks.

NNAR-enhanced models achieve higher returns (63.74%) compared to benchmarks (18%).

Network and centrality measures effectively identify influential stocks.

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.