Tree of Alpha: How MST Networks and Neural Forecasts Outperformed the S&P 500
What if picking winning stocks wasn’t about finding isolated outperformers, but about tracing the invisible web of influence that binds the market together? A recent paper proposes exactly that—building portfolios from the market’s structural core, using a dynamic network of directional dependencies extracted from stock returns. At the heart of the approach lies a clever pipeline that fuses econometrics, network theory, and forecasting: Stocks are modeled in pairs using Vector Autoregression (VAR) over rolling 120-day windows. Forecast Error Variance Decomposition (FEVD) quantifies how much each stock influences others, generating a directional dependency matrix. This matrix is symmetrized and distilled into a Minimum Spanning Tree (MST)—a sparse, cycle-free map of the market’s backbone. From this tree, the portfolio selects the top-5 most connected stocks (by degree centrality) in each window—stocks that act as systemic hubs. Then, instead of equal weighting, capital is allocated inversely proportional to each stock’s Value at Risk (VaR) or proportionally to its Sharpe ratio. Stocks with lower downside risk or better risk-adjusted returns receive higher weights. ...