Stock return forecasting and portfolio construction using an event-conditional mixture-of-experts framework

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Abstract

The problems we investigated are whether two types of economic information can improve the selection of forecasting models, and whether any resulting gains persist in portfolio construction. The first is the timing of scheduled macroeconomic announcements. The second is how closely the current market resembles past (calm, stressed, or recovery) periods. We proposed an event-conditional mixture-of-experts model (Event-MoE). The model combines a long short-term memory (LSTM) network, a gated recurrent unit (GRU), and a transformer encoder. An event-conditional gate sets the mixture from a calendar of Federal Open Market Committee (FOMC), Consumer Price Index (CPI), and nonfarm-payroll (NFP) releases. A regime layer trains one such mixture per historical regime and blends them by how closely the present resembles each. Since per-stock data are limited, the experts are pooled across all stocks. An augmented variant adds a linear expert, a load-balanced gate, and one-step GARCH and HAR-RV volatility forecasts. The models were evaluated both statistically and through long-only portfolio performance, net transaction costs, and an equal-weight benchmark. Statistical significance was assessed using the Diebold–Mariano and Jobson–Korkie/Memmel tests, and robustness was verified through a walk-forward check. The central finding was that forecast accuracy and portfolio value are distinct objectives. So a more-accurate forecast does not automatically translate into a better portfolio.

Keywords

mixture of experts / event-conditional gating / regime similarity / return forecasting / portfolio construction / macroeconomic announcements / deep learning

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