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<p>We present a regime-switching decision support system (DSS) for dynamic portfolio construction that links market data with investor-specific return goals through four transparent modules. In <i>Module 1 (Data acquisition &amp; features)</i>, monthly market signals (e.g., SPX returns, VIX, 10-year yield, and relative volume) are standardized into a market-state feature vector. In <i>Module 2 (Market-state estimation)</i>, an unsupervised clustering model fitted on multi-decade history yields interpretable regimes, and a supervised classifier maps features to months-ahead regime probabilities. In <i>Module 3 (User-specific layer)</i>, a static risk profile and a goal-tracking rule combine the forecast with a user-defined annual return target to produce a discrete risk level <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(u^{(t)}\)</EquationSource> </InlineEquation>. In <i>Module 4 (Optimization policy)</i>, conditional on <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(u^{(t)}\)</EquationSource> </InlineEquation>, the DSS solves either a robust mean–variance optimization program or a CVaR minimization with asset-class constraints, and it rebalances whenever the risk level or regime forecast changes. We implement the DSS on a diversified universe of 256 liquid U.S.-listed assets, including equities, government and corporate bond ETFs, and commodity and alternative asset ETFs. In two rolling-horizon backtests on this universe—2008–2011 (bear) and 2013–2016 (bull)—the DSS outperforms a 70/30 benchmark and single-optimizer baselines, achieving higher performance metrics. These results demonstrate the practical viability and extensibility of regime-aware, goal-driven portfolio construction.</p>

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A regime-switching decision support system for dynamic portfolio optimization

  • 期刊论文

  • 发表日期：

    2026年4月30日

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  • Marcus Grunnesjö,
  • David Islip,
  • Xuanze Li,
  • Yanni Lu,
  • Roy H. Kwon

摘要

We present a regime-switching decision support system (DSS) for dynamic portfolio construction that links market data with investor-specific return goals through four transparent modules. In Module 1 (Data acquisition & features), monthly market signals (e.g., SPX returns, VIX, 10-year yield, and relative volume) are standardized into a market-state feature vector. In Module 2 (Market-state estimation), an unsupervised clustering model fitted on multi-decade history yields interpretable regimes, and a supervised classifier maps features to months-ahead regime probabilities. In Module 3 (User-specific layer), a static risk profile and a goal-tracking rule combine the forecast with a user-defined annual return target to produce a discrete risk level \(u^{(t)}\) . In Module 4 (Optimization policy), conditional on \(u^{(t)}\) , the DSS solves either a robust mean–variance optimization program or a CVaR minimization with asset-class constraints, and it rebalances whenever the risk level or regime forecast changes. We implement the DSS on a diversified universe of 256 liquid U.S.-listed assets, including equities, government and corporate bond ETFs, and commodity and alternative asset ETFs. In two rolling-horizon backtests on this universe—2008–2011 (bear) and 2013–2016 (bull)—the DSS outperforms a 70/30 benchmark and single-optimizer baselines, achieving higher performance metrics. These results demonstrate the practical viability and extensibility of regime-aware, goal-driven portfolio construction.