This paper proposes and implements methods for determining whether incorporating technical trading rules accurately forecasts systemic risk and improves the performance of out-of-sample portfolios. The proposed methodology considers various trading rules for forecasting and addressing potential systemic risk in portfolio selection problems. The method incorporates major trading rules as early warning systems or alarm rules to detect market failure within diverse reward–risk measures. Methodologically, the alarm rules are integrated into portfolio selection strategies that predict returns using multifactor models. Therefore, the portfolio strategies combine the predictive ability of both technical trading rules and multifactor models. Empirical analyses validate the suggested approaches and evaluate the impacts of different technical trading rules on portfolio selection problems. This paper compares the ex ante sample paths of several portfolio strategies aiming to maximize portfolio wealth using either reward–risk or drawdown-based performance measures. The results show that the proposed methodologies outperform the classic approach in terms of out-of-sample performance.

中文翻译：

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预测投资组合选择中的系统性风险：技术交易规则的作用

本文提出并实施了确定纳入技术交易规则是否能准确预测系统性风险并提高样本外投资组合性能的方法。所提出的方法考虑了各种交易规则，用于预测和解决投资组合选择问题中的潜在系统性风险。该方法结合了主要交易规则作为预警系统或警报规则，以在不同的奖励风险措施中检测市场失灵。在方法论上，警报规则被集成到使用多因子模型预测回报的投资组合选择策略中。因此，投资组合策略结合了技术交易规则和多因素模型的预测能力。实证分析验证了建议的方法，并评估了不同技术交易规则对投资组合选择问题的影响。本文比较了几种投资组合策略的事前样本路径，这些策略旨在使用奖励风险或基于回撤的绩效指标来最大化投资组合财富。结果表明，所提出的方法在样本外性能方面优于经典方法。