This paper introduces a new algorithm for exploiting time-series predictability-based patterns to obtain an abnormal return, or alpha, with respect to a given benchmark asset pricing model. The algorithm proposes a deterministic daily market timing strategy that decides between being fully invested in a risky asset or in a risk-free asset, with the trading rule represented by a parametric perceptron. The optimal parameters are sought in-sample via differential evolution to directly maximize the alpha. Successively using two modern asset pricing models and two different portfolio weighting schemes, the algorithm was able to discover an undocumented anomaly in the United States stock market cross-section, both out-of-sample and using small transaction costs. The new algorithm represents a simple and flexible alternative to technical analysis and forecast-based trading rules, neither of which necessarily maximizes the alpha. This new algorithm was inspired by recent insights into representing reinforcement learning as evolutionary computation.

中文翻译：

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搜索基于时间序列可预测性的异常

本文介绍了一种新算法，用于利用基于时间序列可预测性的模式来获得关于给定基准资产定价模型的异常回报或 alpha。该算法提出了一种确定性的每日市场时机策略，该策略决定是完全投资于风险资产还是无风险资产，交易规则由参数感知器表示。通过差分进化在样本中寻找最优参数以直接最大化 alpha。连续使用两种现代资产定价模型和两种不同的投资组合加权方案，该算法能够在样本外和使用少量交易成本的情况下发现美国股票市场横截面中未记录的异常情况。新算法代表了技术分析和基于预测的交易规则的一种简单而灵活的替代方案，它们都不一定会使 alpha 最大化。这种新算法的灵感来自最近将强化学习表示为进化计算的见解。