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Combination Forecasting Reversion Strategy for Online Portfolio Selection

机译：在线投资组合选择的组合预测回归策略

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摘要

Machine learning and artificial intelligence techniques have been applied to construct online portfolio selection strategies recently. A popular and state-of-the-art family of strategies is to explore the reversion phenomenon through online learning algorithms and statistical prediction models. Despite gaining promising results on some benchmark datasets, these strategies often adopt a single model based on a selection criterion (e.g., breakdown point) for predicting future price. However, such model selection is often unstable and may cause unnecessarily high variability in the final estimation, leading to poor prediction performance in real datasets and thus non-optimal portfolios. To overcome the drawbacks, in this article, we propose to exploit the reversion phenomenon by using combination forecasting estimators and design a novel online portfolio selection strategy, named Combination Forecasting Reversion (CFR), which outputs optimal portfolios based on the improved reversion estimator. We further present two efficient CFR implementations based on online Newton step (ONS) and online gradient descent (OGD) algorithms, respectively, and theoretically analyze their regret bounds, which guarantee that the online CFR model performs as well as the best CFR model in hindsight. We evaluate the proposed algorithms on various real markets with extensive experiments. Empirical results show that CFR can effectively overcome the drawbacks of existing reversion strategies and achieve the state-of-the-art performance.
机译：机器学习和人工智能技术最近已被用于构建在线投资组合选择策略。一个流行且最新的策略系列是通过在线学习算法和统计预测模型来探索逆向现象。尽管在一些基准数据集上获得了可喜的结果，但是这些策略通常采用基于选择标准（例如，故障点）的单个模型来预测未来价格。但是，这样的模型选择通常是不稳定的，并且可能导致最终估计中不必要的高可变性，从而导致实际数据集中的预测性能较差，从而导致非最优投资组合。为了克服这些缺点，在本文中，我们建议通过使用组合预测估计量来利用回归现象，并设计一种新颖的在线投资组合选择策略，称为组合预测回归（CFR），该策略会基于改进的回归估计量输出最优投资组合。我们进一步分别提出了两种基于在线牛顿步（ONS）和在线梯度下降（OGD）算法的有效CFR实现，并从理论上分析了它们的后悔界限，从而保证了在线CFR模型的性能和事后最佳的CFR模型。我们通过大量实验评估了在各种实际市场上提出的算法。实验结果表明，CFR可以有效克服现有还原策略的弊端，并可以实现最新的性能。

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