ABSTRACT

In this paper, we are interested in predicting the bubbles in the S&P 500 stock market with a two-step machine learning approach that employs a real-time bubble detection test and support vector machine (SVM). SVM as a nonparametric binary classification technique is already a widely used method in financial time series forecasting. In the literature, a bubble is often defined as a situation where the asset price exceeds its fundamental value. As one of the early warning signals, prediction of bubbles is vital for policymakers and regulators who are responsible to take preemptive measures against the future crises. Therefore, many attempts have been made to understand the main factors in bubble formation and to predict them in their earlier phases. Our analysis consists of two steps. The first step is to identify the bubbles in the S&P 500 index using a widely recognized right-tailed unit root test. Then, SVM is employed to predict the bubbles by macroeconomic indicators. Also, we compare SVM with different supervised learning algorithms by using k-fold cross-validation. The experimental results show that the proposed approach with high predictive power could be a favourable alternative in bubble prediction.

摘要

在本文中，我们有兴趣使用两步机器学习方法预测标准普尔 500 股票市场中的泡沫，该方法采用实时泡沫检测测试和支持向量机 (SVM)。SVM 作为一种非参数二元分类技术，已经是金融时间序列预测中广泛使用的方法。在文献中，泡沫通常被定义为资产价格超过其基本价值的情况。作为预警信号之一，泡沫预测对于负责对未来危机采取先发制人措施的政策制定者和监管机构至关重要。因此，已经进行了许多尝试来了解气泡形成的主要因素并在其早期阶段对其进行预测。我们的分析包括两个步骤。第一步是识别 S& 中的泡沫 P 500 指数使用广泛认可的右尾单位根检验。然后，利用支持向量机通过宏观经济指标预测泡沫。此外，我们通过使用 SVM 与不同的监督学习算法进行比较k折交叉验证。实验结果表明，所提出的具有高预测能力的方法可以成为气泡预测的有利替代方案。