Article: A two-step machine learning approach to predict S&P 500 bubbles.
2020 Volume 48, Issue 13-15, Page(s) 2776–2794
Abstract: In this paper, we are interested in predicting the bubbles in the S&P 500 stock market with a two ... the bubbles in the S&P 500 index using a widely recognized right-tailed unit root test. Then, SVM is employed ...
| 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 |
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| Language | English |
| Publishing date | 2020-09-22 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 1476802-1 |
| ISSN | 1360-0532 ; 0266-4763 |
| ISSN (online) | 1360-0532 |
| ISSN | 0266-4763 |
| DOI | 10.1080/02664763.2020.1823947 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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