A two-step machine learning approach to predict S&P 500 bubbles

DOI10.1080/02664763.2020.1823947OpenAlexW3088142050MaRDI QIDQ5861221

Fatma Başoğlu Kabran, Kamil Demirberk Ünlü

Publication date: 4 March 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2020.1823947

zbMATH Keywords

support vector machinesbubblesmachine learningmacroeconomic indicatorsearly warning

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Related Items (2)

Predicting credit card customer churn using support vector machine based on Bayesian optimization ⋮ Editorial to the special issue: Recent statistical methods for data analysis, applied economics, business & finance

Uses Software

• e1071
• caret
• aplore3
• ElemStatLearn
• psymonitor

Cites Work

• Unnamed Item
• Unnamed Item
• Forecasting stock market movement direction with support vector machine
• Time-series forecasting with a novel fuzzy time-series approach: an example for Istanbul stock market
• The Measurement of Observer Agreement for Categorical Data
• Applied Logistic Regression
• Data Mining with Decision Trees
• TESTING FOR MULTIPLE BUBBLES: HISTORICAL EPISODES OF EXUBERANCE AND COLLAPSE IN THE S&P 500
• TESTING FOR MULTIPLE BUBBLES: LIMIT THEORY OF REAL‐TIME DETECTORS
• Tests for an end-of-sample bubble in financial time series

This page was built for publication: A two-step machine learning approach to predict S&P 500 bubbles