Outliers in financial time series data: Outliers, margin debt, and economic recession

Outliers in financial time series data are different from that in cross-sectional data in terms of the treatment and the detection. First, outliers in time series can be the focus of analysis itself, such as outliers in margin debt to indicate an overheating market. Second, the outlier detection in time series should be accompanied by decomposition to exclude inherent patterns. Unfortunately, there is a lack of consensus on the best decomposition method. Thus, we propose an ensemble model that combines multiple decomposition methods. Using the approach, we found that the outliers in margin debt are strong predictors of a recession. •An outlier in financial time series data is concerning treatment and detection.•The outlier itself is treated as attractive since it indicates the abnormal state.•Outlier detection in non-stationary data requires a divergent approach.•We proposed an ensemble model based on the optimization framework for detection.•The outliers in the remainder of margin debt are strong recession indicators.