Abstract
This study proposes a framework to diagnose stock market crashes and predict the subsequent price rebounds. Based on the observation of anomalous changes in stock correlation networks during market crashes, we extend the log-periodic power-law model with a metric that is proposed to measure network anomalies. To calculate this metric, we design a prediction-guided anomaly detection algorithm based on the extreme value theory. Finally, we proposed a hybrid indicator to predict price rebounds of the stock index by combining the network anomaly metric and the visibility graph-based log-periodic power-law model. Experiments are conducted based on the New York Stock Exchange Composite Index from 4 January 1991 to 7 May 2021. It is shown that our proposed method outperforms the benchmark log-periodic power-law model on detecting the 12 major crashes and predicting the subsequent price rebounds by reducing the false alarm rate. This study sheds light on combining stock network analysis and financial time series modeling and highlights that anomalous changes of a stock network can be important criteria for detecting crashes and predicting recoveries of the stock market.
Highlights
A stock market crash is one of the most significant systemic risks of the modern financial system, causing significant losses for investors
The fundamental insight of modeling financial crashes with the logperiodic power-law (LPPL) model is to capture a particular pattern of the price time series, which can be described as “the faster-than-exponential decline accompanied by accelerating oscillations” [4]
This study proposes a framework to diagnose stock market crashes and predict the subsequent price rebounds by jointly modeling plunges in the stock index price and abnormal changes in the stock correlation network
Summary
A stock market crash is one of the most significant systemic risks of the modern financial system, causing significant losses for investors. Rebounds of the stock market after crashes usually signal the recovery of investors’ confidence or the taking effect of bailout policies. It is critical for investors and policy makers to detect stock market crashes and predict price rebounds. Different methods have been proposed to diagnose the stock market crashes and predict rebounds. Yan et al [3] adopt the LPPL model to study stock market crashes by considering them as the “mirror images” of financial bubbles, which are known as “negative bubbles”. It may not be sufficient to use only the price time series of the stock index when diagnosing stock market crashes. The price time series of the stock index can describe the overall fluctuation of the stock market, but it ignores the complex interactions among multiple assets
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