Forecasting Financial Extremes: A Network Degree Measure of Super-exponential Growth

TL;DR

This paper introduces a new network-based indicator to measure super-exponential growth in stock prices, demonstrating strong predictive power for financial extremes across major indices, outperforming existing pattern recognition methods.

Contribution

The paper presents a novel network degree measure for stock price growth, providing a robust predictor of market extremes and improving upon existing indicators.

Findings

The indicator effectively predicts market peaks and troughs.

It outperforms the LPPL pattern recognition indicator.

The predictive power remains robust across parameter variations.

Abstract

Investors in stock market are usually greedy during bull markets and scared during bear markets. The greed or fear spreads across investors quickly. This is known as the herding effect, and often leads to a fast movement of stock prices. During such market regimes, stock prices change at a super-exponential rate and are normally followed by a trend reversal that corrects the previous over reaction. In this paper, we construct an indicator to measure the magnitude of the super-exponential growth of stock prices, by measuring the degree of the price network, generated from the price time series. Twelve major international stock indices have been investigated. Error diagram tests show that this new indicator has strong predictive power for financial extremes, both peaks and troughs. By varying the parameters used to construct the error diagram, we show the predictive power is very robust. The new indicator has a better performance than the LPPL pattern recognition indicator.