Bubbles are usually chaotic but can be predictable, provided their formation matches the log
periodic power law (LPPL) with unique stylized facts. We investigated Green Bubble …

Financial market crash is one of the most extreme manifestations of financial market
instability. It is of great significance to study its underlying structure and its forecasting …

In this study, the Log-Periodic Power Law Singularity (LPPLS) model is adopted for real-time
identification and monitoring of Bitcoin bubbles and crashes using different time scale data …

Incentivizing businesses to lower carbon emissions and trade back excess carbon
allowances paved the way for rapid growth in carbon credit ETFs. The use of carbon …

In a complex financial system, what is the forecasting performance of macro and micro
evolution models of Econophysics on asset prices? For this problem, from the perspective of …

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 …

We herein employ an alternative approach to model the financial bubbles prior to crashes
and fit a log-periodic power law (LPPL) to IIGPS countries (Italy, Ireland, Greece, Portugal …

Research suggests that a significant number of those investing in cryptocurrencies do not
follow what we might call rational, profit-maximizing behavior. We also know that with the …

Purpose This study aims to predict stock market crashes identified by the CMAX approach
(current index level relative to historical maximum) during periods of global and local events …

Forecasting Bitcoin's returns continues to be a challenging endeavor for both scholars and
practitioners. In this paper, we train a random forest model on a variety of features, with the …