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, and the modified Lagrange regularization method is proposed to alleviate the impact of potential LPPLS model over-fitting to better estimate bubble start time and market regime change. The goal here is to determine the nature of the bubbles and crashes (i.e., whether they are endogenous due to their own price evolution or exogenous due to external market and/or policy influences). A systematic market event analysis is performed and correlated to the Bitcoin bubbles detected. Based on the daily LPPLS confidence indictor from 1 December 2019 to 24 June 2021, this analysis has disclosed that the Bitcoin boom from November 2020 to mid-January 2021 is an endogenous bubble, stemming from the self-reinforcement of cooperative herding and imitative behaviors of market players, while the price spike from mid-January 2021 to mid-April 2021 is likely an exogenous bubble driven by extrinsic events including a series of large-scale acquisitions and adoptions by well-known institutions such as Visa and Tesla. Finally, the utilities of multi-resolution LPPLS analysis in revealing both short-term changes and long-term states have also been demonstrated in this study.

This study investigates how U.S. Federal Reserve interest rate cuts during the 2019–2020 easing cycle influenced the performance of equity mutual funds, with a particular emphasis on contrast between growth and value investment styles. Using an event study framework, we examine abnormal returns, cumulative abnormal returns, and risk-adjusted performance metrics, including those based on both 30 days static and rolling Jensen’s alpha and Sharpe ratios, across short-term (30-day) and long-term (6-month and 1-year) windows surrounding three major rate cut events. Our empirical results show that growth funds significantly outperform value funds following rate reductions, especially over longer horizons. This performance advantage is more pronounced in risk-adjusted measures and strengthens when incorporating rolling dynamics, indicating that and asymmetric sensitivity of fund styles to interest rate changes, shaped by differences in duration exposure and investor sentiment. Overall, this study offers novel insights into how monetary policy influences fund-level dynamics beyond broad market movements and deepens the understanding of monetary transmission in asset management by incorporating time-varying performance metrics.

Due to spectacular gains during periods of rapid price increase and unpredictably large drops, Bitcoin has become a popular emergent asset class over the past few years. In this paper, we are interested in predicting the crashes of Bitcoin market. To tackle this task, we propose a framework for deep learning time series classification based on contrastive learning. The proposed framework is evaluated against six machine learning (ML) and deep learning (DL) baseline models, and outperforms them by 15.8% in balanced accuracy. Thus, we conclude that the contrastive learning strategy significantly enhance the model’s ability of extracting informative representations, and our proposed framework performs well in predicting Bitcoin crashes.