Detecting market bubbles: A generalized LPPLS neural network model

Juntao Ma and Chenchen Li

Economics Letters, 2024, vol. 244, issue C

Abstract: To enhance bubble detection capabilities, we introduce two significant improvements to the Log-Periodic Power Law Singularity (LPPLS) model: (1) a novel fitting approach, which yields more accurate predictions of critical price distributions within a single sample window; (2) a restructured neural network approach further enhances the estimations of the probability distributions of the critical points across both time and price dimensions, and it can be fine-tuned with real-world data. The simulation and practical applications to typical asset price bubbles in cryptocurrencies, commodities, and equity indices demonstrate that our refined model, the Generalized-LPPLS Neural Network (G-LPPLS-NN), outperforms all other models we examined in terms of predictive accuracy for critical point distributions.

Keywords: Bubble detection; LPPLS; Fitting methodology; Neural network; Fine-tuning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165176524004877
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:244:y:2024:i:c:s0165176524004877

DOI: 10.1016/j.econlet.2024.112003

Access Statistics for this article

Economics Letters is currently edited by Economics Letters Editorial Office

More articles in Economics Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().