title: Using trading strategies to detect phase transitions in financial markets.
abstract: We show that the log-periodic power law singularity model (LPPLS), a mathematical embodiment of positive feedbacks between agents and of their hierarchical dynamical organization, has a significant predictive power in financial markets. We find that LPPLS-based strategies significantly outperform the randomized ones and that they are robust with respect to a large selection of assets and time periods. The dynamics of prices thus markedly deviate from randomness in certain pockets of predictability that can be associated with bubble market regimes. Our hybrid approach, marrying finance with the trading strategies, and critical phenomena with LPPLS, demonstrates that targeting information related to phase transitions enables the forecast of financial bubbles and crashes punctuating the dynamics of prices.
authors.affiliation: Department of Management, Technology, and Economics, ETH Zürich, 8092 Zürich, Switzerland.
authors.forename: Z
authors.initials: Z
authors.lastname: Forró
authors.affiliation: Department of Management, Technology, and Economics, ETH Zürich, 8092 Zürich, Switzerland.
authors.forename: R
authors.initials: R
authors.lastname: Woodard
authors.affiliation: Department of Management, Technology, and Economics, ETH Zürich, 8092 Zürich, Switzerland.
authors.forename: D
authors.initials: D
authors.lastname: Sornette