Can we predict crashes? The case of the Brazilian stock market

Cajueiro, Daniel O.; Tabak, Benjamin Miranda; Werneck, Filipe K.

doi:10.1016/j.physa.2008.12.010

Cited by 26 publications

(10 citation statements)

References 39 publications

(47 reference statements)

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“…Based on a number of recent empirical studies , Cajueiro et al 2009, Zunino et al 2009), we suggest several measures to prevent self-organization in a market from bubbling up to the Critical Point via herding, connectivity, and imitation. If our resilience interventions are to minimize the longer-term effects of market crashes-such as the current "Great Recession"-which result from slower, selforganizing endogenous process dynamics-it is critical that we create resilience against endogenous processes rather than attempt to prevent anomalies foreign to the U.S. stock-trading system-such as the Asian meltdown of 1987, the Russian bond default of 1998, the dot.com crash of 2000, or the current meltdowns in Dubai (2009) "Relaxation time" characterizes the way a dynamical system returns to equilibrium after experiencing significant deviations.…”

Section: Internal Vs External Shocksmentioning

confidence: 99%

Adding Resilience to Financial Markets: Toward a Volatility-based, Power-Law Indicated, Resilience Initiation Point

Yalamova¹,

McKelvey²

2010

PsycEXTRA Dataset

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Section: Internal Vs External Shocksmentioning

confidence: 99%

Adding Resilience to Financial Markets: Toward a Volatility-based, Power-Law Indicated, Resilience Initiation Point

Yalamova¹,

McKelvey²

2010

PsycEXTRA Dataset

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“…Since the introduction of the JLS model, there has been plenty of theoretical development of the LPPLS bubble framework, in particular expanding on the underlying mechanisms (Sornette, 1998;Sornette and Johansen, 1998;Ide and Sornette, 2002) and on model calibration (Zhou and Sornette, 2006a;Filimonov and Sornette, 2013;Lin et al, 2014). Concurrently, the LPPLS literature has developed empirically with both post-mortem studies of past bubbles as well as real-time ex-ante successful diagnostics of bubbles in a variety of financial markets, including western stock markets (Johansen et al, 1999;Sornette and Zhou, 2006), emerging stock markets (Johansen and Sornette, 2001;Giordano and Mannella, 2006;Zhou and Sornette, 2009;Cajueiro et al, 2009;Jiang et al, 2010), real-estate markets Sornette, 2006b, 2008), oil markets (Sornette et al, 2009), forex markets (Johansen et al, 1999;Matsushita et al, 2006) and so on.…”

Section: Introductionmentioning

confidence: 99%

“Speculative Influence Network” during financial bubbles: application to Chinese stock markets

Sornette

2017

J Econ Interact Coord

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We introduce the Speculative Influence Network (SIN) to decipher the causal relationships between sectors (and/or firms) during financial bubbles. The SIN is constructed in two steps. First, we develop a Hidden Markov Model (HMM) of regime-switching between a normal market phase represented by a geometric Brownian motion (GBM) and a bubble regime represented by the stochastic super-exponential Sornette-Andersen (2002) bubble model. The calibration of the HMM provides the probability at each time for a given security to be in the bubble regime. Conditional on two assets being qualified in the bubble regime, we then use the transfer entropy to quantify the influence of the returns of one asset i onto another asset j, from which we introduce the adjacency matrix of the SIN among securities. We apply our technology to the Chinese stock market during the period 2005-2008, during which a normal phase was followed by a spectacular bubble ending in a massive correction. We introduce the Net Speculative Influence Intensity (NSII) variable as the difference between the transfer entropies from i to j and from j to i, which is used in a series of rank ordered regressions to predict the maximum loss (%MaxLoss) endured during the crash. The sectors that influenced other sectors the most are found to have the largest losses. There is a clear prediction skill obtained by using the transfer entropy involving industrial sectors to explain the %MaxLoss of financial institutions but not vice versa. We also show that the bubble state variable calibrated on the Chinese market data corresponds well to the regimes when the market exhibits a strong price acceleration followed by clear change of price regimes. Our results suggest that SIN may contribute significant skill to the development of general linkage-based systemic risks measures and early warning metrics. *

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“…Feedback trading is often cited as the reason of the bubble (see Sornette (2006, 2009),Cajueiro, Tabak andWerneck (2009), and Johansen, Ledoit, and Sornette (2003)). See alsoAbreu and Brunnermeier (2003).…”

mentioning

confidence: 99%

When Overconfident Traders Meet Feedback Traders

Boco¹,

Germain²,

Rousseau³

2021

Finance

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We develop a model in which informed overconfident market participants and informed rational speculators trade against trend-chasers. In this model positive feedback traders act as Computer Based Trading (CBT) and lead to positive feedback loops. In line with empirical findings we find a positive relationship between the volatility of prices and the size of the price reversal. The presence of positive feedback traders leads to a higher degree of trading activity by both types of informed traders. Overconfidence can lead to less price volatility and more efficient prices. Moreover, overconfident traders may be better off than their rational counterparts.

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Can we predict crashes? The case of the Brazilian stock market

Cited by 26 publications

References 39 publications

Adding Resilience to Financial Markets: Toward a Volatility-based, Power-Law Indicated, Resilience Initiation Point

Adding Resilience to Financial Markets: Toward a Volatility-based, Power-Law Indicated, Resilience Initiation Point

“Speculative Influence Network” during financial bubbles: application to Chinese stock markets

When Overconfident Traders Meet Feedback Traders

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