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## Stock price jumps: news and volume play a minor role. ArXiv e-prints

Citations: | 26 - 4 self |

### Citations

799 | Do stock prices move too much to be justified by subsequent changes in dividends
- SHILLER
- 1981
(Show Context)
Citation Context ...eterminant of price volatility. There are, however, various pieces of evidence suggesting that this picture is incorrect. Volatility 1is much too high to be explained only by changes in fundamentals =-=[1]-=-. The volatility process itself is random, with highly non-trivial clustering and long-memory properties (for reviews, see e.g. [2, 3, 4]). Many of these properties look very similar to endogenous noi... |

696 |
How Nature Works: The Science of Self-Organized Criticality
- Bak
- 2007
(Show Context)
Citation Context ...e jumps? We believe that the explanation comes from the fact that markets, even when they are ‘liquid’, operate in a regime of vanishing liquidity, and therefore are in a selforganized critical state =-=[31]-=-. On electronic markets, the total volume available in 90.1 0.08 Minute by minute Trade by trade P(V>V p ,|r|>R p ) 0.06 0.04 0.02 0 0 0.01 0.02 0.03 0.04 0.05 0.06 p Figure 6: Tail correlations betw... |

355 |
Distribution of eigenvalues for some sets of random matrices
- Marčenko, Pastur
- 1967
(Show Context)
Citation Context ... and zero otherwise, pi is the average of θt i (i.e. the jump probability pi = T −1 ∑ t θt i ) and T is the number of bins. Most eigenvalues of c are seen to lie within the Marcenko-Pastur noise band =-=[26, 27]-=-, but a few stand out, in particular the ‘market’ eigenvalue with a eigenvector v 1 i close to uniform across all stocks: v1 i ≈ N −1/2 , ∀i. A market jump can be defined such that the indicator χ t =... |

332 | 2001: Empirical properties of asset returns: stylized facts and statistical issues
- Cont
(Show Context)
Citation Context ...y 1is much too high to be explained only by changes in fundamentals [1]. The volatility process itself is random, with highly non-trivial clustering and long-memory properties (for reviews, see e.g. =-=[2, 3, 4]-=-). Many of these properties look very similar to endogenous noise generated by complex, non-linear systems with feedback, such as turbulent flows [5, 6, 7, 8, 9], stick balancing dynamics [10], etc. [... |

256 |
Continuous auctions and insider trading. Econometrica
- KYLE
- 1985
(Show Context)
Citation Context ...e jumps. But if an investor really had valuable private information he would trade as to reveal as little as possible of this information. This is best exemplified by Kyle’s model of informed trading =-=[28]-=-, where the trades of the insider are perfectly hidden in the uninformed flow. Nonetheless, it was recently claimed that large price jumps are due to large incoming volumes that destabilize the order ... |

197 | What Moves Stock Prices
- Cutler, Poterba, et al.
- 1989
(Show Context)
Citation Context ...r appears that most of the volatility arises from trading itself, through the very impact of trades on prices. This was the conclusion reached by Cutler, Poterba and Summers in an early seminal paper =-=[13]-=- (see also [14]). More recently, the authors of [15] used high frequency data to decompose the volatility into an impact component and a news component, and found the former to be dominant (see also [... |

137 |
A theory of powerlaw distributions in financial market fluctuations
- Gabaix, Gopikrishnan, et al.
(Show Context)
Citation Context ...der are perfectly hidden in the uninformed flow. Nonetheless, it was recently claimed that large price jumps are due to large incoming volumes that destabilize the order book and lead to large swings =-=[29]-=-. This point of view was rather convincingly challenged in [18]. Here, we confirm explicitly that large price jumps are in fact not induced by large transaction volumes. We do this by studying tail co... |

84 |
Fluctuations and response in financial markets: the subtle nature of ‘random price changes. Quantitative Finance
- BOUCHAUD, GEFEN, et al.
- 2004
(Show Context)
Citation Context ...as turbulent flows [5, 6, 7, 8, 9], stick balancing dynamics [10], etc. [11]. On liquid stocks, there is in fact little sign of high frequency mean reversion that one could attribute to noise traders =-=[12]-=-. It rather appears that most of the volatility arises from trading itself, through the very impact of trades on prices. This was the conclusion reached by Cutler, Poterba and Summers in an early semi... |

81 |
Noise dressing in financial correlation matrices
- Laloux, Cizeau, et al.
- 1999
(Show Context)
Citation Context ... and zero otherwise, pi is the average of θt i (i.e. the jump probability pi = T −1 ∑ t θt i ) and T is the number of bins. Most eigenvalues of c are seen to lie within the Marcenko-Pastur noise band =-=[26, 27]-=-, but a few stand out, in particular the ‘market’ eigenvalue with a eigenvector v 1 i close to uniform across all stocks: v1 i ≈ N −1/2 , ∀i. A market jump can be defined such that the indicator χ t =... |

64 |
Scaling of the distribution of price fluctuations of individual companies
- Plerou, Gopikrishnan, et al.
- 1999
(Show Context)
Citation Context ...s m(t). The number of s-jumps as a function of s is shown in Fig. 2; it is seen to decay as ≈ s −4 , as expected from the well known approximately inverse cubic distribution of high frequency returns =-=[20]-=-. We note once again that this is a very broad distribution, meaning that the number of extreme events is in fact quite large. For example, for the already rather high value s = 4 and for only 166 sto... |

57 |
Theory of Financial Risk and Derivative Pricing
- Bouchaud, Potters
- 2003
(Show Context)
Citation Context ...y 1is much too high to be explained only by changes in fundamentals [1]. The volatility process itself is random, with highly non-trivial clustering and long-memory properties (for reviews, see e.g. =-=[2, 3, 4]-=-). Many of these properties look very similar to endogenous noise generated by complex, non-linear systems with feedback, such as turbulent flows [5, 6, 7, 8, 9], stick balancing dynamics [10], etc. [... |

56 | Forecasting multifractal volatility
- Calvet, Fisher
(Show Context)
Citation Context ...ong-memory properties (for reviews, see e.g. [2, 3, 4]). Many of these properties look very similar to endogenous noise generated by complex, non-linear systems with feedback, such as turbulent flows =-=[5, 6, 7, 8, 9]-=-, stick balancing dynamics [10], etc. [11]. On liquid stocks, there is in fact little sign of high frequency mean reversion that one could attribute to noise traders [12]. It rather appears that most ... |

48 | 2002): “Events That Shook the Market
- Fair
(Show Context)
Citation Context ...most of the volatility arises from trading itself, through the very impact of trades on prices. This was the conclusion reached by Cutler, Poterba and Summers in an early seminal paper [13] (see also =-=[14]-=-). More recently, the authors of [15] used high frequency data to decompose the volatility into an impact component and a news component, and found the former to be dominant (see also [12, 16]). Here,... |

29 | Relation between bid–ask spread, impact and volatility in order-driven markets
- WYART, BOUCHAUD, et al.
- 2008
(Show Context)
Citation Context ...ading itself, through the very impact of trades on prices. This was the conclusion reached by Cutler, Poterba and Summers in an early seminal paper [13] (see also [14]). More recently, the authors of =-=[15]-=- used high frequency data to decompose the volatility into an impact component and a news component, and found the former to be dominant (see also [12, 16]). Here, we want to confirm this conclusion d... |

22 |
law relaxation in a complex system: Omori law after a financial market crash, preprint
- Lillo, Mantegna, et al.
(Show Context)
Citation Context ... than the rather narrow peak corresponding to endogenous jumps. In both cases, we find (Figure 5) that the relaxation of the excess-volatility follows a power-law in time σ(t) − σ(∞) ∝ t −β (see also =-=[22, 23]-=-). The exponent of the decay is, however, markedly different in the two cases: for news jumps, we find β ≈ 1, whereas for endogenous jumps one has β ≈ 1/2. Our results are compatible with those of [22... |

20 | Volatility processes and volatility forecast with long memory. Quantitative Finance, 4:70–86. Ich erkläre hiermit an Eides Statt, dass ich meine Doktorarbeit “Multivariate Multifractal Models: Estimation of Parameters and Applications to Risk Management” - Zumbach - 2004 |

19 | What really causes large price changes
- Farmer, Gillemot, et al.
- 2004
(Show Context)
Citation Context ...t was recently claimed that large price jumps are due to large incoming volumes that destabilize the order book and lead to large swings [29]. This point of view was rather convincingly challenged in =-=[18]-=-. Here, we confirm explicitly that large price jumps are in fact not induced by large transaction volumes. We do this by studying tail correlations between absolute value of returns and volume. Define... |

15 |
Volatility Clustering and Scaling for Financial Time Series due to Attractor Bubbling
- Krawiecki, Hołyst, et al.
(Show Context)
Citation Context ...]). Many of these properties look very similar to endogenous noise generated by complex, non-linear systems with feedback, such as turbulent flows [5, 6, 7, 8, 9], stick balancing dynamics [10], etc. =-=[11]-=-. On liquid stocks, there is in fact little sign of high frequency mean reversion that one could attribute to noise traders [12]. It rather appears that most of the volatility arises from trading itse... |

14 |
What causes crashes
- Sornette, Malevergne, et al.
- 2003
(Show Context)
Citation Context ...’ to a (possibly endogenous) collective market or sector jump. We find that the volatility pattern around jumps and around news is quite different, confirming that these are distinct market phenomena =-=[17]-=-. We also provide direct evidence that large transaction volumes are not responsible for large price jumps, as also shown in [30]. We conjecture that most price jumps are in fact due to endogenous liq... |

14 |
On a multi-timescale statistical feedback model for volatility fluctuations. arXiv:physics.soc-ph/0507073
- Borland, Bouchaud
- 2005
(Show Context)
Citation Context ...4 and s = 8 (see Fig. 5), although a three parameter fit is compatible with β(s = 8) > β(s = 4) for jumps, as predicted by the multifractal random walk model [17] and the multiscale feedback model of =-=[25]-=-. If we now average the volatility pattern around all news, we find a rather modest peak height (the increase is only 30-40% of the baseline level), confirming the result of figure 3 b: news are often... |

11 |
The dynamics of financial markets - mandelbrot’s multifractal cascades, and beyond. Wilmott Magazine
- Borland, Bouchaud, et al.
- 2005
(Show Context)
Citation Context ...y 1is much too high to be explained only by changes in fundamentals [1]. The volatility process itself is random, with highly non-trivial clustering and long-memory properties (for reviews, see e.g. =-=[2, 3, 4]-=-). Many of these properties look very similar to endogenous noise generated by complex, non-linear systems with feedback, such as turbulent flows [5, 6, 7, 8, 9], stick balancing dynamics [10], etc. [... |

11 |
Short-term market reaction after extreme price changes of liquid stocks
- Zawadowski, Andor, et al.
(Show Context)
Citation Context ... than the rather narrow peak corresponding to endogenous jumps. In both cases, we find (Figure 5) that the relaxation of the excess-volatility follows a power-law in time σ(t) − σ(∞) ∝ t −β (see also =-=[22, 23]-=-). The exponent of the decay is, however, markedly different in the two cases: for news jumps, we find β ≈ 1, whereas for endogenous jumps one has β ≈ 1/2. Our results are compatible with those of [22... |

9 |
2001): “Turbulence in financial markets: the surprising explanatory power of simple cascade models” Quantitative Finance 1
- Lux
(Show Context)
Citation Context ...ong-memory properties (for reviews, see e.g. [2, 3, 4]). Many of these properties look very similar to endogenous noise generated by complex, non-linear systems with feedback, such as turbulent flows =-=[5, 6, 7, 8, 9]-=-, stick balancing dynamics [10], etc. [11]. On liquid stocks, there is in fact little sign of high frequency mean reversion that one could attribute to noise traders [12]. It rather appears that most ... |

8 |
On-off intermittency in a human balancing task’, Phys
- Cabrera, Milton
(Show Context)
Citation Context ...g. [2, 3, 4]). Many of these properties look very similar to endogenous noise generated by complex, non-linear systems with feedback, such as turbulent flows [5, 6, 7, 8, 9], stick balancing dynamics =-=[10]-=-, etc. [11]. On liquid stocks, there is in fact little sign of high frequency mean reversion that one could attribute to noise traders [12]. It rather appears that most of the volatility arises from t... |

8 | Bouchaud J-P. Experts' Earning Forecasts: Bias, Herding And Gossamer Information
- Guedj
(Show Context)
Citation Context ...ing intraday news are rare. Note that companies indeed tend to publish big surprises – like earnings – in overnight. Even earning forecasts by analysts are not taken very seriously by the market (see =-=[21]-=- for a discussion of this point). Another clear-cut difference between jumps and news is the volatility pattern around the two types of events. In Fig. 4 a), we show the average absolute oneminute ret... |

5 |
Do supply and demand drive stock prices?, Quantitative Finance
- Hopman
(Show Context)
Citation Context ...] (see also [14]). More recently, the authors of [15] used high frequency data to decompose the volatility into an impact component and a news component, and found the former to be dominant (see also =-=[12, 16]-=-). Here, we want to confirm this conclusion directly, using different news feeds synchronized with price time series. Our main result is indeed that most large jumps (defined more precisely below) are... |

4 |
Multifractality of DEM/$ rates’, Cowles Foundation Discussion Paper 1165
- Fisher, Calvet, et al.
- 1999
(Show Context)
Citation Context ...ong-memory properties (for reviews, see e.g. [2, 3, 4]). Many of these properties look very similar to endogenous noise generated by complex, non-linear systems with feedback, such as turbulent flows =-=[5, 6, 7, 8, 9]-=-, stick balancing dynamics [10], etc. [11]. On liquid stocks, there is in fact little sign of high frequency mean reversion that one could attribute to noise traders [12]. It rather appears that most ... |

2 |
Turbulence: the Kolmogorov legacy
- Frisch
- 1997
(Show Context)
Citation Context |