Statistical Properties of Market Collective Responses

Wang, Shanshan; Neusüß, Sebastian; Guhr, Thomas

doi:10.2139/ssrn.3072835

Cited by 3 publications

(3 citation statements)

References 34 publications

(45 reference statements)

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“…In our analysis, we view the road sections as interacting constituents. This is formally similar to our previous analyses of stocks as interacting constituents in financial markets [13,14,17,18], but there are obvious differences: Road sections encode a geographical information, i.e, they represent a true topology. Furthermore, the non-stationarity contains seasonalities and periodicities, e.g., rush hours, and hence strong non-Markovian features.…”

Section: Introductionsupporting

confidence: 56%

Collective behavior in the North Rhine-Westphalia motorway network

Wang,

Gartzke,

Schreckenberg

et al. 2021

Preprint

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To understand the dynamics on complex networks, measurement of correlations is indispensable. In a motorway network, it is not sufficient to collect information on fluxes and velocities on all individual links, i.e. parts of the freeways between ramps and highway crosses. The interdependencies and mutual connections are also of considerable interest. We analyze correlations in the complete motorway network in North Rhine-Westphalia, the most populous state in Germany. We view the motorway network as a complex system consisting of road sections which interact via the motion of vehicles, implying structures in the corresponding correlation matrices. In particular, we focus on collective behavior, i.e. coherent motion in the whole network or in large parts of it. To this end, we study the eigenvalue and eigenvector statistics and identify significant sections in the motorway network. We find collective behavior in these significant sections and further explore its causes. We show that collectivity throughout the network cannot directly be related to the traffic states (free, synchronous and congested) in Kerner's three-phase theory. Hence, the degree of collectivity provides a new, complementary observable to characterize the motorway network.

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Section: Introductionsupporting

confidence: 56%

Collective behavior in the North Rhine-Westphalia motorway network

Wang,

Gartzke,

Schreckenberg

et al. 2021

Preprint

Self Cite

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“…There are diverse studies focused on the price response [3][4][5]7,13,15,16,19,22,23,29,30,32,[41][42][43][44]. In our opinion, a critical investigation of definitions and methods and how they affect the results is called for.…”

Section: Introductionmentioning

confidence: 99%

Price response functions and spread impact in correlated financial markets

2021

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Recent research on the response of stock prices to trading activity revealed long-lasting effects, even across stocks of different companies. These results imply non-Markovian effects in price formation and when trading many stocks at the same time, in particular trading costs and price correlations. How the price response is measured depends on data set and research focus. However, it is important to clarify how the details of the price response definition modify the results. Here, we evaluate different price response implementations for the Trades and Quotes (TAQ) data set from the NASDAQ stock market and find that the results are qualitatively the same for two different definitions of time scale, but the response can vary by up to a factor of two. Furthermore, we show the key importance of the order between trade signs and returns, displaying the changes in the signal strength. Moreover, we confirm the dominating contribution of immediate price response directly after a trade, as we find that delayed responses are suppressed. Finally, we test the impact of the spread in the price response, detecting that large spreads have stronger impact.

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“…There are diverse studies focused on the price response [1,4,5,6,8,14,16,17,20,23,24,29,31,40,41,42,43]. In our opinion, a critical investigation of definitions and methods and how they affect the results is called for.…”

Section: Introductionmentioning

confidence: 99%

Price response functions and spread impact in correlated financial markets

Henao-Londono,

Krause,

Guhr

2020

Preprint

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Recent research on the response of stock prices to trading activity revealed long lasting effects, even across stocks of different companies. These results imply non-Markovian effects in price formation and when trading many stocks at the same time, in particular trading costs and price correlations. How the price response is measured depends on data set and research focus. However, it is important to clarify, how the details of the price response definition modify the results. Here, we evaluate different price response implementations for the Trades and Quotes (TAQ) data set from the NASDAQ stock market and find that the results are qualitatively the same for two different definitions of time scale, but the response can vary by up to a factor of two. Further, we show the key importance of the order between trade signs and returns, displaying the changes in the signal strength. Moreover, we confirm the dominating contribution of immediate price response directly after a trade, as we find that delayed responses are suppressed. Finally, we test the impact of the spread in the price response, detecting that large spreads have stronger impact.

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Statistical Properties of Market Collective Responses

Cited by 3 publications

References 34 publications

Collective behavior in the North Rhine-Westphalia motorway network

Collective behavior in the North Rhine-Westphalia motorway network

Price response functions and spread impact in correlated financial markets

Price response functions and spread impact in correlated financial markets

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