Random matrix approach to the dynamics of stock inventory variations

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Zhou, Wei Xing
dc.contributor.author Mu, Guo Hua
dc.contributor.author Kertész, János
dc.date.accessioned 2018-08-21T13:46:55Z
dc.date.available 2018-08-21T13:46:55Z
dc.date.issued 2012-09
dc.identifier.citation Zhou , W X , Mu , G H & Kertész , J 2012 , ' Random matrix approach to the dynamics of stock inventory variations ' New Journal of Physics , vol 14 , 093025 . DOI: 10.1088/1367-2630/14/9/093025 en
dc.identifier.issn 1367-2630
dc.identifier.other PURE UUID: abd86a15-f1d5-4e3d-9d43-44c1ad8105f5
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/random-matrix-approach-to-the-dynamics-of-stock-inventory-variations(abd86a15-f1d5-4e3d-9d43-44c1ad8105f5).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=84867024735&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/27290139/Zhou_2012_New_J._Phys._14_093025.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/33544
dc.description.abstract It is well accepted that investors can be classified into groups owing to distinct trading strategies, which forms the basic assumption of many agent-based models for financial markets when agents are not zero-intelligent. However, empirical tests of these assumptions are still very rare due to the lack of order flow data. Here we adopt the order flow data of Chinese stocks to tackle this problem by investigating the dynamics of inventory variations for individual and institutional investors that contain rich information about the trading behavior of investors and have a crucial influence on price fluctuations. We find that the distributions of cross-correlation coefficient C i j have power-law forms in the bulk that are followed by exponential tails, and there are more positive coefficients than negative ones. In addition, it is more likely that two individuals or two institutions have a stronger inventory variation correlation than one individual and one institution. We find that the largest and the second largest eigenvalues (λ 1 and λ 2) of the correlation matrix cannot be explained by random matrix theory and the projections of investors' inventory variations on the first eigenvector u(λ 1) are linearly correlated with stock returns, where individual investors play a dominating role. The investors are classified into three categories based on the cross-correlation coefficients C V R between inventory variations and stock returns. A strong Granger causality is unveiled from stock returns to inventory variations, which means that a large proportion of individuals hold the reversing trading strategy and a small part of individuals hold the trending strategy. Our empirical findings have scientific significance in the understanding of investors' trading behavior and in the construction of agent-based models for emerging stock markets. en
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries New Journal of Physics en
dc.relation.ispartofseries Volume 14 en
dc.rights openAccess en
dc.subject.other Physics and Astronomy(all) en
dc.subject.other 114 Physical sciences en
dc.title Random matrix approach to the dynamics of stock inventory variations en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department East China University of Science and Technology
dc.contributor.department Budapest University of Technology and Economics
dc.subject.keyword Physics and Astronomy(all)
dc.subject.keyword 114 Physical sciences
dc.identifier.urn URN:NBN:fi:aalto-201808214677
dc.identifier.doi 10.1088/1367-2630/14/9/093025
dc.type.version publishedVersion

Files in this item

Files Size Format View

There are no open access files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search archive

Advanced Search

article-iconSubmit a publication


My Account