Laboratory of Industrial and Information Management/Tampere University of Technology, Tampere, Finland.
Department of Computer Science, School of Science/Aalto University, Espoo, Finland.
PLoS One. 2018 Jun 13;13(6):e0198807. doi: 10.1371/journal.pone.0198807. eCollection 2018.
We identify temporal investor networks for Nokia stock by constructing networks from correlations between investor-specific net-volumes and analyze changes in the networks around dot-com bubble. The analysis is conducted separately for households, financial, and non-financial institutions. Our results indicate that spanning tree measures for households reflected the boom and crisis: the maximum spanning tree measures had a clear upward tendency in the bull markets when the bubble was building up, and, even more importantly, the minimum spanning tree measures pre-reacted the burst of the bubble. At the same time, we find less clear reactions in the minimal and maximal spanning trees of non-financial and financial institutions around the bubble, which suggests that household investors can have a greater herding tendency around bubbles.
我们通过构建投资者特定净成交量之间的相关性网络，识别出诺基亚股票的时间投资者网络，并分析围绕互联网泡沫的网络变化。该分析分别针对家庭、金融和非金融机构进行。我们的结果表明，家庭的生成树指标反映了繁荣和危机：在泡沫形成时的牛市中，最大生成树指标有明显的上升趋势，更重要的是，最小生成树指标提前反应了泡沫的破裂。同时，我们发现非金融和金融机构的最小和最大生成树在泡沫周围的反应不太明显，这表明家庭投资者在泡沫周围可能有更大的跟风倾向。
