NYSE交易活动波动的非平凡缩放
Non-trivial scaling of fluctuations in the trading activity of NYSE
摘要：
复杂系统包含大量相互作用的元件，其动态并不总是先验已知的。在这些情况下-为了揭示其关键特征-我们必须转向经验方法，其中之一是最近由梅内塞斯和巴拉巴西介绍的。它是基于这样的观察：对于成分的活度f_i（t），标准差与平均值之间存在幂律关系：sigma_i ~<f_i>^alpha。对于股票市场交易活动（交易价值），观察到超过5个数量级的良好缩放，指数α = 0.72。这种不平凡的缩放的起源可以追溯到交易率<N>和它们的平均大小之间的比例<V>。在<V><N>纽约证券交易所的1000家最大的公司中，人们发现~ ^0.69。模型独立的计算表明，这两种类型的缩放可以映射到彼此，误差条之间的协议。最后，如果我们在增加的时间尺度上观察波动，直到20天，α值会持续增加。
英文摘要：
Complex systems comprise a large number of interacting elements, whose dynamics is not always a priori known. In these cases -- in order to uncover their key features -- we have to turn to empirical methods, one of which was recently introduced by Menezes and Barabasi. It is based on the observation that for the activity f_i(t) of the constituents there is a power law relationship between the standard deviation and the mean value: sigma_i ~ <f_i>^alpha. For stock market trading activity (traded value), good scaling over 5 orders of magnitude with the exponent alpha = 0.72 was observed. The origin of this non-trivial scaling can be traced back to a proportionality between the rate of trades <N> and their mean sizes <V>. One finds <V> ~ <N>^0.69 for the ~1000 largest companies of New York Stock Exchange. Model independent calculations show that these two types of scaling can be mapped onto each other, with an agreement between the error bars. Finally, there is a continuous increase in alpha if we look at fluctuations on an increasing time scale up to 20 days.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
