We reanalyze high resolution data from the New York Stock Exchange and find a monotonic (but not power law) variation of the mean value per trade, the mean number of trades per minute and the mean trading activity with company capitalization. We show that the second moment of the traded value distribution is finite. Consequently, the Hurst exponents for the corresponding time series can be calculated. These are, however, non-universal: The persistence grows with larger capitalization and this results in a logarithmically increasing Hurst exponent. A similar trend is displayed by intertrade time intervals. Finally, we demonstrate that the distribution of the intertrade times is better described by a multiscaling ansatz than by simple gap scaling. Understanding the financial market as a self-adaptive, strongly interacting system is a real interdisciplinary challenge, where physicists strongly hope to make essential contributions [1,2,3]. The enthusiasm is understandable as the breakthrough of the early 70's in statistical physics taught us how to handle strongly interacting systems with a large number of degrees of freedom. The unbroken development of this and related disciplines brought up several concepts and models like (fractal and multifractal) scaling, frustrated disordered systems, or far from equilibrium phenomena and we have obtained very efficient tools to treat them. Many of us are convinced, that these and similar ideas and techniques will be helpful to understand the mechanisms of the economy. In fact, there have been quite successful attempts along this line [4,5,6]. An ubiquitous aspect of strongly interacting systems is the lack of finite scales. The best understood examples are second order equilibrium phase transitions where renormalization group theory provides a general explanation of scaling and universality [7]. It seems that some features of the stock market can indeed be captured by these concepts: For example, the so called inverse cube law of the distribution of logarithmic returns shows a quite convincing data collapse for different companies with a good fit to an algebraically decaying tail [8,9].
PACSStudies in econophysics concentrate on the possible analogies, although there are important differences between physical and financial systems. This is, of course, a trivial statement -it is enough to refer to the abovementioned self-adaptivity, to the possibility of influencing a