New Economics Papers
on Market Microstructure
Issue of 2011‒10‒22
two papers chosen by
Thanos Verousis

  1. Distinguishing manipulated stocks via trading network analysis By Xiao-Qian Sun; Xue-Qi Cheng; Hua-Wei Shen; Zhao-Yang Wang
  2. Time Scales in Futures Markets and Applications By Laurent Schoeffel

  1. By: Xiao-Qian Sun; Xue-Qi Cheng; Hua-Wei Shen; Zhao-Yang Wang
    Abstract: Manipulation is an important issue for both developed and emerging stock markets. For the study of manipulation, it is critical to analyze investor behavior in the stock market. In this paper, an analysis of the full transaction records of over a hundred stocks in a one-year period is conducted. For each stock, a trading network is constructed to characterize the relations among its investors. In trading networks, nodes represent investors and a directed link connects a stock seller to a buyer with the total trade size as the weight of the link, and the node strength is the sum of all edge weights of a node. For all these trading networks, we find that the node degree and node strength both have tails following a power-law distribution. Compared with non-manipulated stocks, manipulated stocks have a high lower bound of the power-law tail, a high average degree of the trading network and a low correlation between the price return and the seller-buyer ratio. These findings may help us to detect manipulated stocks.
    Date: 2011–10
  2. By: Laurent Schoeffel (CEA Saclay)
    Abstract: The probability distribution of log-returns for financial time series, sampled at high frequency, is the basis for any further developments in quantitative finance. In this letter, we present experimental results based on a large set of time series on futures. We show that the t-distribution with $\nu \simeq 3$ gives a nice description of almost all data series considered for a time scale $\Delta t$ below 1 hour. For $\Delta t \ge 8$ hours, the Gaussian regime is reached. A particular focus has been put on the DAX and Euro futures. This appears to be a quite general result that stays robust on a large set of futures, but not on any data sets. In this sense, this is not universal. A technique using factorial moments defined on a sequence of returns is described and similar results for time scales are obtained. Let us note that from a fundamental point of view, there is no clear reason why DAX and Euro futures should present similar behavior with respect to their return distributions. Both are complex markets where many internal and external factors interact at each instant to determine the transaction price. These factors are certainly different for an index on a change parity (Euro) and an index on stocks (DAX). Thus, this is striking that we can identify universal statistical features in price fluctuations of these markets. This is really the advantage of micro-structure analysis to prompt unified approaches of different kinds of markets. Finally, we examine the relation of power law distribution of returns with another scaling behavior of the data encoded into the Hurst exponent. We have obtained $H=0.54 \pm 0.04$ for DAX and $H=0.51 \pm 0.03$ for Euro futures.
    Date: 2011–10

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