nep-mst New Economics Papers
on Market Microstructure
Issue of 2018‒04‒16
five papers chosen by
Thanos Verousis

  1. Catch me if you can. Can human observers identify insiders in asset markets? By Thomas Stöckl; Stefan Palan
  2. Cluster analysis of stocks using price movements of high frequency data from National Stock Exchange By Charu Sharma; Amber Habib; Sunil Bowry
  4. Stock Market Trading in the Aftermath of an Accounting Scandal. By Sane, Renuka
  5. Where do electronic markets come from? Regulation and the transformation of financial exchanges By Castelle, Michael; Millo, Yuval; Beunza, Daniel; Lubin, David C.

  1. By: Thomas Stöckl (Department Business Administration Online, Management Center Innsbruck); Stefan Palan (Department of Banking and Finance, University of Graz)
    Abstract: Securities regulators around the globe face the challenge of identifying trades based on inside information. We study human observers' ability to identify informed traders and investigate which trading patterns are indicative of informed trading using experimental asset markets. We furthermore test how the behavioral response of informed traders to the threat of detection and punishment impacts observers' detection abilities. We find that market trading data carries information which correlates with informed trading activity. Observers partly succeed in recognizing and using this information to identify in-formed traders.
    Date: 2018–04–03
  2. By: Charu Sharma (Shiv Nadar University, UP); Amber Habib (Shiv Nadar University, UP); Sunil Bowry (Shiv Nadar University, UP)
    Abstract: This paper aims to develop new techniques to describe joint behavior of stocks, beyond regression and correlation. For example, we want to identify the clusters of the stocks that move together. Our work is based on applying Kernel Principal Component Analysis(KPCA) and Functional Principal Component Analysis(FPCA) to high frequency data from NSE. Since we dealt with high frequency data with a tick size of 30 seconds, FPCA seems to be an ideal choice. FPCA is a functional variant of PCA where each sample point is considered to be a function in Hilbert space L^2. On the other hand, KPCA is an extension of PCA using kernel methods. Results obtained from FPCA and Gaussian Kernel PCA seems to be in synergy but with a lag. There were two prominent clusters that showed up in our analysis, one corresponding to the banking sector and another corresponding to the IT sector. The other smaller clusters were seen from the automobile industry and the energy sector. IT sector was seen interacting with these small clusters. The learning gained from these interactions is substantial as one can use it significantly to develop trading strategies for intraday traders.
    Date: 2018–03
  3. By: Ioane Muni Toke (FiQuant - Chaire de finance quantitative - Ecole Centrale Paris)
    Abstract: We develop a Markovian model that deals with the volume offered at the best quote of an electronic order book. The volume of the first limit is a stochastic process whose paths are periodically interrupted and reset to a new value, either by a new limit order submitted inside the spread or by a market order that removes the first limit. Using applied probability results on killing and resurrecting Markov processes, we derive the stationary distribution of the volume offered at the best quote. All proposed models are empirically fitted and compared, stressing the importance of the proposed mechanisms.
    Keywords: killing and resurrecting Markov processes,aggressive market orders,limit order book,volume of the best quote,aggressive limit orders
    Date: 2017–09
  4. By: Sane, Renuka (National Institute of Public Finance and Policy)
    Abstract: In this paper, I study the impact of fraud revelation on trading behaviour of investors. I ask if investors with direct exposure to stock market fraud (treated investors) are more likely to cash out of the stock market than investors with no direct exposure to fraud (control investors)? Using daily investor account holdings data from the National Stock Depository Limited (NSDL), the largest depository in India, I find that treated investors cash out almost 10.6 percentage points of their overall portfolio relative to control investors post the crisis. The cashing out is largely restricted to the bad stock. Over the period of a month, there is no difference in the trading behaviour of the treated and control investors. This paper, for the first time, is able to capture trading behaviour on a daily basis for an extended period of time instead of basing the analysis on household survey data, or observing investors at monthly or yearly frequency.
    Keywords: fraud ; stock market trading ; individual investors ; India
    JEL: D1 G1 G3
    Date: 2018–04
  5. By: Castelle, Michael; Millo, Yuval; Beunza, Daniel; Lubin, David C.
    Abstract: The practices of high-frequency trading (HFT) are dependent on automated financial markets, especially those produced by securities exchanges electronically interconnected with competing exchanges. How did this infrastructural and organizational state of affairs come to be? Employing the conceptual distinction between fixed-role and switch-role markets, we analyse the discourse surrounding the design and eventual approval of the Securities and Exchange Commission’s Regulation of Exchanges and Alternative Trading Systems (Reg ATS). We find that the disruption of the exchange industry at the hands of automated markets was produced through an interweaving of both technological and political change. This processual redefinition of the ‘exchange’, in addition, may provide a suggestive precedent for understanding contemporary regulatory crises generated by other digital marketplace platforms.
    Keywords: financial markets; production markets; regulation; stock exchanges; technology; marketplace platforms
    JEL: F3 G3
    Date: 2016–09–26

This nep-mst issue is ©2018 by Thanos Verousis. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.