• Media type: E-Book
  • Title: Detecting Unobserved Heterogeneity in Efficient Prices Via Classifier-Lasso
  • Contributor: Huang, Wenxin [VerfasserIn]; Su, Liangjun [VerfasserIn]; Zhuang, Yuan [VerfasserIn]
  • imprint: [S.l.]: SSRN, 2022
  • Extent: 1 Online-Ressource (82 p)
  • Language: English
  • DOI: 10.2139/ssrn.4201242
  • Identifier:
  • Keywords: Classifier-Lasso ; Efficient Price ; Informed Trading ; Unobserved Heterogeneity
  • Origination:
  • Footnote: In: Journal of Business & Economic Statistics, 2022
    Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 27, 2022 erstellt
  • Description: This paper proposes a new measure of efficient price as a weighted average of bid and ask prices, where the weights are constructed from the bid-ask long-run relationships in a panel error-correction model (ECM). To allow for heterogeneity in the long-run relationships, we consider a panel ECM with latent group structures so that all the stocks within a group share the same long-run relationship and do not otherwise. We extend the Classifier-Lasso method to the ECM to simultaneously identify the individual's group membership and estimate the group-specific long-run relationship. We establish the uniform classification consistency and good asymptotic properties of the post-Lasso estimators under some regularity conditions. Empirically, we find that more than 30% of the Standard \& Poor's (S\&P) 1500 stocks have estimated efficient prices significantly deviating from the midpoint -- a conventional measure of efficient price. Such deviations explored from our data-driven method can provide dynamic information on the extent and direction of informed trading activities
  • Access State: Open Access