Evaluating long-horizon event study methodology

Alexander Kogan, Miguel A. Lejeune

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

This study uses a novel method, the Logical Analysis of Data (LAD), to reverse engineer and construct credit risk ratings which represent the creditworthiness of financial institutions and countries. LAD is a data mining method based on combinatorics, optimization, and Boolean logic that utilizes combinatorial search techniques to discover various combinations of attribute values that are characteristic of the positive or negative character of observations. The proposed methodology is applicable in the general case of inferring an objective rating system from archival data, given that the rated objects are characterized by vectors of attributes taking numerical or ordinal values. The proposed approaches are shown to generate transparent, consistent, self-contained, and predictive credit risk rating models, closely approximating the risk ratings provided by some of the major rating agencies. The scope of applicability of the proposed method extends beyond the rating problems discussed in this study and can be used in many other contexts where ratings are relevant. We use multiple linear regression to derive the logical rating scores.

Original languageEnglish (US)
Title of host publicationHandbook of Financial Econometrics and Statistics
PublisherSpringer New York
Pages439-483
Number of pages45
ISBN (Electronic)9781461477501
ISBN (Print)9781461477495
DOIs
StatePublished - Jan 1 2015

All Science Journal Classification (ASJC) codes

  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)
  • Mathematics(all)

Fingerprint

Dive into the research topics of 'Evaluating long-horizon event study methodology'. Together they form a unique fingerprint.

Cite this