English version N. 54 Studi e Ricerche  (May 2003)
Key words: insider trading, market manipulation, abnormal return, detection of market abuse, alert, diffusion process.

Marcello Minenna
CONSOB, Divisione Intermediari
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In every country with legislation on market abuse, i.e. on market manipulation and insider trading, the repression of these offences is entrusted to supervisory and judicial authorities with powers that vary with the legislation in question. A procedure permitting cases of market abuse to be detected in real time is a need that is strongly felt by financial market supervisory authorities. Such a procedure consists basically in the analysis of the transactions carried out on the market by traders in order to detect anomalies that could be symptomatic of market abuse. The aim of this paper is to develop, through recourse to probability theory, a method for identifying cases of market abuse more effectively.

I wish to thank Emilio Barucci of the University of Pisa for his assistance in searching and analyzing the literature and for his comments and suggestions with regard to the quantitative modelling that underlies the procedure for detecting market abuses. My thanks also go to Veronica Faralli (CORIPE) and Maddalena Lenzi (Consob) for repeatedly reprocessing the data for the different quantitative solutions prepared in order to calibrate the procedure for detecting market abuse, Luca Doveri (CORIPE) and Enrico Maria Scurati for the analyses needed to develop the indicators of market concentration, and Giovanni Portioli, Paola Deriu and Carlo Milia (Consob) for collecting the data and information that permitted the empirical verification of the procedure.      

I am also very grateful to Luigi Spaventa, Claudio Salini (Consob) and Francesco Tuccari for having encouraged the development of this procedure in the belief that quantitative analysis can effectively support supervisory activity. As regards the mathematics contained in Appendix A, I am grateful for the suggestions made by Mavira Mancino of the University of Florence for Section A.2 and by Ennio Arlandi (FINARM) for Section A1.