Estimation of the probability of informed trading models via an expectation-conditional maximization algorithm

The estimation of the probability of informed trading (PIN) model and its extensions poses significant challenges owing to various computational problems. To address these issues, we propose a novel estimation method called the expectation-conditional-maximization (ECM) algorithm, which can serve as...

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Bibliographic Details
Published in:Financial innovation (Heidelberg) Vol. 11; no. 1; pp. 67 - 37
Main Authors: Ghachem, Montasser, Ersan, Oguz
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 24.01.2025
Springer Nature B.V
SpringerOpen
Subjects:
Accuracy
Adjusted PIN model
Algorithms
C13
C38
ECM
Economics
Economics and Finance
Estimates
Expectation conditional-maximization algorithm
G14
G17
Information asymmetry
Liquidity
Literature reviews
Macroeconomics/Monetary Economics//Financial Economics
Maximum-likelihood estimation
Methods
MPIN
Multilayer probability of informed trading
PIN model
Political Economy/Economic Systems
Private information
Securities markets
C13
C38
Maximum-likelihood estimation
G14
MPIN
G17
ECM
PIN model
Private information
Information asymmetry
Multilayer probability of informed trading
Adjusted PIN model
Expectation conditional-maximization algorithm
ISSN:2199-4730, 2199-4730
Online Access:Get full text
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Summary:The estimation of the probability of informed trading (PIN) model and its extensions poses significant challenges owing to various computational problems. To address these issues, we propose a novel estimation method called the expectation-conditional-maximization (ECM) algorithm, which can serve as an alternative to the existing methods for estimating PIN models. Our method provides optimal estimates for the original PIN model as well as two of its extensions: the multilayer PIN model and the adjusted PIN model, along with its restricted versions. Our results indicate that estimations using the ECM algorithm are generally faster, more accurate, and more memory-efficient than the standard methods used in the literature, making it a robust alternative. More importantly, the ECM algorithm is not limited to the models discussed and can be easily adapted to estimate future extensions of the PIN model.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2199-4730
2199-4730
DOI:10.1186/s40854-024-00729-w