trading friction
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2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Immas Nurhayati ◽  
Irwan Adi Ekaputra ◽  
Zaäfri Ananto Husodo

2015 ◽  
Vol 15 (1) ◽  
pp. 133-153 ◽  
Author(s):  
Ravichandran K. Subramaniam ◽  
Shyamala Dhoraisingham Samuel ◽  
Sakthi Mahenthiran

ABSTRACTThe study examines the association between the different types of blockholdings, the levels of corporate social responsibility (CSR) disclosure, and liquidity of shares traded in Malaysian public listed companies (PLCs) on Bursa Malaysia. The sample consists of 194 most actively traded PLCs for the year 2009. A CSR index was constructed using the criteria used by a leading newspaper that provides an annual CSR award. We suggest that such CSR awards help legitimize the business activities of PLCs in the eyes of a government promoting sustainable business practices. The study finds that while insider blockholdings increases the trading friction and reduces liquidity, the nongovernmental institutional blockholdings improve the liquidity of shares traded on Bursa Malaysia. Moreover, the government institutional blockholdings interacts with the CSR disclosure levels to affect the liquidity of the shares traded. These findings make important contributions to emerging capital markets where government regulations incentivize CSR disclosures and the involvement of institutional investors in the governance of PLCs are the norm.Data Availability: The authors are willing to share the data for use by others in extending or replicating results reported in their articles (send request to Ms. Dhoraisingham at: [email protected]).


1999 ◽  
Vol 10 (06) ◽  
pp. 1149-1162 ◽  
Author(s):  
GIULIA IORI

We propose a model with heterogeneous interacting traders which can explain some of the stylized facts of stock market returns. A generalized version of the Random Field Ising Model (RFIM) is introduced to describe trading behavior. Imitation effects, which induce agents to trade, can generate avalanches in trading volume and large gaps in demand and supply. A trade friction is introduced which, by responding to price movements, creates a feedback mechanism on future trading and generates volatility clustering.


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