A Review of Stock Market Microstructure

Author(s):  
Carole Comerton-Forde ◽  
James Rydge
2018 ◽  
Vol 44 (1) ◽  
pp. 46-73 ◽  
Author(s):  
DeokJong Jeong ◽  
Sunyoung Park

Purpose The purpose of this paper is to empirically analyze the effect of the increasing connectedness among financial institutions in the Korean financial market, as it affects the market microstructure in the stock market. Thus this work, first, analyzes the trend and characteristics of connectedness in the Korean financial sector. This work then demonstrates the impacts of connectedness on volatility and price discovery in the stock market. Design/methodology/approach The entire Korean financial sector is analyzed from January 1990 to July 2015, including the periods of the 1997 Asian crisis and the 2007/2008 global financial crisis. This paper quantifies the connectedness between financial institutions using network methodology. Densely connectedness specifically refers to the cases in which a node experiences strong-lagged return spillover from and/or to itself. Findings Connectedness is established as an important determinant of stock price discovery. This paper illustrates that connectedness increases on significant economic events such as the 1997 Asian crisis and the 2007/2008 global financial crisis. Furthermore, this paper demonstrates that the more densely connected a particular financial institution, the more volatile the stock price and the less accurate the stock price quality. Research limitations/implications Understanding the financial system from a network perspective has been on the rise after the 2007/2008 global financial crisis. This work helps regulators and policy makers understand the full implications of introducing new policies that can more closely connect financial institutions. Originality/value This paper precisely captures financial institutions’ connectedness by including all types of financial institutions at the micro level. Additionally, this paper links connectedness to market microstructure in the stock market.


Author(s):  
Raihan Ashikin Mohd Nor ◽  
Hawati Janor ◽  
Mohd Hasimi Yaacob ◽  
Noor Azuan Hashim

This paper examines the influence of asymmetric information on foreign capital inflows in ASEAN PLUS THREE (ASEAN+3) countries. Linking capital flows to stock market setting, it substantiates other efforts concerning the debatable issues of the effect of asymmetric information on foreign direct investment (FDI) and foreign portfolio investment (FPI). The asymmetric information is captured through the stock market microstructure perspective on the width and depth dimensions using highly frequency cross sectional data from year 2000 to 2015. Roll and Amivest models are employed to quantify the width and depth aspects of the asymmetric information. Employing the panel data technique, the results demonstrate the significant effect of market transparency on foreign capital inflows specifically the FDI as compared to the FPI. An increase in the width and depth analysis based on the Amivest model signifies a high informational transparency, thus shows a lower asymmetric information which consequently leads to the high foreign capital inflows. The results of the study provide information to the policymakers in monitoring capital inflows on the aspect of market transparency and highlight the importance of the stock market microstructure in assessing the asymmetric information for ASEAN+3 countries.  


2018 ◽  
Vol 22 (5) ◽  
pp. 141-153
Author(s):  
N. A.  Bilev

In modern electronic stock exchanges there is an opportunity to analyze event driven market microstructure data. This data is highly informative and describes physical price formation which makes it possible to find complex patterns in price dynamics. It is very time consuming and hard to find this kind of patterns by handcrafted rules. However, modern machine learning models are able to solve such issues automatically by learning price behavior which is always changing. The present study presents profitable trading system based on a machine learning model and market microstructure data. Data for the research was collected from Moscow stock exchange MICEX and represents a limit order book change log and all market trades of a liquid security for a certain period. Logistic regression model was used and compared to neural network models with different configuration. According to the study results logistic regression model has almost the same prediction quality as neural network models have but also has a high speed of response which is very important for stock market trading. The developed trading system has medium frequency of deals submission that lets it to avoid expensive infrastructure which is usually needed in high-frequency trading systems. At the same time, the system uses the potential of high quality market microstructure data to the full extent. This paper describes the entire process of trading system development including feature engineering, models behavior comparison and creation of trading strategy with testing on historical data.


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