Market Liquidity and Its Dimensions: Linking the Liquidity Dimensions to Sentiment Analysis through Microblogging Data

Francisco Guijarro,Ismael Moya-Clemente,Jawad Saleemi

doi:10.3390/jrfm14090394

Francisco Guijarro, Ismael Moya-Clemente + Show 1 more

Open Access

https://doi.org/10.3390/jrfm14090394

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Abstract

Market liquidity has an immediate impact on the execution of transactions in financial markets. Informed counterparty risk is often priced into market liquidity. This study investigates whether microblogging data, as a non-financial information tool, is priced along with market liquidity dimensions. The analysis is based on the Australian Securities Exchange (ASX), and from the results, we conclude that microblogging content in pessimistic periods has a higher impact on liquidity and its dimensions. On a daily basis, pessimistic investor sentiments lead to higher trading costs, illiquidity, a larger price dispersion and a lower trading volume.

Highlights

IntroductionThis work investigates whether microblogging data, as a source of information, can explain liquidity dimensions
This work investigates whether microblogging data, as a source of information, can explain liquidity dimensions.In the behavioral finance literature, emotion-driven market participants with stochastic predictions are gaining a considerable amount of interest
We found that pessimistic sentiments are positively and significantly associated with price impact volumebased liquidity

Summary

Introduction

This work investigates whether microblogging data, as a source of information, can explain liquidity dimensions. In the behavioral finance literature, emotion-driven market participants with stochastic predictions are gaining a considerable amount of interest. Recent research often quantified surveys, message boards (e.g., ragingbull.com, accessed on 19 August 2021) or financial news to construct sentiment indicators for modeling stock market behavior. Researchers are exploring microblogging data for use in both modeling and predicting stock market behavior (Zhang et al 2011). Microblogging sentiment indicators may be more economically meaningful than traditional sources of financial information (Oliveira et al 2017). An abundance of studies can be found that examine microblogging data for financial market prediction. There is still room to explore the impact of microblogging content on various liquidity dimensions

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