Microblogging services can enrich the information investors use to make financial decisions on the stock markets. As liquidity has immediate consequences for a trader’s movements, this risk is an attractive area of interest for both academics and those who participate in the financial markets. This paper focuses on market liquidity and studies the impact on liquidity and trading costs of the popular Twitter microblogging service. Sentiment analysis extracted from Twitter and different popular liquidity measures were gathered to analyze the relationship between liquidity and investors’ opinions. The results, based on the analysis of the S&P 500 Index, found that the investors’ mood had little influence on the spread of the index.

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.

Pakistan's political instability has pushed its economic system to the brink of collapse. Considering this political turmoil, this study addresses the behavior of liquidity providers against microblogging-opinionated information. The behavioral perspective was quantified through multiple linear regressions, the Bayesian theorem, and the vector error correction technique. Before this political crisis, sentiment indicators were linked to the liquidity-conditional cost for the same trading session. In the political uncertainty environment, pessimistic opinions were the sole concern of the liquidity providers during the same trading session. The liquidity facilitator was observed to price the liquidity in light of pessimistic sentiments. The Bayesian theorem suggested a higher posterior probability for the occurrence of the liquidity-facilitating cost in response to the pessimistic sentiments. Nevertheless, the past time series changes for the sentiment indicators were irrelevant in determining changes in cost-based liquidity for the next trading session.

The impact on economic aspects of the COVID-19 is continuing under discussion. This study unveils effects of the pandemic on relationship dynamics between liquidity cost and stock market returns. Using the time series and machine learning techniques, the analysis is based on the Dow Jones Industrial Average (DJI) index. If the entire dataset was examined, the liquidity cost was found to be positive and significantly related to the DJI index returns. From the VAR estimation, the market returns were significantly explained by past values of the liquidity cost. The statistical Granger-causality was also observed between variables. If the relationship was analyzed during peak restrictions, the results were changed. The liquidity cost was observed to be negative and insignificantly related to the DJI index returns. The market returns were not associated with lags of the liquidity cost. In addition, the Granger-causality was not found between variables. If effects associated with easing restrictions were examined, the liquidity cost was found to be positive and significantly associated with returns on the DJI index. Meanwhile, the returns were more sensitive to the liquidity cost. However, the market returns were not explained by lags of the liquidity cost. The liquidity cost did not Granger-cause returns. The findings suggest that the liquidity cost must be priced in returns due to the pandemic-related uncertainty.

In the literature of asset pricing, this paper introduces a new method to estimate the cost-based market liquidity (CBML), that is, the bid-ask spread. The proposed model of spread proxy positively correlates with the examined low-frequency spread proxies for a larger dataset. The introduced approach provides potential implications in important aspects. Unlike in the Roll bid-ask spread model and the CHL bid-ask estimator, the CBML model consistently estimates market liquidity and trading cost for the entire dataset. Additionally, the CBML estimator steadily measures positive spreads, unlike in the CS bid-ask spread model. The construction of the proposed approach is not computationally intensive and can be considered for distinct studies at both market and firm levels.

In the wake of the COVID-19 pandemic, this paper links the cost-based market liquidity risk to investors' sentiment analysis through microblogging content. The study performed a sentiment analysis of tweets and considered distinct measures of cost-based market liquidity. In financial market, liquidity risk and execution cost are other major concerns for both academics and those who participate in trading. The literature in asset pricing usually distinct the informed trades and uninformed trades. Due to the growth of internet and Web 2.0 phenomenon, microblogging social service providers are considerably big data sources for various purposes, including the financial market behavior modeling and prediction. The empirical results, based on the analysis of Australian Securities Exchange (ASX), found that Twitter sentiment indicators were relevant in the forecasting of market liquidity and execution cost at market level. In the COVID-19 outbreak, the investors' pessimistic perceptions about the ASX caused adverse impact on its liquidity and final cost paid by traders.