Intraday Patterns of Liquidity on the Warsaw Stock Exchange before and after the Outbreak of the COVID-19 Pandemic

Abstract

A highly significant feature of the stock market is its efficiency, which is associated with information efficiency. However, the liquidity of stock on the market is its essential characteristic. The inflow of information in highly liquid markets allows for the maintenance of high information efficiency. The COVID-19 pandemic affected many aspects related to stock markets, including their liquidity. The impact of the pandemic is so multidimensional that there are still areas that need to be investigated. One of them is the intraday liquidity patterns on the stock markets. Therefore, the present paper aims to verify the existence of intraday liquidity patterns on the Warsaw Stock Exchange in three periods: before, during and after the panic caused by the first wave of the COVID-19 pandemic. The results confirmed the existence of a U-shaped intraday distribution of the number of transactions and their trading. This outcome highlights the importance of the first and last minutes of a trading session. The COVID-19 pandemic resulted in the domination of WSE transactions of small individual investors who feared the loss of value of their assets, selling them on the stock exchange. In the pandemic, the average percentage change between transactions increased.

Market Quality and Short-Selling Ban during the COVID-19 Pandemic: A High-Frequency Data Approach

“…Autocorrelation or co-movements generate "U"shaped or inverted "J" patterns as noted by Martens et al (2002), Hua and Li (2011), and Tian and Guo (2007), among others; Kottaridi et al (2020a) pointed out that the intraday pattern is more pronounced in specific days of the week in the case of returns and volatility. During the COVID-19 period, although the "U"-shape persisted, Kubiczek and Tuszkiewicz (2022) pointed out that private investors operated more, decompensating the usual distribution of the percentages of data during the day that usually exists at the opening and close of the trading day.…”

Forecasting of Bitcoin Illiquidity Using High-Dimensional and Textual Features

“…Assuming that w, d, h, and m represent the weekly, daily, hourly, and minute intervals, the interval of each of these values is equal to w = [1; :::; W], d = [1; 2; :::; 7], h = [00; 01; :::; 23], and m = [00; 01; :::; 59], and W refers to the weekly interval in data collection. According to the research in [38], to measure volatility and volume patterns, we introduce the relative measures of volatility and volume in days, hours, and minutes. For this purpose, we measure volatility using the absolute return instead of the squared return.…”

“…In Ref. [38], the authors studied daily liquidity patterns in the Warsaw Stock Exchange in three periods before, during, and after the panic caused by the first wave of the COVID-19 pandemic. Also, the effect of different periods was studied using different correlation approaches.…”

COVID-19 Media Chatter and Macroeconomic Reflectors on Black Swan: A Spanish and Indian Stock Markets Comparison

“…The mayhem of the COVID-19 pandemic in the form of extreme volatility and uncertainty induced anxiety in investors' minds and chaotic behavior in financial markets across the world (Cepoi 2020;Kamaludin et al 2021;Sharma et al 2021;Yu et al 2022;Zeng et al 2022). The steep dependence of financial assets on media sentiment pertinent to the ongoing pandemic triggers efficiency in market microstructure (Dash et al 2014;Ding and Qin 2020;Barky et al 2022;Kubiczek and Tuszkiewicz 2022;Saetia and Yokrattanasak 2023). Despite the sizeable literature on demystifying the nexus and spillover dynamics of different financial variables (Ali et al 2022b;Zhang and Mao 2022;Yu and Xiao 2022;Sinlapates et al 2023), the scarcity of robust predictive architecture required to yield the future figures for stock markets in developed and emerging economies simultaneously is an emerging problem.…”