Determinants of price reversal in high-frequency trading: empirical evidence from Indonesia

The paper examines the bias introduced by metaorder limit prices when measuring quality of execution services on financial market. While evaluating the quality of execution services, observed execution costs should be adjusted for metaorder participation rate, size and duration to ensure that they are comparable across execution service providers. One of the exogenous factors which may bias measured execution costs are the different metaorder limit prices in the sample. Currently, there are no proposed methods to normalize for this bias. In the research, the difference in execution costs for metaorders with different limit prices was examined by implementing a limit order book simulation model. It was discovered that the difference in metaorder limit prices is a source of significant heterogeneity in the execution cost distribution. However, we were able to prove that when market agents trade with constant intensities, the difference in execution costs for metaorders with different limit prices is fully explained by their realized participation rate. As a result, financial institution may assess quality of execution services for metaorders without any reservations about differences in metaorders limit prices as long as execution costs are adjusted for different participation rates.

This paper provides a practical, empirical and theoretical framework that allows investment managers to evaluate stock exchanges’ market quality when choosing among different plausible international trading venues. To compare trading exchanges, it extends the hypothesis of market microstructure invariance to trading across exchanges. A measure ω, the ratio of the market-wide volatility to microstructure invariance, is introduced. The paper computes ω for the exchanges around the world. Its value for the NSE (India) is 24.5%, the Korea Exchange (Korea) is 7.9%, the Shanghai Exchange (China) is 3.5%, and the Shenzhen Exchange (China) is 4.4%, which is significantly different from that of major exchanges in the USA (NYSE – 0.8%, NASDAQ – 1.3%) and Europe (LSE (UK) – 0.4). This country risk dimension clearly identifies which equity exchanges cannot hold their own direct correlational hedges and therefore mandatorily require derivative positions, and has significant implications for the decision making of global long-short equity asset allocators in the Asian listed equity markets.

Researchers have scrutinized the link between investor sentiment and stock market liquidity globally, yet few have delved into this dynamic in emerging markets, especially China. Utilizing a sample of 1,839 publicly listed companies in China from 2010 to 2019, this study applies firm- and year-fixed-effects models to explore the nexus between investor sentiment and stock illiquidity, employing the Amihud measure for stock illiquidity assessment. The outcomes of these fixed-effect regressions illustrate a significantly positive relationship between investor sentiment and stock liquidity in the Chinese market. The positive link is more evident in scenarios characterized by high firm leverage, rapid revenue growth, larger corporations, greater institutional ownership, higher stock volatility, and lower book-to-market ratios. Intriguingly, this analysis incorporates the quadratic term of investor sentiment to examine the potential for a nonlinear dynamic between stock illiquidity and investor sentiment. The findings elucidate that the effect of investor sentiment on stock liquidity diminishes at elevated levels of sentiment, revealing a nonlinear inverse U-shaped relationship. The positive correlation between investor sentiment and stock liquidity persists across the three divisions of the Chinese Shenzhen Stock Exchange and remains robust using alternative liquidity measures, such as Roll’s impact and zeros impact. Addressing causality concerns, current investor sentiment appears to influence subsequent liquidity levels. These results provide valuable perspectives for policymakers, business executives, and investors in the stock market.

Acknowledgment
This research was funded by the Department of Education of Zhejiang Province General Program [Y202353438], the Wenzhou Association for Science and Technology—Service and Technology Innovation Program [jczc0254], the Wenzhou-Kean University Student Partnering with Faculty Research Program [WKUSPF2023004], and the Wenzhou-Kean University International Collaborative Research Program [ICRP2023002].