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

  • Received November 27, 2019;
    Accepted February 20, 2020;
    Published March 19, 2020
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  • Article Info
    Volume 17 2020, Issue #1, pp. 175-187
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    1 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

This article analyzes whether the factors of the mechanism of high-frequency trading (HFT) or intraday trading affect the process of price reversal and continuation. The price reversal phenomenon is gaining importance rapidly due to the increasingly intensive use of IT/Fintech-based trading automation facilities on the Indonesia Stock Exchange. However, one knows little about how their trading affects volatility and liquidity pressures that cause price reversals. A new research approach uses the factors of market microstructure mechanism based on high-frequency data (HFD-intraday). The research method uses purposive random sampling, which classified price fractions into three groups, specifically low price, medium price, and high price, which are analyzed by logistic panel regression. The research variables used include price reversal (dependent), stock return, trading volume, transaction frequency, volume/frequency (V/F) proxy, volatility, and liquidity. According to low price model research findings, all variables show a significant effect on price reversal; for medium price model, all variables except liquidity show a significant effect on price reversal; and for high price model, all variables have a significant effect on price reversal, except trading volume and volatility. In conclusion, low price shares tend to have higher price reversal probability compared to continuity because they tend to be liquid, low institutional ownership, and minimal reporting/analysis and are controlled by HFTs (uninformed traders). Some variables are not significant because of the bounce effect around the bid-ask spread.

Many thanks to Armida S. Alisjahbana, Roy H. Sembel, Budiono, Rahardi S. Rahmanto, and the anonymous referee/reviewer for valuable inputs and feedback.

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    • Table 1. Tick size and maximum change (IDR)
    • Table 2. Research samples
    • Table 3. Variable description and measurement
    • Table 4. Descriptive analysis
    • Table 5. White heteroskedasticity test
    • Table 6. Logit white regression treatment
    • Table 7. Determinants of price reversal