We analyze the complete tick-level stock trading records at the Taiwan Stock Exchange and explore what factors are principally associated with jumps in high frequency. Among the potential candidate variables suggested in the literature, liquidity proxies appear to be primarily associated with signed jumps in high frequency. The results from the least absolute shrinkage and selection operator (LASSO), the elastic net method, and principal component analysis further show that liquidity issues are more important than information or sentiment in understanding sudden and discontinuous price innovations to financial assets in high frequency.
|Effective start/end date||2018/08/01 → 2020/07/31|
- signed jumps
- information asymmetry
- machine learning
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