TY - JOUR
T1 - High frequency trading and stock index returns
T2 - A nonlinear dynamic analysis
AU - Cecen, Aydin A.
AU - Jain, Pawan
AU - Xiao, Linlan
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/6
Y1 - 2021/6
N2 - This study seeks to understand whether and to what extent High Frequency Trading (HFT) affects the probabilistic properties of the stock returns in five markets. More specifically, it focuses on the impact of HFT/Machine trading on five major stock indices, DAX, Nikkei 225, S&P 500, Russell 2000, and TOPIX. The empirical analysis demonstrates that while the introduction of machine trading and/or HFT appears to make the return series more “predictable” by reducing their Multiscale Entropy, it does not affect the Markov property, which, not surprisingly, does not hold for the entire return series under study.
AB - This study seeks to understand whether and to what extent High Frequency Trading (HFT) affects the probabilistic properties of the stock returns in five markets. More specifically, it focuses on the impact of HFT/Machine trading on five major stock indices, DAX, Nikkei 225, S&P 500, Russell 2000, and TOPIX. The empirical analysis demonstrates that while the introduction of machine trading and/or HFT appears to make the return series more “predictable” by reducing their Multiscale Entropy, it does not affect the Markov property, which, not surprisingly, does not hold for the entire return series under study.
KW - Heavy-tails
KW - High frequency trading
KW - Markov property
KW - Multi scale entropy
UR - http://www.scopus.com/inward/record.url?scp=85099776336&partnerID=8YFLogxK
U2 - 10.1016/j.cnsns.2021.105710
DO - 10.1016/j.cnsns.2021.105710
M3 - Article
AN - SCOPUS:85099776336
SN - 1007-5704
VL - 97
JO - Communications in Nonlinear Science and Numerical Simulation
JF - Communications in Nonlinear Science and Numerical Simulation
M1 - 105710
ER -