High frequency trading and stock index returns: A nonlinear dynamic analysis

• Published in: Communications in nonlinear science & numerical simulation 2021-06, Vol.97, p.105710, Article 105710
• Main Authors: Cecen, Aydin A., Jain, Pawan, Xiao, Linlan
• Format: Article
• Language: English
• Subjects: Empirical analysis; Entropy; Heavy-tails; High frequencies; High frequency trading; Markov analysis; Markov property; Multi scale entropy; Nonlinear dynamics; Probability; Securities trading; Stock prices
• ISSN: 1007-5704; 1878-7274
• DOI: 10.1016/j.cnsns.2021.105710

•We study the impact of High Frequency Trading (HFT) on the probabilistic properties of the stock returns across five major stock market indexes.•Our results document a decline in Multiscale Entropy (MSE) during the post-HFT periods, suggesting that the presence of HFT makes the index returns more predictable.•We also find that the Markov property does not hold before and after the introduction of HFT in different markets.•We improve on the confidence intervals of Multiscale Entropy (MSE) estimates using a circular bootstrap method.•Collectively, our results demonstrate a decline in market efficiency due to the introduction of HFT/algorithmic trading. 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.