Dataset Information

The adaptive market hypothesis and high frequency trading.

ABSTRACT: This paper uses NASDAQ order book data for the S&P 500 exchange traded fund (SPY) to examine the relationship between one-minute, informational market efficiency and high frequency trading (HFT). We find that the level of efficiency varies widely over time and appears to cluster. Periods of high efficiency are followed by periods of low efficiency and vice versa. Further, we find that HFT activity is higher during periods of low efficiency. This supports the argument that HFTs seek profits and risk reduction by actively processing information, through limit order additions and cancellations, during periods of lower efficiency and revert to more passive market-making and rebate-generation during periods of higher efficiency. These findings support the argument that the adaptive market hypothesis (AMH) is an appropriate description of how prices evolve to incorporate information.

SUBMITTER: Meng K

PROVIDER: S-EPMC8682897 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The adaptive market hypothesis and high frequency trading.

Meng Ke K Li Shouhao S

PloS one 20211217 12

This paper uses NASDAQ order book data for the S&P 500 exchange traded fund (SPY) to examine the relationship between one-minute, informational market efficiency and high frequency trading (HFT). We find that the level of efficiency varies widely over time and appears to cluster. Periods of high efficiency are followed by periods of low efficiency and vice versa. Further, we find that HFT activity is higher during periods of low efficiency. This supports the argument that HFTs seek profits and r ...[more]

PMID: 34919550

Similar Datasets

Project description:The development of China's National Carbon Market has strengthened the inherent link between the carbon market and the broader energy market, providing a potential for cross-market risk transmission resonance. Studying the risk spillover effects between China's National Carbon Market and the crude oil futures market is of significant practical importance, both in terms of carbon market development and carbon risk management. Based on the Maximal Overlap Discrete Wavelet Transform (MODWT), the price series are decomposed across multiple scales, and the risk spillover effects between the carbon market and the crude oil futures market are examined from both the time domain and the frequency domain. Methods such as wavelet energy decomposition, wavelet correlation, lead-lag analysis, and wavelet coherence are used to explore the mean spillover effects and volatility spillover effects (collectively referred to as risk spillover effects) across various scales. The study finds that China's National Carbon Market exhibits a clear compliance-driven effect, with relatively low market liquidity. The crude oil futures market experiences frequent price fluctuations, primarily driven by long-term factors. In the time domain, the risk transmission resonance between the carbon market and the crude oil futures market is high, with significant positive correlations observed at the D1 to D4 scales, and noticeable mean spillover effects. In the frequency domain, at the D3 to D4 scales, the carbon market and the crude oil futures market exhibit similar volatility frequencies, indicating strong volatility spillover effects. Based on these findings, it is recommended that the trading volume of the carbon market be gradually increased to improve market liquidity. Furthermore, the risk monitoring and early warning mechanisms of China's National Carbon Market should be improved. For carbon-emitting companies, enhancing awareness of carbon asset management and making informed investment and hedging decisions based on the correlation between the two markets is crucial.

Dataset Information

The adaptive market hypothesis and high frequency trading.

Publications

The adaptive market hypothesis and high frequency trading.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets