Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 2036-2040· 0 citations· 21 references
Abstract
High-frequency trading (HFT) has changed the nature of contemporary financial markets by making it possible to execute trades much faster and provide liquidity 24/7. As much as HFT can enhance efficiency in the market and speed up the process of price discovery, it also presents volatility and systemic risks especially when the market is under stress. The paper investigates HFT effects on market microstructure and price discovery processes with transaction-based depth of the order book data of major U.S. equities. Empirical evidence shows that HFT is a major source of bid-ask spread reduction, where the breadth of the order book is heightened, and price efficiency is short-term, in normal conditions. Nevertheless, in stressed market conditions, HFT leads to increased volatility, liquidity, and short-term poor price discovery. Such results inform regulators and market participants about the two-fold impact of algorithm trading on the quality of the market. Keywords- High-frequency trading, market microstructure, price discovery, liquidity, volatility, algorithmic trading.
We examine how restricting high-frequency trading (HFT) affects stock liquidity in China’s A-share market. Using China’s 2024 Provisions on Program Trading in the Securities Market (Trial) as a quasi-natural experiment, we construct a stock-level high-frequency trading intensity index from tick-level order data and app...
Jun Wang, L. Ji, Shao Chen· International Journal of Fin...· 0 citations
It is concluded that investing in algorithmic trading platforms is highly viable, enhancing price discovery, market liquidity, and trading cost efficiency, far exceeding the 10% discount hurdle rate.
Suru Prem Sai, Pavani Mudem, T. Meghana· American Journal of AI Cyber...· 0 citations
Market timing models aim to anticipate short-term market movements according to a given source of information. Such information could be extracted from an analysis of history or a forecast of the future. In fact, the financial markets are driven mainly by the expectations of market investors and by exogenous sources....
S. Vitali, Miloš Kopa, R. Domínguez et al.· Annals of Operations Researc...· 0 citations
The aim of the study is to prove that dynamic portfolios can effectively reflect the temporal dynamics of current risks of a higher order, providing greater reliability and stability compared to traditional portfolios. The subject is the economic imbalance in portfolio models, which occurs when different participants h...
A. Mikhaylov, N. A. Yousif, Y. Sotskov et al.· Finance: Theory and Practice· 0 citations
Market mispricing is where the prices of a stock in the market appear not to correlate with its intrinsic or fundamental price. In traditional financial theory, and specifically the Efficient Market Hypothesis, deviations of this kind are supposed to be uncommon and short lived since rational investors are supposed to...
F. Kalha· Journal of economics, financ...· 0 citations
The results show that after machine learning filtering, the Sharpe ratio and win rate of most stocks are improved, and the improvement in Sharpe ratio is statistically significant, however, there is no stable positive correlation between AUC and strategy improvement, indicating that machine learning is more suitable as...
Yanyouyou Wang· Finance & Economics· 0 citations
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