The Impact of Algorithmic Trading on Market Efficiency: An Analysis of High-Frequency Trading Strategies

Abstract

Algorithmic trading, particularly high-frequency trading (HFT) strategies, has become one of the most significant financial innovations in recent decades, greatly impacting market liquidity, volatility, and efficiency. These trades, driven by complex algorithms and rapid data processing, have increased the speed of market response to new information, contributing to improved market performance. However, HFT also presents challenges and risks that may affect market stability. This study aims to examine the impact of algorithmic and high-frequency trading on liquidity, volatility, and market efficiency, and to analyze the benefits and challenges associated with these trading strategies. This study follows a descriptive review method, utilizing an analysis of previous studies on HFT across various financial markets. Data were sourced from reputable scientific databases to comprehensively assess the effects of algorithmic trading on market performance. Results show that HFT improves liquidity and reduces volatility in high-volume markets. However, in emerging markets with lower liquidity, HFT sometimes leads to increased volatility and market instability. Additionally, these trading strategies can cause sudden market fluctuations and erode public trust in financial markets. While HFT generally has positive effects on market efficiency, its associated challenges and risks necessitate strict oversight and appropriate regulatory measures to prevent severe volatility and sudden market instability.