Abstract:  

Quantitative (quant) trading leverages mathematical models, statistical analysis, and algorithmic execution to identify and exploit market opportunities. This paper reviews research on the foundations of quant trading, evaluating strategies, performance metrics, and the role of behavioural considerations in automated systems. While quant trading minimizes emotional bias, human oversight remains critical to manage risk and adapt to regime changes. 

Introduction:  

Quantitative trading has become a dominant force in modern financial markets, driven by advances in computing power and data availability. Strategies range from high-frequency trading to statistical arbitrage and factor investing. Research explores how algorithmic approaches improve market efficiency and liquidity, though they can also amplify systemic risk under extreme conditions.

Core Strategies:  

1. Statistical Arbitrage: Exploits price inefficiencies using mean reversion or co-integration models.  

2. Trend-Following Models: Utilizes momentum indicators to capture sustained price moves.  

3. Machine Learning Applications: Enhances signal detection and predictive accuracy in complex datasets.

Performance and Risk Management:  

Backtesting, stress testing, and portfolio optimization are essential to validate strategies. Studies show that while quant strategies can outperform in specific market regimes, overfitting and model decay remain primary challenges.

Psychological and Behavioural Considerations:  

Research highlights that while algorithms reduce the influence of trader emotion, human biases can still emerge in model design, data selection, and intervention during drawdowns.

Conclusion:  

Quantitative trading represents a powerful intersection of finance, statistics, and technology. Effective implementation requires robust models, disciplined risk management, and awareness of the human factors behind algorithmic decision-making.

References:  

• Avellaneda, M., & Lee, J. (2010). Statistical arbitrage in the U.S. equities market. Quantitative Finance, 10(7), 761-782.  

• Chan, E. P. (2013). Algorithmic Trading: Winning Strategies and Their Rationale. Wiley.  

• López de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.