A study on whether quantitative trading strategies can continuously outperform humans using different strategies

Abstract

Gone are the days when traders on Wall Street chaotically shouted bids and offers across trading floors, giving way to advanced trading systems that execute trades almost instantly as exponential technological growth has reshaped the financial industry. This study conducts a comprehensive comparative analysis of modern quantitative, rule-based autonomous investment strategies against more traditional, fund manager-led strategies within ETFs across three distinct markets: equities, fixed-income, and foreign exchange. Quantitative strategies utilise algorithms to make data-driven decisions, minimising human biases and emotions. In contrast, discretionary strategies rely on fund managers’ proprietary research, market insights, and intuition from experience. The two strategies were tested over a five-year period encompassing both stable and volatile markets arising from the unpredictability of the COVID-19 pandemic, ensuring a robust analysis of varied market conditions. While discretionary ETFs did, at times, achieve higher returns, they did so with higher volatility. This indicates that the prospect of higher returns is significantly influenced by the fund manager's skill, making consistent performance less predictable compared to algorithmic approaches. Quantitative strategies consistently outperformed their discretionary counterparts in a multifaceted series of tests focusing on risk-adjusted returns. The findings support the growing popularity of quantitative strategies in an increasingly automated environment, offering insights to investors and investment managers alike. Challenges to more traditional financial concepts also emerge, suggesting that markets might not be as efficient as previously believed, contributing to ongoing academic debates. Despite being limited by a five-year time frame since the legislative changes took place, this study lays a foundation for future research as the longer-term effects become observable. To conclude, the balance between algorithmic efficiency and human insight will shape the future of fund management, and the level of optimal oversight over the models remains to be determined. This work illustrates the benefits and potential provided by quantitative strategies and suggests a need for a fresh perspective on traditional investment approaches, hinting at a well-developed hybrid of the two. The era of traders shouting orders across trading floors may be over, but the drive to secure the best investment strategies has only grown more intense, propelled by technology’s continuous evolution.