Algorithmic trading is rapidly changing the landscape of stock markets, with high-frequency trading (HFT) at its forefront. Regulators and investors alike are increasingly acknowledging the influence of HFT and algorithmic approaches in modern finance. HFT, a subset of algorithmic trading, involves the rapid buying and selling of large volumes of securities through automated systems. This method is continually evolving and is poised to become the dominant form of algorithmic trading in the future.
This shift towards algorithmic trading is revolutionizing trading practices. Stock traders are leveraging algorithms to enhance both speed and efficiency in securities trading. As technology advances, these algorithms are becoming more sophisticated, integrating artificial intelligence (AI) to adapt to diverse and complex trading patterns. The progression towards pragmatic machine learning (ML) within algorithmic trading is enabling real-time analysis of vast datasets from varied sources.
Machine learning, a branch of computer science, utilizes statistical models, algorithms, computational complexity, AI principles, control theory, and other interdisciplinary approaches. Its core strength lies in developing computationally and informationally efficient algorithms that can derive robust predictive models from extensive datasets. This makes ML an ideal tool for addressing challenges in HFT, both in trade execution and in generating alpha—the measure of an investment’s performance relative to a market index. The synergy between algorithmic trading and ML is giving rise to what we can term AI trading.
AI-Driven ETFs and Stock Selection: Redefining Portfolio Management
Exchange-Traded Funds (ETFs) have significantly reshaped portfolio investment. The majority of ETFs are index funds, characterized by low expense ratios due to their passive management style. Index funds simplify operations by mirroring market indices, eliminating the need for active security selection, a process largely automatable by computers.
A prime example of AI’s impact on ETFs is the AI-powered equity ETF, AIEQ. Developed by ETF Managers Group and leveraging IBM Watson’s AI capabilities, AIEQ represents an actively managed portfolio driven by artificial intelligence. Notably, AIEQ has consistently demonstrated outperformance compared to the S&P 500, highlighting the potential of AI in investment management.
Beyond ETFs, AI is also transforming stock picking through AI Advisors, potentially replacing human analysts in actively managed equity funds. BlackRock, a leading global investment management firm, has initiated a transition towards fully automated investment programs powered by self-learning AI algorithms, reducing reliance on human stock-pickers.
BlackRock CEO Laurence Fink suggests that the underperformance of actively managed equity funds and subsequent capital outflows are linked to the limitations of human discretion in portfolio management and stock selection. He argues that the widespread availability of information has made traditional active management more challenging, necessitating a greater reliance on big data, AI, and quantitative models within investment strategies. BlackRock executive Mark Wiseman reinforces this view, stating that the traditional approach of individual stock picking based on perceived superior insight is becoming obsolete.
These trends raise concerns about the potential displacement of human financial advisors by robo-advisors, leading to job losses. However, it is crucial to acknowledge that the performance data of AI-managed portfolios is still relatively limited, and the long-term market volatility implications of AI trading remain under academic scrutiny.
“Even though it is less costly and more efficient in some cases to employ AI investment advisors, personal contact and human discretion will be imperative at certain stages of investing.”
– Suchismita Mishra
While AI offers cost-effectiveness and efficiency in certain aspects of investment advising, the human element of personal interaction and discretionary judgment remains vital, particularly at critical investment junctures. A hybrid model, integrating both AI and human expertise, may represent a more sustainable path forward for the finance industry. Consequently, higher education is likely to evolve, incorporating FinTech and data science applications to prepare professionals for a future where humans and AI systems collaborate in finance.
