AI Reshaping Long-Term Investment Strategies

AI: Reshaping Long-Term Investment Strategies for the Future

Imagine a world where you can ‘paint’ your next masterpiece simply by describing it, where protein structures are predicted with unprecedented accuracy, aiding drug discovery and our understanding of diseases! This is not science fiction—it's the reality that Artificial Intelligence (AI) is creating today. With AI models like Midjourney and AlphaFold leading the way, we are witnessing this transformation firsthand.

This revolution is poised to transform the investment landscape as well. With Goldman Sachs predicting a $200 billion global investment in AI startups by 2025 according to Goldman Sachs, it's not a question of whether AI will impact long-term investment strategies, but rather how significantly it will transform them.

Understanding AI in the Context of Investing

AI is a multidisciplinary nature. Drawing from fields like computer science, neuroscience, and psychology, AI empowers machines to learn from data, enabling them to recognise patterns, make decisions, understand language, and even interpret visual information. This breadth of capabilities positions AI to revolutionise how we approach investing. The NVIDIA 2024 State of AI in Financial Services report indicates this trend, with 56% of companies investing in AI for investment management, including portfolio optimisation and algorithmic trading.

While valuable, traditional investment strategies, which often rely on human expertise and historical data analysis, can be limited by cognitive biases, the sheer volume of information to process, and a slower response to rapidly changing market conditions. In contrast, AI models offer a significant advantage by analysing vast datasets in real-time, providing unbiased insights, and continuously adapting to new market dynamics.

Sasfin Asset Managers' multi-strategy approach, combining fundamental research with a focus on sustainable returns, necessitates a deep understanding of investment drivers. This commitment to clarity underpins our exploration of AI, despite the unique challenges its integration presents. While advanced AI models like GPT-4 and Gemini have shown unprecedented capabilities in financial data analysis, many still operate as "black boxes." Their opacity hinders widespread adoption in investment management, where accountability and clear reasoning are paramount.

To address this challenge, our research prioritises Explainable AI (XAI). This field aims to make AI models more transparent and understandable, effectively illuminating the 'black box'. into a transparent and understandable tool. For example, our causal-factor model for stock selection identifies and quantifies the causal relationships between various factors and stock returns. By understanding these causal links, portfolio managers gain valuable insights, allowing them to make more informed and data-driven investment decisions.

While XAI enhances transparency and understanding, the combination of AI strengths and human expertise is a game-changer for long-term investment strategies. This balanced approach combines the unparalleled analytical capabilities of AI with the nuanced judgment and experience of seasoned professionals, resulting in more comprehensive and robust investment strategies. According to an April 2024 survey by Alex Forbes, 29% of asset managers using AI reported that human experts make the final investment decisions based on AI-generated insights. Additionally, 21% said that humans review and validate AI-driven insights. This suggests that AI serves as a tool for enhancing human decision-making rather than replacing it, further emphasising the collaborative model between technology and human expertise in the investment process.

While we are still in the early stages of our AI journey, we recognise that the successful application of AI to investment strategies depends on careful implementation, continuous refinement, and an approach that leverages the strengths of both artificial and human intelligence.