EP2: Scott Treloar: Skiing in Singapore and how to quantify investor skill

The Curious Quant

This podcast episode explores trends and challenges in asset management, including the need for technology investment, the role of machine learning and data, the valuation of human capital, and the identification of alpha in trading strategies. It also delves into the impact of AI and machine learning on market efficiency and the broader societal implications of AI adoption in asset management. Takeaways • Current asset management industry requires more technology investment. • Probabilistic programming and Bayesian Updating Framework can be used to identify human skill. • Data integration and management are key for asset managers in the digital age. • Finance industry faces challenges in adopting machine learning and data enabled investment processes due to structural inefficiencies, information costs, complexity, and risk aversion. • Hedge funds have become more regulated, making it harder for smaller managers to exist, but machine learning and AI can improve operational efficiency, lower costs, and introduce new business models. • Machine learning can be used to identify patterns and characteristics of successful investors, but it's important to consider uncertainty and dynamics. • Human capital can be valued in investing based on skill, signals, and timing. • Machine learning and AI may lead to more mispricing in markets due to focus on short-term movements and statistical patterns rather than fundamental value. • Societal challenges of AI adoption include limitations in replicating human intelligence, the need for interpretability and oversight, and the distinction between principles and patterns in decision-making.

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