This episode discusses the latest advancements and challenges in risk modelling, portfolio management, and investment strategies in asset management. Experts explore the evolution of risk models, including the use of alternative data and non-linearity to enhance risk assessment. They delve into the shrinking alpha, the importance of considering uncertainty in the mean, and the role of volatility in risk management. They also examine the complexities of states of the world, portfolio management, and the incorporation of alternative data into factor models.
Takeaways
• Risk modelling has evolved with alternative data, non-linearity, and structural models to improve accuracy.
• Alpha is shrinking due to factor investing, leading asset owners to pay for benchmark replication.
• Identifying states of the world for risk modelling is challenging due to the difficulty in predicting crises.
• Risk models are used in portfolio management to tell a story and support decision-making.
• Alternative data faces limitations when incorporated into factor models due to varying applicability across industries.
• Total Portfolio Approach emphasizes the interconnectedness of asset classes and the need for holistic portfolio management.
• The dynamic financial landscape requires active communication and collaboration among investment teams to manage factor exposures.
• AI algorithms can lead to radicalization and confirmation bias, reinforcing existing views and decreasing exposure to diverse information.
• Stories provide a framework for understanding complex data and making informed decisions in finance and risk management.
