Alex Antic: Data science across finance, academia and government

I chat with Alex on his experiences across a range of environments, the role of machine learning in Australia’s future and how to best deploy data science in academia and industry.

Nothing on this podcast is to be considered investment advice or a recommendation. No investment decision or activity should be undertaken without first seeking qualified and professional advice.

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How private credit investors are preparing for software’s AI reckoning

How private credit investors are preparing for software’s AI reckoning

As the private credit industry matures, the first real stress tests are emerging – from AI-exposed software loans to eroding covenants in megadeals. Institutional investors are responding by rethinking where the illiquidity premium justifies the risk.

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The financial system after Covid-19: Reinforcing the lower for longer regime or a catalyst for change

This session examined the structural trends in the financial sector that have been either amplified or altered by the COVID crisis.

Is China’s growing influence a threat or opportunity?

This workshop looked at how to navigate a worsening geopolitical situation and what it means for economic growth.

What economic transparency means to sovereign investments

A lack of transparency and sound data remain huge challenges in many emerging markets. The panel discussed the link between governance principles, asset prices, trust in government and investment volatility and what investors can do about it.

Finding growth in emerging markets

This session argued that investors should look to companies with strong secular growth to find alpha in emerging markets.

Technological innovation – the (investment) world reimagined

What technological tools are investors using in a remote environment including more innovative ways to do due diligence? What behaviours and processes have changed because of COVID and lockdown?

Investor case study: Using AI in investments

Wei Xie explained the OPTrust focus on two subdomains of machine learning - reinforcement learning and uncertainty modelling - and how they can be used together.

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