Crowding risks and early unwind signals

In this episode of The Blunt Dollar, Ignacio sits down with portfolio strategist Vikram Josyula to explore a different way of thinking about macro investing. Instead of trying to predict the business cycle, Vikram focuses on detecting regime shifts early and adapting portfolios before risks compound.

Drawing from his experience across engineering, risk management, and quantitative research, he explains why many macro frameworks fail and how machine learning can improve decision-making without replacing human judgment. 

💡 Why predicting the business cycle often fails, and why adaptive frameworks work better in markets that evolve gradually but break suddenly

📊 How machine learning and rolling macro indicators can help identify regime shifts earlier than traditional economic models

🧠 The behavioral traps investors fall into when interpreting factor data and how narratives often distort what the numbers are actually saying

⚠️ The hidden risks behind “defensive” strategies, low volatility trades, and crowded factors that can unravel when everyone rushes for the exit

🤖 The real role of AI in investing today and why it should be viewed as jet fuel for analysts rather than a replacement for human judgment

If you like macro investing, systematic strategies, and deep conversations about how markets actually work, this one's for you.

And if you haven’t already, make sure to subscribe to The Blunt Dollar for more raw and honest finance conversations.

Oh, and if you haven't already... subscribe to The Blunt Dollar for more raw and honest finance conversations.

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Enjoy the episode!

Disclaimer: This podcast is for informational and educational purposes only. It does not constitute financial, investment, or legal advice. Listeners should consult a qualified financial professional before making any financial decisions.