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Trading Insights: Behavioral biases and their impact on investing decisions

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Manage episode 501693043 series 2601045
Content provided by J.P. Morgan. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by J.P. Morgan or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

This episode features Michelle Baddeley, Professor of Economics at Sydney’s University of Technology (UTS) and author of several books on behavioral economics and finance. She sits down with Eloise Goulder, head of the Data Assets & Alpha Group at J.P. Morgan, to discuss the impact of behavioral biases — such as loss aversion, anchoring and confirmation bias — on investing decisions, the meme stock phenomenon and consumer choices. They also explore how the collective impact of these biases is manifested across the economy and markets. How can these biases be avoided in trading? And will AI serve to mitigate or amplify them in the future?

This episode was recorded on July 31, 2025.

Shownotes:

Michelle Baddeley’s work on these topics:

For more information about Michelle and her work go to smarteconomics.org

The podcast's views do not necessarily reflect those of J.P. Morgan Chase & Co or its affiliates (together “J.P. Morgan) and are not from J.P. Morgan’s Research Department. They do not constitute recommendations or offers to buy or sell securities. Intended for institutional and professional investors, not retail use, it is for informational purposes only. Products and services mentioned may not suit all investors or be available in all jurisdictions. J.P. Morgan may make markets and trade in discussed securities and asset classes. Visit www.jpmorgan.com/disclosures/salesandtradingdisclaimer for more disclaimers and regulatory disclosures. External speakers' opinions are personal and not J.P. Morgan's views. © 2025 JPMorgan Chase & Company. All rights reserved.

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302 episodes

Artwork
iconShare
 
Manage episode 501693043 series 2601045
Content provided by J.P. Morgan. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by J.P. Morgan or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

This episode features Michelle Baddeley, Professor of Economics at Sydney’s University of Technology (UTS) and author of several books on behavioral economics and finance. She sits down with Eloise Goulder, head of the Data Assets & Alpha Group at J.P. Morgan, to discuss the impact of behavioral biases — such as loss aversion, anchoring and confirmation bias — on investing decisions, the meme stock phenomenon and consumer choices. They also explore how the collective impact of these biases is manifested across the economy and markets. How can these biases be avoided in trading? And will AI serve to mitigate or amplify them in the future?

This episode was recorded on July 31, 2025.

Shownotes:

Michelle Baddeley’s work on these topics:

For more information about Michelle and her work go to smarteconomics.org

The podcast's views do not necessarily reflect those of J.P. Morgan Chase & Co or its affiliates (together “J.P. Morgan) and are not from J.P. Morgan’s Research Department. They do not constitute recommendations or offers to buy or sell securities. Intended for institutional and professional investors, not retail use, it is for informational purposes only. Products and services mentioned may not suit all investors or be available in all jurisdictions. J.P. Morgan may make markets and trade in discussed securities and asset classes. Visit www.jpmorgan.com/disclosures/salesandtradingdisclaimer for more disclaimers and regulatory disclosures. External speakers' opinions are personal and not J.P. Morgan's views. © 2025 JPMorgan Chase & Company. All rights reserved.

  continue reading

302 episodes

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