Data Assets & Alpha Group: An underperformance in European equities episode artwork

EPISODE · Jun 25, 2024 · 14 MIN

Data Assets & Alpha Group: An underperformance in European equities

from Making Sense · host J.P. Morgan

In this episode the group explore the risk / reward for European equities following a sharp period of underperformance vs US equities in recent weeks.  They discuss recent macro and political dynamics across Europe, including upcoming elections in France and the UK, the extent to which investors have been selling European equities, and equity pockets where risk / reward is looking most compelling.  Federico Manicardi, Head of International Market Intelligence, and Jigar Vakharia, from the Positioning Intelligence team, are in discussion with Eloise Goulder, Head of the Data Assets & Alpha Group at JP Morgan. To learn more about the Data Assets & Alpha Group: https://www.jpmorgan.com/markets/market-data-intelligence This episode was recorded on June 21, 2024. The views expressed in this podcast may not necessarily reflect the views of J.P. Morgan Chase & Co and its affiliates (together “J.P. Morgan”), they are not the product of J.P. Morgan’s Research Department and do not constitute a recommendation, advice, or an offer or a solicitation to buy or sell any security or financial instrument.  This podcast is intended for institutional and professional investors only and is not intended for retail investor use, it is provided for information purposes only. Referenced products and services in this podcast may not be suitable for you and may not be available in all jurisdictions.  J.P. Morgan may make markets and trade as principal in securities and other asset classes and financial products that may have been discussed.  For additional disclaimers and regulatory disclosures, please visit: www.jpmorgan.com/disclosures/salesandtradingdisclaimer. For the avoidance of doubt, opinions expressed by any external speakers are the personal views of those speakers and do not represent the views of J.P. Morgan.

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