Name: 5th Annual Machine Learning in Quantitative Finance
Start: 2023-09-20 00:00:00 UTC
End: 2023-09-22 00:00:00 UTC

5th annual: Machine Learning in Quantitative Finance

Assess best practice and learn how to mitigate challenges in various machine learning techniques to generate maximum alpha and ensure substantial ROI
20 - 22 September 2023: New York

The conference will with the help of speakers from T Rowe Price, Deutsche Bank, Charles Schwab, Morgan Stanley, Alliance Bernstein and Scotiabank to name some, provide key insights into how to cut through the noise and extract investable signals from NLP and alternative data.

The speakers will also provide case studies of best use cases for machine learning in quantitative finance and how to systematically extract value and reduce cost through enhancing productivity.

For more information, please contact Ayis Panayi, Digital Marketing & PR Executive at ayisp@global-fmi.com

Key Themes & Topics

This GFMI conference will provide key insights into how to cut through the noise and extract investable signals from NLP and alternative data. The conference will look at case studies of best use cases for machine learning in quantitative finance and how to systematically extract value and reduce cost through enhancing productivity.

Thought leaders will consider the various risks and limitations of machine learning and alternative data and analyze how to overcome investor and executive reluctance through improving explainability. This conference will also look at how to augment human quantitative strategies with machine learning techniques to stay ahead of competition and enhance and speed up decision-making.

KEY SESSIONS:

Apply NLP to extract insights from financial text sources

ChatGPT: All noise or a useful tool?

Assess how alternative data can advance your quantitative strategies

Redefining Research: Leverage alternative data to produce data-driven insights

ChatGPT and Generative AI: Some costs, risks, and gains considerations

Blend traditional quant strategies with machine learning for best effect

5th Annual Machine Learning in Quantitative Finance

Location

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