The 16th annual Quant Summit USA returns to New York on July 9-12 with an agenda highlighting the biggest trends in the industry and showcasing the latest research in these areas.

The quant playing field has been gradually changing over the years, and now we can see where many quants have shifted their focus. One of the big trends is the proliferation of machine learning in finance and its application in trading, asset allocation, stock selection, portfolio optimization, risk management and compliance - to name just a few areas.

Key themes this year include:

• Portfolio optimization techniques
• Machine learning in financial markets
• Quantitative buy-side research
• Quantum computing applications in finance
• Quant tools in portfolio construction
• Innovation in modelling
• And many more!

Speaker

Suresh Baral

Managing Director,

Protiviti

Presentation Title: "How to Develop Machine Learning Capabilities, While Avoiding Pitfalls"

The rapid innovations in machine learning technology, availability of large volumes / variety of data, increased computing power and the marketing publicity have created a gold rush to apply machine learning in the financial industry. While machine learning provides opportunities for increased predictive power and production efficiency, it is not without pitfalls. Many organizations are realizing that the results of their proof-of-concept projects are not measuring up to the expectations. This presentation discusses how to develop machine learning capabilities and processes to benefit from the recent innovations while avoiding these pitfalls.

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