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Quant Finance

AI & machine learning applications in FX markets, exotic derivatives, and ESG insights

Posted by on 16 June 2021
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Boosting style with sentiment in fixed income and FX markets

Can country-specific macro news sentiment boost the performance of traditional style factors in the cross-section of developed markets sovereign bonds and currencies?

This session with Inna Grinis, Senior Data Scientist, RavenPack will address this question using real-time sentiment analytics from RavenPack, examining the findings that sentiment-based tilts of benchmark portfolios significantly boost risk-adjusted returns across carry, value, momentum, and defensive styles.

Learning exotic derivatives without calibration

Marco Bianchetti, Head of Fair Value Policy, Financial & Market Risk Management, Intesa Sanpaolo, and Pietro Rossi, Analyst, Prometeia & both Adjunct Professors at the University of Bologna, explore:

  • Where we are
  • From model parameters to market data
  • Configuring and training the ANNs
  • Results and stability analysis
  • Future work

Natural language processing ESG insights for quantitative investment

This session with Sylvain Forte, CEO & Founder, SESAMm, will be dedicated to showing how natural language processing can help construct ESG datasets based on web data to generate time series for quantitative use cases:

  • Extracting relevant information from data sources – tagging companies and ESG risks using Natural Language Processing
  • Assessing the relevance of time series and comparing them with ESG controversies
  • Analysis of extreme use cases: Wirecard
  • Building volatility forecasts and quantitative strategies using machine learning, combining market data with ESG alternative data

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