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