Mathematical Finance & Financial Data Science Seminar
Machine Learning for Quantitative Investment and Wealth Management: Opportunities and Challenges
Speaker: Cristian Homescu, Investment & Portfolio Analytics, Chief Investment Office, Global Wealth and Investment Management, Bank of America
Date: Tuesday, March 9, 2021, 5:30 p.m.
It is increasingly apparent that Financial Machine Learning is a rather specialized area instead of just a combination of standard Machine Learning and Financial Data.
This also brings a very practical question: “What is hype and reality when applying machine learning ML to quantitative investment and wealth management (QWIM)?”
This presentation delves into what appears to work well (and, respectively, not so well) when ML is used within the context of QWIM, while also discussing the practical challenges (including QWIM specific challenges). The QWIM applications are categorized into areas:
- classification and pattern recognition, such as:
- classification and partition of the investment universe
- investing based on alternative data
- text analysis of company and regulatory documents
- sentiment analysis of news and social media
- ESG (Environmental, Social and Governance) investing
- fund decomposition, inference and replication
- network analysis and clustering, such as:
- clustering-based portfolio optimization
- network-based portfolio optimization
- analysis of interconnectedness risk
- network effects on investment portfolios.
- time series forecasting, such as:
- forecasting of financial time series
- empirical asset pricing
- reinforcement learning, such as:
- pricing and hedging of financial derivatives
- optimal dynamic trading strategies
- portfolio allocation
- goals-based investing
Other practical ML applications in QWIM include:
- synthetic financial data generation
- testing investment strategies and portfolios
- factor-based investment strategies
- incorporating market states and regimes into investment portfolios.
The presentation also describes some of the the challenges, including
- lack of sufficient data
- need to satisfy privacy, fairness and regulatory requirements
- model overfitting
- explainability and interpretability
- hyperparameter tuning
Registration: This event is free, but requires registration. Please click here to register.