With the advancement in Machine Learning, it is like being a kid in a candy store. But the wrappers are seductive and contain many surprises, making it a challenging landscape for managers to navigate. In this presentation, we hear from an industry veteran who brought AI to Wall Street, and is an advisor to to the major consulting companies and FinTech players, and a pioneer of Machine Learning in Finance.
· Implementation of a universal data model for finance-oriented text analytics
· Balancing sparsity and precision using flexible, hierarchy based aggregation
· Measuring perceived performance in the present and expectations for the future
· Impact of Covid-19 on the utility and consumption of alternative data
· Data sourcing and evaluation of new datasets
· Fundamental vs quant and systematic methods approaches to alt data
· Machine learning to generate actionable financial insights
· Neural networks, deep learning, supervised and unsupervised machine learning techniques
· Performing text analytics for NLP, sentiment analysis and topic modelling
· Model governance and SR 11-17
· Importance of tech and processes to put data-driven advantages into production through data deployment
· Components that go into modern data deployment
· Buy-side case studies of people, processes and technology to successfully stand up and scale up pursuit of data-driven edge
· Identifying when to use in-house AI services vs outsourced options
· Determining how to get better insights, reduce costs and improve security and transparency
· On-boarding and warehousing of data from alt data providers
· Improving predictive accuracy by employing multiple models in your security selection process
· Choosing the right tool for the right job; how to apply ML in a way that supplements linear models
· Applying the principals of comparative advantage to identify task best handled by humans vs machines
· Panel of leading PM’s on the impact of data science to returning alpha
· Improving predictive accuracy by employing multiple models in your security selection process
· Using ESG metrics to capture the return and risk drivers that traditional financial metrics can’t
· Advantages of an ESG quant approach
· How ESG consideration can enhance both a portfolio’s economic performance and ESG profile
· Using new data and combined datasets not just to find alpha
· As acquisition and analysis of alt data is now commonplace, how can we use it for the next big thing?
· Recapturing alpha – is alt data the only way?
· Web harvesting without compromising on data integrity and ethics
· Evaluating the overall impact on P&L without investing too much time and money to explore it
· Choosing, combining and validating data to reduce risk
· Remaining competitive
· Adapting and reacting to structural breaks
· Instigating the integration of new technology into a traditional structure
· Building internal capabilities through investment in people and technology
· Remaining competitive in a saturated market
· Linking datasets from different data sources for a clearer, more accurate outcome
· Process of data linkage as applied to financial market