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.
During this session, WorldQuant’s David Rukshin will be talking with Bank of America’s Dr. Rajesh Krishnamachari about designing and scaling an efficient data strategy and platform, the opportunity for advanced technology such as AI and machine learning, and how these factors may continue to evolve in the future.
· 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
· Novel data sources and clever data science techniques may dominate headlines, but data deployment is the key to putting these advantages into action.
· We cover the components that go into modern data deployment infrastructures: people, processes, technology.
· We present case studies from Quandl and its buy-side clients
· 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 fundamental analysis through systematic strategies
· Layering geo-spatial information into security selection process
· Finding value at the intersection of macro and micro signals
· 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?
· Outlining the process of making impactful decisions
· Using attention-tracking hardware and intuition-capturing techniques to quantify and model the financial decision-making process
· Assessing the dependency of decisions and their corresponding previous sequential activity
· 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