Big Data: A Door Open for Financial Innovations ?

1. Big Data: A Door Open for Financial Innovations ? Steve Wilcockson Industry Manager – Financial Services

2. Financial Services: Big Data, Machine Learning and Model Integration.

3. Your Datasets: How large ? “Garbage in, garbage out – data quality is key” – tier one investment bank “We encounter more challenges with simulated data, than with real data.” – wealth manager.

4. Machine Learning: Data-Driven

5. Advantages & Pitfalls: Machine Learning Investment Manager “I use Bayesian estimation, Markov Chain Monte Carlo, dynamic Bayesian networks, Hidden Markov Modelling and various classification algorithms: svms [support vector machines] and decision trees” Portfolio Advisor “I use a range of machine learning classification algorithms to aggregate useful index, stock and economic information from which I build my portfolio strategies.” Investment Banker “I developed and traded my own intra-day, trend-following G10 FX strategies, which used a unique combination of traditional machine learning algorithms (Neural and Bayesian Networks) with a Genetic Algorithm optimization wrapper” US Prop Trading Shop “We are going to use machine learning tools to analyze predictability in publically available daily stock returns.” Hedge Fund “I would like to hear your experience on the use of state space models in stat arb. I do believe they offer a superior way to model the equilibrium dynamically allowing it to evolve through time. The tricky part is how to deal with the risk of over fitting.” Prop Trading Firm “I risk-managed a guy who was terrible for over-fitting. His models were optimised to within an inch of his life and did not work out of sample. They were too oriented to the noise..” Systematic Fund Manager “No matter what cool algorithms we threw at the testbench and then live, simple linear modelling worked surprisingly well; we could understand the model, apply judgment over risk factors and model parameters. Far more satisfying” Fund Manager “I started to use state space models to get a framework for testing parameter stability to avoid over fitting.”

6. Flexible Research; Effective Implementation Production: Take Algorithms to Data Application/ Data Servers Research: Bring Data to Algorithms

7. Modelling and Model Implementation: Now Development and Model Testing Historical Data Model Testing Modeling / Analysis Research / Algorithms End of Day / Intraday Model Validation Model Development Files Back-Testing Calibration Databases Production Production Data Client Decision Engine Models Real-Time Feeds Web Approved Data Spreadsheets Rules

8. Modelling and Model Implementation: Emerging Development and testing Model Testing Model Validation Managed/ Consistent Data Modeling / Analysis Back-Testing Research / Algorithms End of Day / Intraday Model Development Files Calibration Databases Real-Time Derived User Contributed Text / Social Client Unit Spreadsheets Software Test Testing Coverage Web Decision Engine Models On-Demand Reporting/Vis Rules Production

9. Example 1: Map/Reduce in Research (Linear Regression / Machine Learning)

10. Example 2: Fraud Detection

11. Challenges & Opportunities: “Algorithms Everywhere” • Cultural Clash/Marriage of Multiple Teams and Infrastructures • Data • Quants • IT • “The Business” • Data-Driven Modelling and Equation-Based Modelling. • Dark Art / Cool Science; • Complexity / Simplification • How will the Education Community Respond ? • Rise of Data Science • Multidisciplinary Collaboration • Project-based Learning

12. www.mathworks.co.uk/financeskills