ML master class
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This document outlines an agenda for a master class on AI and machine learning for financial professionals presented by Sri Krishnamurthy. The speaker bio introduces Sri as an experienced financial analyst and consultant who has taught at several universities. The agenda includes an overview of key trends in AI and machine learning, a machine learning primer, and case studies. The document provides background on QuantUniversity and concludes by thanking attendees.
ML master class
- 1. A Master Class in AI and Machine Learning for Financial Professionals 2019 Copyright QuantUniversity LLC. Presented By: Sri Krishnamurthy, CFA, CAP [email protected] www.analyticscertificate.com 08/06/2019 Online Master Class
- 2. 2 Speaker bio • Advisory and Consultancy for Financial Analytics • Prior Experience at MathWorks, Citigroup and Endeca and 25+ financial services and energy customers. • Columnist for the Wilmott Magazine • Author of forthcoming book “Financial Modeling: A case study approach” published by Wiley • Teaches Analytics in the Babson College MBA program and at Northeastern University, Boston • Reviewer: Journal of Asset Management Sri Krishnamurthy Founder and CEO QuantUniversity
- 3. 3 About www.QuantUniversity.com • Boston-based Data Science, Quant Finance and Machine Learning training and consulting advisory • Trained more than 1000 students in Quantitative methods, Data Science and Big Data Technologies using MATLAB, Python and R • Building a platform for AI and Machine Learning Enablement in the Enterprise
- 4. • Key trends in AI and machine learning • Machine Learning in 20 minutes • Case studies Agenda
- 5. AI and Machine Learning in Finance
- 6. 6 The 4th Industrial revolution is Here! Source: Christoph Roser at AllAboutLean.com As per Wikipedia*, “The 4th Industrial Revolution ….. marked by emerging technology breakthroughs in a number of fields, including robotics, artificial intelligence, nanotechnology, quantum computing, biotechnology, the Internet of Things, the Industrial Internet of Things (IIoT), decentralized consensus, fifth-generation wireless technologies (5G), additive manufacturing/3D printing and fully autonomous vehicles.” * https://en.wikipedia.org/wiki/Fourth_Industrial_Revolution
- 7. 7 Scientists are disrupting the way we live! Source: https://www.ladn.eu/tech-a-suivre/mobilite-2030-vehicules-volants-open-data/
- 8. 8 Interest in Machine learning continues to grow https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf
- 9. 9 MACHINE LEARNING AND AI IS REVOLUTIONIZING FINANCE
- 10. 10 Market impact at the speed of light! 10
- 11. 11 Machine Learning & AI in finance: A paradigm shift 11 Stochastic Models Factor Models Optimization Risk Factors P/Q Quants Derivative pricing Trading Strategies Simulations Distribution fitting Quant Real-time analytics Predictive analytics Machine Learning RPA NLP Deep Learning Computer Vision Graph Analytics Chatbots Sentiment Analysis Alternative Data Data Scientist
- 12. 12 CFA Institute has adopted Fintech and AI content in its curriculum Ref: https://www.cfainstitute.org/-/media/documents/support/programs/cfa/cfa-program-level-iii-fintech-in-investment-management.ashx
- 13. 13 The Virtuous Circle of Machine Learning and AI 13 Smart Algorithms Hardware Data
- 14. 14 The rise of Big Data and Data Science 14 Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg
- 15. 15 Smart Algorithms 15 Distributing Computing Frameworks Deep Learning Frameworks 1. Our labeled datasets were thousands of times too small. 2. Our computers were millions of times too slow. 3. We initialized the weights in a stupid way. 4. We used the wrong type of non-linearity. - Geoff Hinton “Capital One was able to determine fraudulent credit card applications in 100 milliseconds”* * http://go.databricks.com/hubfs/pdfs/Databricks-for-FinTech-170306.pdf
- 16. 16 Hardware Speed up calculations with 1000s of processors Scale computations with infinite compute power
- 18. 18 • Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead1 • Artificial intelligence is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals1 Definitions: Machine Learning and AI 18 1. https://en.wikipedia.org/wiki/Machine_learning 2. Figure Source: http://www.fsb.org/wp-content/uploads/P011117.pdf
- 19. Machine Learning Workflow Data Scraping/ Ingestion Data Exploration Data Cleansing and Processing Feature Engineering Model Evaluation & Tuning Model Selection Model Deployment/ Inference Supervised Unsupervised Modeling Data Engineer, Dev Ops Engineer Data Scientist/QuantsSoftware/Web Engineer • AutoML • Model Validation • Interpretability Robotic Process Automation (RPA) (Microservices, Pipelines ) • SW: Web/ Rest API • HW: GPU, Cloud • Monitoring • Regression • KNN • Decision Trees • Naive Bayes • Neural Networks • Ensembles • Clustering • PCA • Autoencoder • RMS • MAPS • MAE • Confusion Matrix • Precision/Recall • ROC • Hyper-parameter tuning • Parameter Grids Risk Management/ Compliance(All stages) Analysts& DecisionMakers
- 20. 20 1. Data 2. Goals 3. Machine learning algorithms 4. Process 5. Performance evaluation Key steps involved
- 22. 22 Dataset, variable and Observations Dataset: A rectangular array with Rows as observations and columns as variables Variable: A characteristic of members of a population ( Age, State etc.) Observation: List of Variable values for a member of the population
- 23. 23 Variables A variable could be: ▫ Categorical – Yes/No flags – AAA,BB ratings for bonds ▫ Numerical – 35 mpg – $170K salary
- 24. 24 Longitudinal ▫ Observations are dependent ▫ Temporal-continuity is required Cross-sectional ▫ Observations are independent Datasets
- 27. 27 • Descriptive Statistics ▫ Goal is to describe the data at hand ▫ Backward-looking ▫ Statistical techniques employed here • Predictive Analytics ▫ Goal is to use historical data to build a model for prediction ▫ Forward-looking ▫ Machine learning & AI techniques employed here Goal 27
- 28. 28 • Given a dataset, build a model that captures the similarities in different observations and assigns them to different buckets- Clustering • Given a set of variables, predict the value of another variable in a given data set- Prediction ▫ Predict salaries given work experience, education etc. ▫ Predict whether a loan would be approved given fico score, current loans, employment status etc. Predictive Analytics : Cross sectional datasets 28
- 29. 29 Goal Descriptive Statistics Cross sectional Numerical Categorical Numerical vs Categorical Categorical vs Categorical Numerical vs Numerical Time series Predictive Analytics Cross- sectional Segmentation Prediction Predict a number Predict a category Time-series Summary 29
- 31. 31 Machine Learning Unsupervised Supervised Reinforcement Semi-Supervised Machine Learning
- 32. 32 Goal Descriptive Statistics Cross sectional Numerical Categorical Numerical vs Categorical Categorical vs Categorical Numerical vs Numerical Time series Predictive Analytics Cross- sectional Segmentation Prediction Predict a number Predict a category Time-series Machine Learning Algorithms 32
- 33. 33 Supervised Algorithms ▫ Given a set of variables 𝑥", predict the value of another variable 𝑦 in a given data set such that ▫ If y is numeric => Prediction ▫ If y is categorical => Classification ▫ Example: Given that a customer’s Debt-to-Income ratio increased 20%, what are the chances he/she would default in 3 months? Machine Learning 33 x1,x2,x3… Model F(X) y
- 34. 34 Unsupervised Algorithms ▫ Given a dataset with variables 𝑥", build a model that captures the similarities in different observations and assigns them to different buckets => Clustering ▫ Example: Given a list of emerging market stocks, can we segment them into three buckets? Machine Learning 34 Obs1, Obs2,Obs3 etc. Model Obs1- Class 1 Obs2- Class 2 Obs3- Class 1
- 35. 35 • Parametric models ▫ Assume some functional form ▫ Fit coefficients • Examples : Linear Regression, Neural Networks Supervised Learning models - Prediction 35 𝑌 = 𝛽' + 𝛽) 𝑋) Linear Regression Model Neural network Model
- 36. 36 • Non-Parametric models ▫ No functional form assumed • Examples : K-nearest neighbors, Decision Trees Supervised Learning models 36 K-nearest neighbor Model Decision tree Model
- 38. 38 Machine Learning movers and shakers Deep Learning Automatic Machine Learning Ensemble Learning Natural Language Processing
- 42. 42 • What transformations do I need for the x and y variables ? • Which are the best features to use? ▫ Dimension Reduction – PCA ▫ Best subset selection – Forward selection – Backward elimination – Stepwise regression Feature Engineering 42
- 45. 45 Evaluating Machine learning algorithms Supervised - Prediction R-square RMS MAE MAPE Supervised- Classification Confusion Matrix ROC Curves Evaluation framework 45
- 46. 46 • Fit measures in classical regression modeling: • Adjusted 𝑅, has been adjusted for the number of predictors. It increases only when the improve of model is more than one would expect to see by chance (p is the total number of explanatory variables) 𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅, = 1 − ⁄∑"8) 9 (𝑦" − ;𝑦"), (𝑛 − 𝑝 − 1) ∑"8) 9 𝑦" − ?𝑦" , /(𝑛 − 1) • MAE or MAD (mean absolute error/deviation) gives the magnitude of the average absolute error 𝑀𝐴𝐸 = ∑"8) 9 𝑒" 𝑛 Prediction Accuracy Measures
- 47. 47 ▫ MAPE (mean absolute percentage error) gives a percentage score of how predictions deviate on average 𝑀𝐴𝑃𝐸 = ∑"8) 9 𝑒"/𝑦" 𝑛 ×100% • RMSE (root-mean-squared error) is computed on the training and validation data 𝑅𝑀𝑆𝐸 = 1/𝑛 H "8) 9 𝑒" , Prediction Accuracy Measures
- 48. 48 1. Data 2. Goals 3. Machine learning algorithms 4. Process 5. Performance Evaluation Recap
- 49. Machine Learning Workflow Data Scraping/ Ingestion Data Exploration Data Cleansing and Processing Feature Engineering Model Evaluation & Tuning Model Selection Model Deployment/ Inference Supervised Unsupervised Modeling Data Engineer, Dev Ops Engineer Data Scientist/QuantsSoftware/Web Engineer • AutoML • Model Validation • Interpretability Robotic Process Automation (RPA) (Microservices, Pipelines ) • SW: Web/ Rest API • HW: GPU, Cloud • Monitoring • Regression • KNN • Decision Trees • Naive Bayes • Neural Networks • Ensembles • Clustering • PCA • Autoencoder • RMS • MAPS • MAE • Confusion Matrix • Precision/Recall • ROC • Hyper-parameter tuning • Parameter Grids Risk Management/ Compliance(All stages) Analysts& DecisionMakers
- 50. #Disrupt19 Credit Risk Decision Making Using Lending Club Data
- 51. 51 1. Case Intro 2. Data Exploration of the Credit risk data set 3. Problem Definition and Machine learning 4. Performance Evaluation 5. Deployment Case study
- 52. 52 Credit risk in consumer credit Credit-scoring models and techniques assess the risk in lending to customers. Typical decisions: • Grant credit/not to new applicants • Increasing/Decreasing spending limits • Increasing/Decreasing lending rates • What new products can be given to existing applicants ?
- 53. 53 Credit assessment in consumer credit History: • Gut feel • Social network • Communities and influence Traditional: • Scoring mechanisms through credit bureaus • Bank assessments through business rules Newer approaches: • Peer-to-Peer lending • Prosper Market place
- 54. 54 The Data 54 https://www.kaggle.com/wendykan/lending-club-loan-data
- 55. 55 Credit Risk pipeline Data Ingestion from Lending Club Pre-Processing Feature Engineering Model Development and Tuning Model Deployment Stage 1 Stage 2 Stage 3 Stage 4 Stage 5
- 56. 56 56
- 57. 57 4-week online course in AI & ML in Finance 10/4/2019 to 10/25/2019– Livestream 1-day class in AI &ML in Finance August 12th 2019 –New York & Livestream Where do you go from here https://cfa-sf.org/events/EventDetails.aspx?id=1258670&group= https://www.cfany.org/event/machine-learning-and-ai-for-financial-professionals/
- 58. 58 1. Whitepapers at www.quantuniversity.com 2. https://blogs.cfainstitute.org/investor/tag/machine-learning/ 3. https://techcrunch.com/ 4. https://www.technologyreview.com/ 5. https://www.bbc.com/timelines/zypd97h 6. https://www.bbc.com/timelines/zq376fr Additional Reading
- 60. Thank you! Sri Krishnamurthy, CFA, CAP Founder and CEO QuantUniversity LLC. srikrishnamurthy www.QuantUniversity.com Contact Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be distributed or used in any other publication without the prior written consent of QuantUniversity LLC. 60