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QU Summer school 2020 speaker Series - Session 7 A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times! Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc. Managing Machine Learning Models in the Financial Industry
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Synthetic data in finance
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
Quant university MRM and machine learning
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Lecture 1: Model Risk Management for AI and Machine Learning
Artificial intelligence and machine learning are part of today’s modeler’s toolbox for building challenger models and new innovative models that address business needs. However, AI presents new and unique challenges for risk management, particularly for assessing, controlling, and managing model risk for models of limited transparency. Another key consideration is the speed at which these models can be developed, validated, and then deployed into productive use to be competitive adhering to a robust model risk management program. This talk will highlight best practices for integrating AI into model risk practices and showcase examples across the model lifecycle.
Synthetic data in finance
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
achine Learning and Model Risk
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https://qusummerschool.splashthat.com
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Machine Learning and Model Risk (With a focus on Neural Network Models)
All models are wrong and when they are wrong they create financial or non-financial risks. Understanding, testing and managing model failures are the key focus of model risk management particularly model validation.
For machine learning models, particular attention is made on how to manage model fairness, explainability, robustness and change control. In this presentation, I will focus the discussion on machine learning explainability and robustness. Explainability is critical to evaluate conceptual soundness of models particularly for the applications in highly regulated institutions such as banks. There are many explainability tools available and my focus in this talk is how to develop fundamentally interpretable models.
Neural networks (including Deep Learning), with proper architectural choice, can be made to be highly interpretable models. Since models in production will be subjected to dynamically changing environments, testing and choosing robust models against changes are critical, an aspect that has been neglected in AutoML.
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Lecture 1: Alexander Denev
In this talk, Alexander will introduce Alternative Data and discuss it's uses from his book, The Book of Alternative Data
- What is alternative data?
- Adoption of alternative data
- Information value chain
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- Processes required to develop signals
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Lecture 2: Saeed Amen
In this talk, Saeed will discuss use cases in Alternative Data
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- Using CLS flow data to trade FX
- Geospatial Insight satellite data to estimate retailers' EPS
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Machine learning for factor investing
Machine learning for factor investing - Tony Guida
https://quspeakerseries5.splashthat.com/
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In this presentation, Tony will first introduce the concept of supervised learning. Then he will cover the practitioner angle for constructing non linear multi factor signals using stock characteristics. He will show the added value of ML based signals over traditional linear stale factors blend in equity.
This master class is derived from Guillaume Coqueret and Tony Guida's latest book "Machine Learning for Factor Investing"
Synthetic data generation for machine learning
As machine learning becomes more pervasive in the industry, data scientists and quants are realizing the challenges and limitations of machine learning models. One of the primary reasons machine learning applications fail is due to the lack of rich, diverse and clean datasets needed to build models. Datasets may have missing values, may not incorporate enough samples for all use cases (for example: availability of fraudulent transaction records to train a model) and may not be easily sharable due to privacy concerns. While there are many data cleansing techniques to fix data-related issues and we can always try and get new and rich datasets, the cost is at times prohibitive and at times impractical leading many institutions to abandon machine learning and go back to rule-based methods.
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic datasets can be used for comprehensive scenario analysis, missing value filling and privacy protection of the datasets when building models. The advent of novel techniques like Deep Learning has rekindled interest in using techniques like GANs and Encoder-Decoder architectures in financial synthetic data generation.
In this workshop, we will discuss the state of the art in Synthetic data generation and will illustrate the various techniques and methods that can be used in practice. Through examples using QuSynthesize & QuSandbox, we will demonstrate how these techniques can be realized in practice.
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The use of Data Science and Machine learning in the investment industry is increasing, and investment professionals, both fundamental and quantitative, are taking notice. Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative data sets including text analytics, cloud computing, and algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more technologies penetrate enterprises, financial professionals are enthusiastic about the upcoming revolution and are looking for direction and education on data science and machine learning topics.
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Recommended
Synthetic data in finance
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
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QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Lecture 1: Model Risk Management for AI and Machine Learning
Artificial intelligence and machine learning are part of today’s modeler’s toolbox for building challenger models and new innovative models that address business needs. However, AI presents new and unique challenges for risk management, particularly for assessing, controlling, and managing model risk for models of limited transparency. Another key consideration is the speed at which these models can be developed, validated, and then deployed into productive use to be competitive adhering to a robust model risk management program. This talk will highlight best practices for integrating AI into model risk practices and showcase examples across the model lifecycle.
Synthetic data in finance
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
achine Learning and Model Risk
QU Speaker Series - Session 3
https://qusummerschool.splashthat.com
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Machine Learning and Model Risk (With a focus on Neural Network Models)
All models are wrong and when they are wrong they create financial or non-financial risks. Understanding, testing and managing model failures are the key focus of model risk management particularly model validation.
For machine learning models, particular attention is made on how to manage model fairness, explainability, robustness and change control. In this presentation, I will focus the discussion on machine learning explainability and robustness. Explainability is critical to evaluate conceptual soundness of models particularly for the applications in highly regulated institutions such as banks. There are many explainability tools available and my focus in this talk is how to develop fundamentally interpretable models.
Neural networks (including Deep Learning), with proper architectural choice, can be made to be highly interpretable models. Since models in production will be subjected to dynamically changing environments, testing and choosing robust models against changes are critical, an aspect that has been neglected in AutoML.
Frontiers in Alternative Data : Techniques and Use Cases
QuantUniversity Summer School 2020 (https://qusummerschool.splashthat.com/)
https://quspeakerseries10.splashthat.com/
Lecture 1: Alexander Denev
In this talk, Alexander will introduce Alternative Data and discuss it's uses from his book, The Book of Alternative Data
- What is alternative data?
- Adoption of alternative data
- Information value chain
- Risks associated with alternative data
- Processes required to develop signals
- Valuation of alternative data
Lecture 2: Saeed Amen
In this talk, Saeed will discuss use cases in Alternative Data
-Deciphering Federal Reserve communications
- Using CLS flow data to trade FX
- Geospatial Insight satellite data to estimate retailers' EPS
- Saving "alpha" with transaction cost analysis
- Using Bloomberg News data to trade FX
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Machine learning for factor investing - Tony Guida
https://quspeakerseries5.splashthat.com/
Topic: Machine Learning for Factor Investing: case study on "Trees"
In this presentation, Tony will first introduce the concept of supervised learning. Then he will cover the practitioner angle for constructing non linear multi factor signals using stock characteristics. He will show the added value of ML based signals over traditional linear stale factors blend in equity.
This master class is derived from Guillaume Coqueret and Tony Guida's latest book "Machine Learning for Factor Investing"
Synthetic data generation for machine learning
As machine learning becomes more pervasive in the industry, data scientists and quants are realizing the challenges and limitations of machine learning models. One of the primary reasons machine learning applications fail is due to the lack of rich, diverse and clean datasets needed to build models. Datasets may have missing values, may not incorporate enough samples for all use cases (for example: availability of fraudulent transaction records to train a model) and may not be easily sharable due to privacy concerns. While there are many data cleansing techniques to fix data-related issues and we can always try and get new and rich datasets, the cost is at times prohibitive and at times impractical leading many institutions to abandon machine learning and go back to rule-based methods.
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic datasets can be used for comprehensive scenario analysis, missing value filling and privacy protection of the datasets when building models. The advent of novel techniques like Deep Learning has rekindled interest in using techniques like GANs and Encoder-Decoder architectures in financial synthetic data generation.
In this workshop, we will discuss the state of the art in Synthetic data generation and will illustrate the various techniques and methods that can be used in practice. Through examples using QuSynthesize & QuSandbox, we will demonstrate how these techniques can be realized in practice.
Ml and AI for financial professionals
The use of Data Science and Machine learning in the investment industry is increasing, and investment professionals, both fundamental and quantitative, are taking notice. Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative data sets including text analytics, cloud computing, and algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more technologies penetrate enterprises, financial professionals are enthusiastic about the upcoming revolution and are looking for direction and education on data science and machine learning topics.
In this webinar, we aim to bring clarity to how AI and machine learning is revolutionizing financial services. We will introduce key concepts and through examples and case studies, we will illustrate the role of machine learning, data science techniques, and AI in the investment industry. At the end of this webinar, participants will see a concrete picture of how machine learning and AI techniques are fueling the Fintech wave!
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This overview session offers a tour of machine learning and AI methods, examining case studies to understand the technology companies, data vendors, banks, and fintech startups that are the key players in trading and investment management. Practical examples and case studies will help participants understand key machine learning methodologies, choose an algorithm for a specific goal, and recognize when to use machine learning and AI techniques
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The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making. In this course, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry. Rather than just showing how to write code or run experiments in Python, we will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics.
YOU WILL LEARN:
• Role of Machine Learning and AI in Financial services
• When do we use Machine learning and AI techniques?
• What are the key machine learning methodologies?
• How do you choose an algorithm for a specific goal?
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Modular Machine Learning for Model Validation
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Implementing model validation through a set of interdependent modules that utilizes both traditional econometrics and data science techniques can produce robust assessments of the predictive effectiveness of investment signals in an economically intuitive manner.
The proposed methodology, modular machine learning, also answers a number of practical questions that arise when applying block time series cross-validation such as what number of folds to use and what block size to use between folds.
It is possible to re-interpret the Fundamental Law of Active Management into a model validation framework by expressing its fundamental concepts, information coefficient and breadth, using the formal language of data science.
In this talk, we introduce an approach towards model validation which we call modular machine learning (MML) and use it to build a methodology that can be applied to the evaluation of investment signals within the conceptual scheme provided by the FL. Our framework is modular in two respects: (1) It is comprised of independent computational components, each using the output of another as its input, and (2) It is characterized by the distinct role played by traditional econometric and date science methodologies.
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1- https://www.h2o.ai/resources/white-paper/machine-learning-considerations-for-fairly-and-transparently-expanding-access-to-credit/
2- https://arxiv.org/abs/2011.03156
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Markowitz portfolio optimization is optimal in theory, however, when applied in practice it often fails catastrophically. Usually, this is addressed by various simplifications to increase robustness. In this talk I will make the case that the reason this theory fails in practice is because uncertainty in the parameter estimation is not taken into account. By using Bayesian statistics we can fix Markowitz and retain all its desirable properties while still having a robustness technique that can be easily extended. This talk is geared at intermediate and will give a general introduction to Bayesian modeling using PyMC3 and focus on application and code examples rather than theory.
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Bio
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Video: https://youtu.be/ky3159dqQ_o?t=30
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With innovations in hardware, algorithms, and large datasets, the use of Data Science and Machine Learning in finance is increasing. As more and more open-source technologies penetrate enterprises, quants and data scientists have a plethora of choices for building, testing, and scaling models. Alternative datasets including text analytics, cloud computing, algorithmic trading are game-changers for many firms exploring novel modeling methods to augment their traditional investment and decision workflows. While there is significant enthusiasm, model risk professionals and risk managers are concerned about the onslaught of new technologies, programming languages, and data sets that are entering the enterprise. With very little guidance from regulators on how to govern the tools and the processes, organizations are developing their own home-cooked methods to address model risk management challenges.
In this webinar, we aim to bring clarity to some of the model risk management challenges when adopting data science, AI, and Machine Learning methods in the enterprise. We will discuss key drivers of model risk in today’s environment and how the scope of model governance is changing. We will introduce key concepts and discuss key aspects to be considered when developing a model risk management framework when incorporating data science techniques and AI methodologies.
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Learn how artificial intelligence (AI) and machine learning are revolutionizing financial services — this course will introduce key concepts and illustrate the role of machine learning, data science techniques, and AI through examples and case studies from the investment industry. The presentation uses simple mathematics and basic statistics to provide an intuitive understanding of machine learning, as used by financial firms, to augment traditional investment decision making.
This overview session offers a tour of machine learning and AI methods, examining case studies to understand the technology companies, data vendors, banks, and fintech startups that are the key players in trading and investment management. Practical examples and case studies will help participants understand key machine learning methodologies, choose an algorithm for a specific goal, and recognize when to use machine learning and AI techniques
QuantUniversity Machine Learning in Finance Course
The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making. In this course, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry. Rather than just showing how to write code or run experiments in Python, we will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics.
YOU WILL LEARN:
• Role of Machine Learning and AI in Financial services
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• What are the key machine learning methodologies?
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Topic: Modular Machine Learning for Model Validation
Implementing model validation through a set of interdependent modules that utilizes both traditional econometrics and data science techniques can produce robust assessments of the predictive effectiveness of investment signals in an economically intuitive manner.
The proposed methodology, modular machine learning, also answers a number of practical questions that arise when applying block time series cross-validation such as what number of folds to use and what block size to use between folds.
It is possible to re-interpret the Fundamental Law of Active Management into a model validation framework by expressing its fundamental concepts, information coefficient and breadth, using the formal language of data science.
In this talk, we introduce an approach towards model validation which we call modular machine learning (MML) and use it to build a methodology that can be applied to the evaluation of investment signals within the conceptual scheme provided by the FL. Our framework is modular in two respects: (1) It is comprised of independent computational components, each using the output of another as its input, and (2) It is characterized by the distinct role played by traditional econometric and date science methodologies.
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With Raghu Kulkarni and Steve Dickerson
Recently, machine learning has been used extensively in credit decision making. As ML proliferates the industry, issues of considerations for fair and transparent access to credit decision making is becoming important.
In this talk, Dr.Raghu Kulkarni and Dr.Steven Dickerson from Discover Financial Services will share their experiences at Discover. The talk will include:
- An overview of how ML models are used across financial life cycle
- Practical problems practitioners run into and why explainability and bias detection becomes important.
References:
1- https://www.h2o.ai/resources/white-paper/machine-learning-considerations-for-fairly-and-transparently-expanding-access-to-credit/
2- https://arxiv.org/abs/2011.03156
Bayesian Portfolio Allocation
Markowitz portfolio optimization is optimal in theory, however, when applied in practice it often fails catastrophically. Usually, this is addressed by various simplifications to increase robustness. In this talk I will make the case that the reason this theory fails in practice is because uncertainty in the parameter estimation is not taken into account. By using Bayesian statistics we can fix Markowitz and retain all its desirable properties while still having a robustness technique that can be easily extended. This talk is geared at intermediate and will give a general introduction to Bayesian modeling using PyMC3 and focus on application and code examples rather than theory.
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As the financial industry continues to embrace AI and Machine Learning models, model risk management (MRM) departments are grappling with challenges on how to update model governance frameworks to adapt to the changing landscape of model management. While most MRM departments are structured and processes defined to address traditional statistical and quant models, data-driven models like Machine Learning models require modifications in the way models are defined, tested, validated, and governed.
In this webinar, we will discuss ten key aspects to factor when developing your model risk management framework when integrating Machine Learning models. We will discuss key drivers of model risk in today’s environment and how the scope of model governance is changing. We will introduce key concepts and discuss key aspects to be considered when developing a model governance framework when incorporating data science techniques and AI methodologies. Through this Decalogue, we aim to bring clarity on some of the model governance challenges when adopting data science, AI and machine learning methods in the enterprise.
Qwafafew meeting 5
In 2009 author and motivational speaker Simon Sinek delivered the now-classic TED talk “Start with why”. Viewed by over 28 million people, “Start with Why” is the third most popular TED video of all time and it teaches us that great leaders and companies inspire us to take action by focusing on the WHY over the “what” or the “how”. In this talk we’ll ask how applied data and computational scientists can use the power of WHY to frame problems, inspire others, and give them answers to business questions they might never think of asking.
Bio
Jessica Stauth is a Managing Director in Fidelity Labs, an internal startup incubator with a mission to create new fintech businesses that drive growth for the firm. Dr. Stauth previously held roles as Managing Director of Portfolio Management, Research, and Trading at Quantopian, a crowd-sourced systematic hedge fund based in Boston, Director of Quant Product Strategy for Thomson Reuters (now Refinitiv), and as a Senior Quant Researcher at the StarMine Corporation, where she built global stock selection models including the design and implementation of the StarMine Short Interest model. Dr. Stauth holds a PhD in Biophysics from UC Berkeley, where her research focused on computational neuroscience.
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In this master class, Stefan shows how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data. The goal is to yield a generative model capable of producing synthetic samples representative of this class. While most popular with image data, GANs have also been used to generate synthetic time-series data in the medical domain. Subsequent experiments with financial data explored whether GANs can produce alternative price trajectories useful for ML training or strategy backtests.
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As AI and ML penetrates the financial industry, there are growing concerns about ethical use of AI in Finance. In this talk, Dan will focus on how the AI can be operationalized to help industry professionals and executive teams alike think about opportunities, risks as well as required actions factoring in ethics in our data-driven world.
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Summary:
Topic 1: A Structural Approach for Use of Text Analytics in Credit Risk
Topic 2: Natural Language Processing in Finance
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- Doug Bryden | Partner; Head of the Operational Risk & Environment Group, Travers Smith LLP
- Mark Richardson | Partner; IT, Telecoms and Electronics, Keltie
- Sébastien A. Krier | Founder & AI Ethics/Policy Expert, Dataphysix Ltd
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AI to Improve Regulatory Compliance, Governance & Auditing. How AI identifies and prevents risks, above and beyond traditional methods. Techniques and analytics that protect customers and firms from cyber-attacks and fraud. Using AI to quickly and efficiently provide evidence for auditing requests.
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Rapid prototyping quant research ml models using the qu sandbox
- 1. QUANTUNIVERSITY SUMMER SCHOOL Rapid Prototyping Quant Research ML Models for Algorithmic Auditing using the QuSandbox 2020 Copyright QuantUniversity LLC. Presented By: Sri Krishnamurthy, CFA, CAP sri@quantuniversity.com www.quantuniversity.com 08/26/2019 QU SPEAKER SERIES 7 https://quspeakerseries7.splashthat.c om/
- 2. 2 Speaker bio • Quant, Data Science & ML practitioner • Prior Experience at MathWorks, Citigroup and Endeca and 25+ financial services and energy customers. • Columnist for the Wilmott Magazine • Teaches Data Science/AI at Northeastern University, Boston • Reviewer: Journal of Asset ManagementSri Krishnamurthy Founder and CEO QuantUniversity
- 3. 3 About QuantUniversity • Boston-based Data Science, Quant Finance and Machine Learning training and consulting advisory • Trained more than 1000 students in Quantitative methods, Data Science, ML and Big Data Technologies • Building a platform for operationalizing AI and Machine Learning in the Enterprise
- 4. 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
- 5. 5 Source: Sculley et al., 2015 "Hidden Technical Debt in Machine Learning Systems" Challenges
- 7. 7 MLFlow
- 8. 8 DVC
- 10. 10 • Understanding sentiments in Earnings call transcripts Goal
- 11. 11 • Interpreting emotions • Labeling data Options • APIs • Human Insight • Expert Knowledge • Build your own Challenges 93
- 12. 12 NLP pipeline Data Ingestion from Edgar Pre-Processing Invoking APIs to label data Compare APIs Build a new model for sentiment Analysis Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 • Amazon Comprehend API • Google API • Watson API • Azure API
- 13. QuSandbox The platform for Algorithmic Auditing of Data Science and AI workflows in the Enterprise 2019 Copyright QuantUniversity LLC.
- 14. 14 QuSandbox research suite Model Analytics Studio QuSandboxQuTrack QuResearchHub Prototype, Iterate and tune Standardize workflowsProductionize and share Track experiments
- 15. 15 The four components that need to be encapsulated for reproducible pipelines Code Data Environment Process
- 16. 16 QuSandbox
- 18. Sign up for Updates at www.qusandbox.com srikrishnamurthy www.QuantUniversity.com www.analyticscertificate.com 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. 18