Q4-2022 FDP Charter Exam Registration is Closed Early Registration for Q2-2023 FDP Exam Opens Dec 7, 2022 |
Our webinars will boost your expertise in the latest trends in the industry and share insight into the FDP charter. Have a recommendation for an industry related topic, an author we should invite, an article you want to learn more about? Contact us: info@fdpinstitute.org. |
Candidate Orientation Q4-2022 Exam Date: September 21 ,2022 How to prepare for your exam day! This webinar features members of the FDP Curriculum Team as well as Candidate Relations. The following topics are discussed: Curriculum materials, exam format, and available resources. For each topic we discuss the required readings, keywords, learning objectives and sample questions. | Reimagining the Science of Prediction Date: September 14, 2022 Today's investors face daunting prediction challenges, and it is more critical than ever to blend intuition with data-driven rigor. Dr. Hossein Kazemi of the FDP Institute will host a discussion with coauthors of the new book “Prediction Revisited”, David Turkington and Megan Czasonis. Focus of the discussion will be the building blocks of data-driven prediction from a fresh perspective whereby data represents “experiences” rather than variables, revealing the link between linear regression and information theory and how the concept of relevance leads to more intuitive and effective predictions. |
Understanding the Landscape of Quantitative Investing Date: August 30, 2022 Join Albourne Partners, AIMA, and FDP Institute for a discussion on quantitative investing. What types of managers and strategies use quantitative techniques such as artificial intelligence, machine learning, regression, and backtesting? What do investors need to understand before making allocations to these strategies? What due diligence considerations are specifically helpful when evaluating quantitative managers? AIMA is a valued Association Partner of the FDP Institute. Employees of AIMA member companies are eligible for a 20% discount on first-time FDP exam registration fees. Click here to learn more about the AIMA Member Discount. | Natural Language Processing (NLP) for Financial Services (US/EMEA) Date: August 16, 2022 (PM Session) Language is one of the great untapped resources of information. Today’s NLP field covers the full cycle of recognizing and understanding speech, processing natural language and generating text including automatic coding. NLP is everywhere – when you dictate a text message to Siri, ask Alexa for the weather, search on Google, use email services that filter out spam, check out spelling and grammar, and even autocomplete an entire message. | Natural Language Processing (NLP) for Financial Services (US/EMEA) Date: August 16, 2022 (AM Session) Language is one of the great untapped resources of information. Today’s NLP field covers the full cycle of recognizing and understanding speech, processing natural language and generating text including automatic coding. NLP is everywhere – when you dictate a text message to Siri, ask Alexa for the weather, search on Google, use email services that filter out spam, check out spelling and grammar, and even autocomplete an entire message. |
Expanded Conversation on Algorithmic Trading and Machine Learning for Cryptoassets Date: July 21, 2022 During this session we took a more in depth view with additional examples and questions answered. Due to the transparent nature of public blockchains, substantial data is available to traders. Join us to discuss algorithmic trading powered by machine learning, as well as delving into some interesting and unexpected findings. Algorithmic trading using non-deterministic methods will also be discussed. | Algorithmic Trading and Machine Learning for Cryptoassets Date: July 14, 2022 Due to the transparent nature of public blockchains, substantial data is available to traders. Join us to discuss algorithmic trading powered by machine learning, as well as delving into some interesting and unexpected findings. Algorithmic trading using non-deterministic methods will also be discussed. | FDP Exam Prep: Kaplan Schweser Course Date: July 11, 2022 Master the Financial Data Professional® curriculum and take the risk out of exam day with Kaplan Schweser’s FDP Review Package. This package features an OnDemand review course, which is delivered 100% online and led by Dr. Greg Filbeck. |
FDP Q4-2022 Charter Information Session Date: July 6, 2022 Learn more about the curriculum and the roadmap to prepare for the upcoming FDP exam. The Financial Data Professional Institute (FDPI) has designed a self-study program to provide financial professionals with an efficient path to learn the essential aspects of financial data science. The Financial Data Professional (FDP) is a global designation for investment professionals with data science skills. | FDP Q4-2022 In-Depth Review of Prometric Test Center and Remote Proctor Testing Date: June 22, 2022 Map out your learning journey to obtain the FDP Charter. FDP managing Director Keith Black shares the value add of the FDP Charter, the updated curriculum, hiring trends, and how you can leverage your FDP learnings as you advance your career. The FDP team answered questions about the FDP program, where to obtain your curriculum, and our testing options. | Deep Reinforcement Learning for Asset Allocation in US Equities Date: June 15, 2022 This paper demonstrates the application of reinforcement learning to create a financial model-free solution to the asset allocation problem, learning to solve the problem using time series and deep neural networks. We demonstrate this on daily data for the top 24 stocks in the US equities universe with daily rebalancing. We use a deep reinforcement model on US stocks using different deep learning architectures. We use Long Short Term Memory networks, Convolutional Neural Networks, and Recurrent Neural Networks and compare them with more traditional portfolio management approaches like mean-variance, minimum variance, risk parity, and equally weighted. The Deep Reinforcement Learning approach shows better results than traditional approaches using a simple reward function and only being given the time series of stocks. In Finance, no training to test error generalization results come guaranteed. We can say that the modeling framework can deal with time series prediction and asset allocation, including transaction costs. |
FDP Q4-2022 Charter Information Session Date: June 6, 2022 Learn more about the curriculum and the roadmap to prepare for the upcoming FDP exam. The Financial Data Professional Institute (FDPI) has designed a self-study program to provide financial professionals with an efficient path to learn the essential aspects of financial data science. The Financial Data Professional (FDP) is a global designation for investment professionals with data science skills. | How to Make Data Resonate for Your Business Date: May 26, 2022 Join us as Olus Kayacan discusses how efficient data analysis provides solid predictive power for Investment Management. Data can also be cumbersome and generate chaos if not handled properly. Learn how to use data to maximize the impact on your business! | Master Class: AI & Machine Learning for Investment Professionals Date: April 27, 2022 Innovations in technology have revolutionized financial services to an extent that large financial institutions are behaving like technology companies! It is no secret that technological innovations like Data Science and AI are fundamentally changing how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals about how they should retool and gear themselves towards the upcoming revolution. |
FDP Q2-2022 Info Session Date: January 12, 2022 Learn more about the curriculum and the roadmap to prepare for the upcoming FDP exam. The Financial Data Professional Institute (FDPI) has designed a self-study program to provide financial professionals with an efficient path to learn the essential aspects of financial data science. The Financial Data Professional (FDP) is a global designation for investment professionals with data science skills. slide deck | FDP Q2-2022 Learn more about your test options: At a Prometric Test Center or via Remote Proctor Testing Date: January 27, 2022 Map out your learning journey to obtain the FDP Charter. FDP managing Director Keith Black shares the value add of the FDP Charter, the updated curriculum, hiring trends, and how you can leverage your FDP learnings as you advance your career. The FDP team answered questions about the FDP program, where to obtain your curriculum, and our testing options. | Candidate Orientation Q2-2022 Exam Date: March 2, 2022 How to prepare for your exam day! This webinar features members of the FDP Curriculum Team as well as Candidate Relations. The following topics are discussed: Curriculum materials, exam format, and available resources. For each topic we discussed the required readings, keywords, learning objectives and sample questions. |
Turn Text To Alpha part II
Date: February 10, 2022 Join Dan Joldzic, CEO of Alexandria Technology and Keith Black, PhD, CFA, CAIA, FDP for Part II as they discuss natural language processing (NLP) in investment management. NLP techniques can be used to analyze a variety of data sources, including company news, earnings calls, and social media posts. The output from these models can be used for stock selection with varying time horizons and degrees of alpha capture. In this webinar we will discuss generating alpha from company news and social media sentiment. |
Using Alternative Data in Financial Markets
Date: February 9, 2022 Quantitative investors have long used traditional data sources, such as income statements and balance sheets of public firms, to drive stock selection models. With the explosion in the amount and diversity of data in the last five years, alternative data sources are quickly revolutionizing quantitative investing. Alternative data sources can include natural language processing of news and social media content, review of credit card transactions and consumer emails, and geolocation data using cell phone signals and satellite images. |
Turn Text To Alpha part I
Date: December 9, 2021 Join Dan Joldzic, CFA, FRM, CEO & Quantitative Research, from Alexandria Technology for a discussion of natural language processing (NLP). #NLP techniques can be used to analyze a variety of data sources, including news reports, company regulatory filings and earnings calls, as well as posts on social media sites such as Reddit. The output from these models can be used for stock selection with varying time horizons and degrees of alpha capture. This data can also be used to analyze sentiment regarding the ESG footprint of both public and private companies. |
Practical Machine Learning in Asset Management: Manager selection and valuing the secondary sale of private assets
Date: November 2021 Join Lydia Ofori, CFA, CAIA, Adam Duncan, and Keith Black, PhD, CFA, CAIA, FDP as they discuss how machines are interfacing with humans to effect manager selection and private asset secondary sales. | Introducing a New Hedge Fund Dataset Date: May 6, 2021 Description: How to use a hedge fund database, based on open-sourced hedge fund return and how to gain access to the data and a slew of interesting examples. How do you efficiently clean and manage semi-structured data? Alex and Linus will demonstrate with examples based on factor analysis, correlations, and dispersions and explain why they probably should have picked a different programming language. Panelists: Linus Nilsson, Founder NilssonHedge; Alex Lostado, Quant Analyst, NilssonHedge; and Brandon Kremer CFA, CAIA, CIPM, FDP, Portfolio Manager Heximer Investment Management Slidedeck + Research Project | Alternative Data and Collective Intelligence Investing: Risks of Adoption Date: March 8, 2021 Description: Over the last five years, the volume of information available to all has skyrocketed, leading to increased investor interest in alternative data or crowdsourced investment strategies. While opportunities for added value may be present, adoption also comes with a number of risks, both for early and late adopters. Join this conversation as readings from the 2021 Financial Data Professional (FDP) curriculum are brought to life. |
Integrating Disruptive Innovation Into an Organization's DNA Date: September 9, 2020 Description: CAIA CEO Bill Kelly and Cathie Wood, Founder and CEO of ARK Investment Management, discus
“Innovation is the Key to Growth” – ARK Investment Panelists: Cathie Woods, CEO & Found Ark Investment and William J. Kelly, CEO, CAIA Association | Eagle Alpha Discusses Alternative Data for Investment Strategies Date: September 2, 2020 Description: Ronan Crosson (data strategy) and Thomas Combes (data science) of Eagle Alpha will review what is Alt Data and share market trends and adoption. They will discuss ROI on alt data and show use cases for the most popular datasets for both discretionary funds and quants funds. While highlighting the importance of getting the processes right they will touch upon the end-to-end process: Discovery, Prioritization, Evaluation, Procurement & Integration. The session will also include a deep dive on quality testing for alternative datasets. Panelists: Ronan Crosson, Director, Data Strategy & Analytics, Eagle Alpha; Thomas Combes, Head of Data Science, Eagle Alpha and Keith Black, Ph.D, CAIA, CFA, FDP, Managing Director, Content Development, CAIA Association. | Making Sense of Machine Learning Date: August 19, 2020 Description: Machine learning (ML) enables powerful algorithms to analyze financial data in new and exciting ways. But this excitement is often tempered by fear that investors don’t really understand why a model behaves the way it does. We need to move beyond this “black box” stigma. We propose a framework that demystifies the predictions from any ML algorithm. Our approach computes what we call a “fingerprint” for a given model’s linear, nonlinear, and interaction effects that drive its predictions — and ultimately its investment performance. In a real-world case study applied to currency return predictions, we find that popular ML models like neural network and random forest think in ways that do indeed make sense, and which we can begin to understand. These fingerprints empower investors to describe and probe the similarities and differences across ML models, and to extract genuine insight from machine-learned rules. Panelists: David Turkington, Senior Managing Director and Head of Portfolio and Risk Research, State Street Associates; Yimou Li, Assistant Vice President and Machine Learning Researcher, State Street Associates; and Aaron Filbeck, CFA, CAIA, CIPM, FDP, Director, Global Content Development, CAIA Association. |
Data Science in Production Date: August 6, 2020 Description: Nigel Noyes will reference architectures for using Data Science in production. Panelists: Nigel Noyes, Director, Automation, Data Science at Quicken Loans and Mehrzad Mahdavi, Executive Director, FDP Institute. | Challenges of Algorithmic Fairness in Financial Services Date: July 29, 2020 As financial services sector increasingly adopts advanced algorithms, including machine learning and AI, there has been greater regulatory and public scrutiny on the potential for algorithms to replicate unfair bias and discrimination against disadvantaged groups, exacerbating existing inequalities. This webinar will walk through use cases in mortgage lending and in peer-to-peer lending to discuss the complex challenges of trying to make the algorithms "fair" in evaluating credit risk of minority groups. | Factor and Asset Allocation: The Role of Networks and LASSO Date: July 16, 2020 Description: Dr. Gueorgui Konstantinov spoke about the financial Industry needs for new Methods to model and explain the complex market behavior. The industry is in a state of transition. The Asset Management Industry must avoid over-fitting, and adjust findings for false discoveries. Using machine learning and predictive models, investors can find asset and factor allocation solutions. Implementing LASSO regressions help to derive asset and factor allocation. Network theory and predictive models help to derive portfolio allocation in a new way, which successfully captures economic relationships. However, it is inevitable to evaluate the new strategies in a manner consistent with the requirements in the brave new financial world. Adjusting the Sharpe Ratios and t-Statistics become inevitable in the new set up. The focus in the webinar is in the application of new tools in Investment Management. |
Machine Learning Models in Credit Risk Analysis Date: April 29, 2020 Description: Mr. Arifi demonstrated ensemble models in DataRobot and showed why FDP is important. That is, knowing the models and model parameters are more important than knowing how to program in python. | Data Supply Chain Management Date: April 7, 2020 Description: Data Supply Chain Management is the collection, organization, flow and streamlining of data – including any pre-processing and normalization steps - to make it usable, guided by domain knowledge, for the next downstream process. Typically, this next step involves analysis via traditional statistical or contemporary machine learning tools. The end goal of the exercise is to generate insights that can imply customer value, inform revenue or pricing metrics, optimize costs and help gain a competitive advantage in the marketplace. | Quantitative ESG Investing Date: April 1, 2020 Description: ESG investing is an area of active interest for both the investment and academic communities. Despite the intense interest, there currently is no agreed upon definition of ESG investing, or how to best build investment portfolios that incorporate both return and sustainability dimensions. (Both are important for sustainability-minded investors.) In this article, the authors categorize the broad types of ESG investing currently in the market and introduce an ESG investment framework. This results in a portfolio that optimally combines the dual objectives of alpha and sustainability outperformance. TOPICS: Portfolio theory, portfolio construction, ESG investing |
Machine Learning Prediction of Recessions: An Imbalanced Classification Approach Date: July 8, 2020 Panelists: Al Yazdani, Chief Data Scientists & Founder, Calcolo Analytics, LLC and Mehrzad Mahdavi, Executive Director, FDP Institute. | The Art and Science of Big Data in Quantitative Investing Date: May 21, 2020 Panelists: Elene Khoziaeva, CFA, Head of US Equity, Bridgeway Capital Management and Mehrzad Mahdavi, Executive Director, FDP Institute. Recording + Transcript | Computational Drug Discovery Date: May 6, 2020 Description: The role of AI/ML and High-Performance Computing in Drug Discovery Panelist: Woody Sherman, CSO, Silicon Therapeutics & Adjunct Professor, UMass |
Usage of Alternative Data and ML in Asset Classes Date: March 25th, 2020 Panelists: Michael Oliver Weinberg, Head of Hedge Funds and Alternative Alpha, APG; Peter Strikwerda, Global Head of Digital Innovation, APG; Aaron Filbeck, CFA, CAIA, CIPM, FDP, Director, Global Content Development, CAIA Association. Slide Deck | Long Term Machine Learning Predictions for US Equity Date: March 5, 2020 Description: Tony Guida co-wrote and edited the book "Big Data and Machine Learning in Quantitative Investment", one of the required readings for the upcoming exam. This webinar is titled "Long Term Machine Learning Predictions for US equity" and is a user case study of Chapter 7 of the book which Tony co-wrote with Guillaume Coqueret. | A Machine Learning Approach to Risk Factors: A Case Study Using the Fama-French-Carhart Model Date: February 27, 2020 Panelists: Joe Simonian, Ph.D., CIO Autonomous Investment Technologies, LLC, Mehrzad Mahdavi, Ph.D., Executive Director, FDP Institute |
Analyzing Text to Detect Risk Date: February 19, 2020 Description: NLP is a fast-growing area of data science for the finance industry. Recent advances in financial technology (FinTech) have dramatically transformed the financial landscape with respect to the way we access, invest, and transfer financial capital. In this article, the authors explore a promising avenue for the use of natural-language processing in an effective yet non-invasive method by which to monitor the health and integrity of financial institutions and corporations in general by analyzing corporate emails and news. | Big Data is a Big Deal Date: January 29, 2020 Description: Gene Getman is a Client Portfolio Manager for the 1798 Alternatives group, focused on Investor Relations for the Hedge Fund business. He is the Product Specialist for the 1798 Q Strategy and other innovative or capacity constrained alternative investment strategies based in New York. More importantly: Gene is the co-author of "Big Data is a Big Deal: An investor’s guide to the applications and challenges of alternative data" | Data Ethics in Machine Learning and Finance Date: January 22, 2020 Description: Our conversation with Guen Dondé, head of Research at the Institute of Business Ethics. Co-researcher of "IBE Ethics at Work" and "Implications of AI on Business Ethics". Thought provoking, engaging, and great learning overall! |
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