| REGISTRATION IS OPEN! Missed the FDP Exam Info Session? click here |
Our webinars will boost your expertise in the latest trends in the industry and share insight into the FDP charter. |
We examine the problem of predicting recessions from a machine learning perspective. We employ a number of machine learning algorithms to predict the likelihood of recession in a given month using historical data from a set of macroeconomic time series predictors. We argue that, due to the low frequency of historical recessions, this problem is better dealt with an imbalanced classification approach. We apply measures to compensate for class imbalance and use various performance metrics to evaluate and compare models. With these measures in place, ensemble machine learning models predict recessions with high accuracy and great reliability. In particular, a Random Forest model achieves a near perfect True Positive Rate within the historical training sample, generalizes extremely well to a test period containing 2008-2009 financial crisis, and shows elevated recession probabilities during the last few months of 2019, associated with the tightened macroeconomic environment and worsened by global pandemic.
|
Dr. Gueorgui Konstantinov will be speaking 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.
|
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. In preparation for the webinar, please read:
Optional introductory reading for non-technical audience:
|
Webinar Recordings & Slide Decks |
FDP Info Session: Learn more about the upcoming FDP Exam. Join Mehrzad Mahdavi, Executive Director FDPI and Hossein Kazemi, Sr. Curriculum Advisor FDPI as they discuss: WHY the program was created, the industry trends, WHO the program is for, and most importantly: HOW you can obtain your FDP credential.We will discuss the curriculum, required readings, learning objectives and offer some sample questions. |
A Conversation with Imir Arifi, Ph.D. Title: 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.
|
A Conversation with ... Rick Roche, CAIA Date: April 22 Description: Discover the latest trends in quantitative model development. AI and Machine Learning in Investment Management is an insider’s look into the opaque world of quantitative investing and the emergence of Artificial Intelligence, Big and small Data.
|
A conversation with ... Ganesh Mani Date: April 7 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.
|
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 |
A Conversation with ... Michael Oliver Weinberg and Peter Strikwerda Date: March 25th, 2020 Description: |
A Conversation with ... Tony Guida 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. Note: Guida's book "Big Data and Machine Learning in Quantitative Investment" is a required reading for the October/November 2020 FDP Exam
|
A Conversation with ... Joe Simonian Date: February 27, 2020 Description: Joe Simonian is the founder and CIO at Autonomous Investment Technologies, LLC, as well as the co-editor of The Journal of Financial Data Science. Note: During this webinar Simonian discusses "A Machine Learning Approach to Risk Factors: A Case Study Using the Fama–French–Carhart Model" a required reading for the October/November 2020 FDP Exam |
A Conversation with ... Seoyoung Kim Date: February 19, 2020 Description: Dr. Seoyoung Kim is a Professor of Business Analytics at Santa Clara University and co-author of "Zero-Revelation RegTech: Detecting Risk through Linguistic Analysis of Corporate Emails and Newsl" Natural language processing 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.
*********** |
A Conversation with ... Gene Getman 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" Note: The "Big Data" paper is a required reading for the March 2020 exam.
|
A Conversation With ... Guen Dondé
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! Note: the three readings discussed are part of the required readings for the October/ November 2020 exam. Panelists: Guen Dondé, Kathryn Wilkens, Sr. Advisor FDP Institute, Mehrzad Mahdavi, Executive Director, FDP Institute,
|
FDPI Candidate Orientation for the March 2020 Exam Description: 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. Panelists: Mehrzad Mahdavi, Executive Director, FDP Institute, Hossein Kazemi, Sr. Advisor FDP Institute, Keith Black, Managing Director Content Strategy CAIA. | Learn more about the FDP designation. Description: The Financial Data Professional Institute (FDPI) was established by CAIA Association to address the growing need in finance for a workforce that has the skills to perform in a digitized world where an increasing number of decisions will be data and analytics-driven. Panelists: William “Bill” Kelly – CEO, CAIA Association; Mehrzad Mahdavi, Executive Director, FDP Institute; Hossein Kazemi, Sr. Advisor, CAIA & FDPI; Keith Black - Managing Director Curriculum at CAIA and recent FDP charter holder; Mirjam Dekker, Project Manager, FDPI | Learn more about the FDP designation Description: The Financial Data Professional Institute (FDPI) was established by CAIA Association to address the growing need in finance for a workforce that has the skills to perform in a digitized world where an increasing number of decisions will be data and analytics-driven. Panelists: William “Bill” Kelly – CEO, CAIA Association; Mehrzad Mahdavi, Executive Director, FDP Institute; Hossein Kazemi, Sr. Advisor, CAIA & FDPI; Mirjam Dekker, Project Manager, FDPI |
Brought to you by:
| 2020 Financial Data Professional Institute© Contact Us: info@FDPinstitute.org Privacy | Policies | Terms of Use | FAQ |
