Much has been reported and said recently about CFA Institute’s inclusion of cryptocurrency and blockchain into the CFA curriculum.
“We saw the field advancing more quickly than other fields and we also saw it as more durable,” said Stephen Horan, managing director for general education and curriculum at CFA Institute in Charlottesville, Virginia. “This is not a passing fad.”
It’s positive that organizations like CFA are drawing attention to the space, said Darius Sit, a former foreign-exchange and bond trader at BNP Paribas SA who’s now managing partner of cryptocurrency trading firm QCP Capital Pte in Singapore. “More education is always good.”
But how exactly is this being implemented into the CFA curriculum? We break it all down and also interviewed Barbara Pettit, Head of Curriculum and Learning Experience at CFA Institute, as well as Derek Burkett, CFA, FRM, CAIA, VP Advanced Designations at Kaplan Schweser to find out.
Barbara Pettit, CFA Institute: The material on blockchain and cryptocurrency will appear in a new CFA Level I reading called “Fintech in Investment Management” alongside other fintech subjects, including selected investment management applications such as robo-advisers, big data as well as the use of artificial intelligence in the analysis of big data. This new reading will ensure that candidates become familiar with the core concepts and, importantly, understand how these technologies affect the investment management industry and their work as industry professionals. Coverage will expand when we detect further inclusion of these technologies into core buyside workflows.
More data science and fintech topics may eventually be added to the curriculum depending on [CFA Institute’s] ongoing practice analysis.
Barbara Pettit: Yes, at this time, the most significant additions of fintech topics are in both Level I and Level II, but we plan to incorporate more investment management applications throughout the curriculum in the future.
Barbara Pettit: To ensure that current CFA Level II candidates have the necessary knowledge to understand the new material on machine learning that has been added to the Level II, this year (and this year only), we are including the new “Fintech in Investment Management” reading at both Level I and Level II.
Derek Burkett, Kaplan Schweser: For CFA candidates, the approach to learning the new fintech material (or any other new topic for that matter) is no different than the approach to learning well established topics such as quantitative methods, or economics. In all cases, candidates must learn the curriculum in the context of the learning outcome statements, practice what they’ve learned extensively, and assess their understanding using rigorous mock exams.
Candidates should also keep in mind that fintech is a relatively small part of the curriculum. So, while it is important to understand the material they should keep in mind that other topics will be tested more heavily and should allocate their time accordingly. That’s one of the core challenges of preparing for the CFA exam. It’s an endurance race that requires a respected training partner to help candidates spend their time efficiently.
At CFA Level I, there will be one new reading: Fintech in Investment Management. This is of one out of six readings in Portfolio Management, which boils down to approximately 1% of the CFA Level I exam.
The LOS details are as follows:
FINTECH IN INVESTMENT MANAGEMENT
The candidate should be able to
- describe “fintech;”
- describe Big Data, artificial intelligence, and machine learning;
- describe fintech applications to investment management;
- describe financial applications of distributed ledger technology
At CFA Level II, two readings are introduced in Quantitative Methods:
- The same reading as in Level I, Fintech in Investment Management, is included, but only for this year (to bring the 2019 Level II candidates up to speed).
- The second reading being introduced is Multiple Regression and Machine Learning.
Both readings translate to roughly 2-4% of the CFA Level II exam. However, Multiple Regression and Machine Learning is a lot more in-depth than Fintech in Investment Management. The LOS for Multiple Regression and Machine Learning are as follows:
MULTIPLE REGRESSION AND MACHINE LEARNING
The candidate should be able to:
- formulate a multiple regression equation to describe the relation between a dependent variable and several independent variables and determine the statistical significance of each independent variable;
- interpret estimated regression coefficients and their p-values;
- formulate a null and an alternative hypothesis about the population value of a regression coefficient, calculate the value of the test statistic, and determine whether to reject the null hypothesis at a given level of significance;
- interpret the results of hypothesis tests of regression coefficients;
- calculate and interpret 1) a confidence interval for the population value of a regression coefficient and 2) a predicted value for the dependent variable, given an estimated regression model and assumed values for the independent variables;
- explain the assumptions of a multiple regression model;
- calculate and interpret the F-statistic, and describe how it is used in regression analysis;
- distinguish between and interpret the R2 and adjusted R2 in multiple regression;
- evaluate how well a regression model explains the dependent variable by analyzing the output of the regression equation and an ANOVA table;
- formulate a multiple regression equation by using dummy variables to represent qualitative factors and interpret the coefficients and regression results;
- explain the types of heteroskedasticity and how heteroskedasticity and serial correlation affect statistical inference;
- describe multicollinearity and explain its causes and effects in regression analysis;
- describe how model misspecification affects the results of a regression analysis and describe how to avoid common forms of misspecification;
- describe models with qualitative dependent variables;
- evaluate and interpret a multiple regression model and its results
- distinguish between supervised and unsupervised machine learning;
- describe machine learning algorithms used in prediction, classification, clustering, and dimension reduction;
- describe the steps in model training.