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Event Description
12/21/2017 5:00 PM12/21/2017 7:00 PM

[​​​Member Organized Event] Meet your fellow CFAW DC members for casual, open conversation. Markets, hobbies, families, networking - feel free to join and share in conversation.​

1/16/2018 9:00 AM1/17/2018 5:00 PM

The amount of data available to organizations and individuals is unprecedented. Financial services sectors, including securities & investment services and banking, have the most digital data stored per firm on average. Finance companies that want to maximize use of this available data require professionals who have a keen understanding of data science and know how to use it to solve meaningful business challenges.

This two-day, hands-on course provides a structured teaching environment where attendees learn the Python programming language as a powerful tool to conduct robust data analyses on finance-related data sets. At the end of the workshop, course participants will have applied the Python programming language and essential data analysis techniques to practical programming exercises to gain experience solving challenging finance-related problems.

Specific area in finance where data science skills acquired from this course can be effectively applied include: sentiment analysis, advanced time series analysis, risk management, real-time pricing and economic data analysis, customer segmentation analysis, and machine learning algorithm creation for financial technologies.

No prior programming experience required.

Learning Objectives:

  • Learn basic foundations in programming and data science
  • Receive an overview of state-of-the-art data science and machine learning methods
  • Discover how finance professionals can use data science to solve real-world problems
  • Understand the advantages of data science and specific analytical methods
  • Obtain hands-on Python programming experience
  • Understand effective data visualization techniques using Python
  • Ability to hit the ground running with executing core data tasks by the end of the course

Who Should Attend?

  • Investment professionals
    • Quantitative traders
    • Event-driven fund managers
    • PE/VC investors
    • Traditional asset managers
  • Fintech entrepreneurs and/or product managers
  • FP&A and strategy professionals at corporations
  • PE portfolio company operators
  • Private wealth managers
  • Economists
  • IT and tech professionals at financial institutions
  • Management consultants

Please Note: Participants will need to bring a laptop with them to the course.

1/19/2018 5:00 PM1/19/2018 7:00 PM

[Member Organized Event] Come spend an evening volunteering with fellow CFAW members.  We will be making and serving dinner to the hungry at DC Central Kitchen.  Event begins at 5:00pm, and volunteers should arrive 15 minutes early for orientation.  10 volunteer slots available.


2/6/2018 8:00 AM2/7/2018 4:00 PM

Course Goals & Overview:

Excel is well-known as a very powerful spreadsheet application, but its full potential can arguably only be unlocked using VBA, or the hidden code running behind the scenes. Start with an overview of the basics, such as Excel's object model, data types and variables. Write your own functions and subroutines while learning when to use one or the other, complete with five cumulative case study examples simulating actual use-cases in data validation and automation. While no background in other programming languages is required, it can definitely help during this intensive VBA primer. Those starting from scratch may even end up building the foundation for further coding skills both in Excel (or other Microsoft Office software) and in other applications, as data structures, loops, and other best practices are universal.

Learning Objectives:

  • Learn the fundamentals of VBA in Excel, including the object model, data structures, methods, and properties
  • Begin using the Macro Recorder, before progressing to the VBA Editor and getting familiar with its layout/functions
  • Explore the various elements of the VBA language, such as variables, procedures, VBA functions, and operators
  • Prepare macros with proper error handling and diligent debugging components to accommodate user error
  • Build visually pleasing user forms, following best practices in user interface design to add/manipulate data seamlessly

Course Sections – Part I:

  • Overview of the Excel object model, including the definitions of its various elements
  • Use proper syntax to reference objects, call their methods, and access its properties
  • Forgo the Macro Recorder for the full-featured VBA Editor, becoming acquainted with its various panels/windows
  • Study VBA's wide variety of supported data types (e.g. strings, doubles, Booleans) and how to select the correct one
  • Declare, set, and reset variable, arrays, collections, and more
  • Use Option Explicit to ensure strict control over variable declaration and sloppy execution
  • Annotate code with comments for increased program legibility and to help other contributing developers
  • Write functions and subroutines and learn the differences between the two as well as the best situations for both
  • Utilize Excel's built-in functions in VBA code using the Application keyword to avoid reinventing the wheel
  • Take advantage of the overlaps between VBA functions and Excel functions, especially for text manipulation
  • Understand VBA control statements, such as conditionals and the assortment of loop types
  • Create nested loops to iterate through two-dimensional arrays or ranges of rows/columns
  • Establish variable scope to ensure proper program flow and permission logic

Course Sections – Part II:

  • Identify the various ways a program can yield compile (syntax) errors
  • Handle run-time errors (exceptions) and learn to correct them beforehand based on the accompanying error code
  • Implement error handling techniques with On Error, GoTo, and Resume statements
  • Debug proactively throughout the development process via message boxes and breakpoints
  • Take a more passive approach to debugging during development via the Locals and Immediate views
  • Use Step Into, Step Over, and Step Out to execute specific lines or procedures precisely to narrow down bugs
  • Design message boxes with or without buttons, each complete with their respective subroutines
  • Prompt the user for information using input boxes that accept either inputs (e.g. text, numbers) or a cell range
  • Construct user forms by selecting the appropriate toolbox controls (e.g. TextBox, Label, Frame, buttons)
  • Manage control properties in the VBA Editor to handle both appearance (size/color) and functionality
  • Develop best practices underlying effective forms, utilizing event-specific and control-specific code prudently


  • Excel Fundamentals for the Finance Professional

Please Note: Participants will need to bring a laptop with them to the course.

3/6/2018 8:00 AM3/6/2018 4:00 PM

Course Goals & Overview: 

This course focuses on how to effectively and efficiently utilize Microsoft Excel for data analysis. A financial analyst will not only use Excel to build financial models, but also to crunch a large data dump. Learn how to minimize as much manual labor as possible, thereby saving time and performing more detailed analysis quickly. Apply commonly-used formulas in new and different ways; uncover often over-looked Excel formulas; streamline number crunching and analysis via functions and tools including pivot tables, sumif, sum+if, transpose, working with arrays, vlook-up, subtotals, and regression analysis; enhance your spreadsheets with drop-down boxes, data validation techniques, automation of alternate row shading; take Excel to the next level with emphasis on automation.

Learning Objectives:

  • Learn how to minimize as much manual labor as possible in data analysis 
  • Learn to use the most overlooked Excel formulas that will make your life easier 
  • Learn powerful functions built in Excel that streamline your analysis

Course Overview:

  • Master Excel shortcuts via formatting & analytical exercises encompassing efficiencies, shortcuts & sensitivity analysis
  • Data integrity techniques: understand how Excel implodes when you don’t maintain integrity of your raw data
  • Learn different “switches alternatives” (if, choose, offset and toggles ) to build more robust analyses
  • Understand why IF statements are the second root of all evil in Excel and why they should be avoided at all costs
  • Learn data validation techniques to dummy proof your model and provide additional error checking in your analysis
  • Add some spice to your Excel analysis and models using dropdowns and how to automate options and scenarios
  • Automate alternate row shading in a table of data using complex conditional formatting 
  • Fully automate vlookup to streamline tedious analysis while understanding the limitations of vlookup
  • Understand why OFFSET(MATCH) is vastly superior to vlookup and why we discourage use of INDEX function
  • Pivot Tables and Pivots on Steroids: summarize and dissect large amounts of data as well as calculated fields
  • Truly unlock the full power of Excel by utilizing ARRAYS simplify complex calculations
  • Learn how to use the transpose array function without static copy paste special transpose as values

Please Note: Participants will need to bring a laptop with them to the course.

4/4/2018 9:00 AM4/4/2018 5:00 PM
Course Goals and Overview:

This course focuses on the mergers and acquisitions process, the basics of deal structures, and covers the main tools and analyses that M&A investment bankers and acquirers utilize. Learn about common ​​structural issues, crucial merger consequence analysis and structures and methodologies. Translate fundamentals into different modeling techniques, including the most basic and widely used back-of-the-envelope method, Accretion / Dilution, as well as a more robust combination analysis combining a Target and Acquirer's Income Statement. Learn how to sensitize basic deal structures and combination options. 

Learning Objectives:

  • Common structural issues in a transaction (stock vs. asset, 338(h)(10) elections)
  • Merger consequence analysis including accretion / dilution and financial implications of a deal
  • Build a fully functional accretion / dilution model that accounts for different transaction structures
  • Learn how to sensitize financial projections and the financial impact on a transaction

Course Sections:

M&A Deal Structuring

  • Review of various deal considerations and deal structuring options (cash vs. stock)
  • Common structural issues in a transaction (stock vs. asset, 338(h)(10) elections)
  • Buyer and seller preferences for various deal structures and rationale
  • Tax implications of transactions based on deal structure and FASB 142 goodwill amortization
  • Merger consequence analysis including accretion / dilution and financial implications of a deal
  • Analysis of breakeven PE for both 100% stock and 100% cash considerations
  • Dive deep into merger accounting for your merger model including NOL treatment and FMV step-up

Accretion Dilution Modeling

Build dynamic merger consequence analysis (accretion / dilution) incorporating the following:

  • Synergies switch, cash vs. stock sensitivity
  • Amortization of goodwill switch (depending on purchase price allocation)
  • Common structural issues: Stock vs asset deals and 338 (h)(10) elections
  • Tax implications of transactions based on deal structure and FASB 142 goodwill amortization
  • Analysis of breakeven PE for both 100% stock and 100% cash considerations
  • Calculate pre-tax and after-tax synergies/cushion required to breakeven

Simple Merger Modeling

Construct a merger model, a simple combination of Income Statement for target and acquirer:

  • Project simple stand-alone Income Statement for both target and acquirer
  • Analyze selected balance sheet figures and ratios and multiples
  • Estimate target valuation and deal structure
  • Calculate selected Pro Forma balance sheet items
  • Combine target and acquirer's Income Statement and estimated synergies
  • Calculate cash flow for debt repayments to estimate debt repayments and cash balances
  • Compute interest expense and interest income based on pay downs
  • Calculate accretion / dilution and credit ratios

Please Note: Participants will need to bring a laptop with them to the course.

4/25/2018 12:00 PM4/25/2018 2:00 PM

Course Goals and Overview:

Take advantage of this rare opportunity to get live, in-person training from an expert and former chief architect, who helped develop many of the ETF analysis capabilities on the Bloomberg terminal. 

Learning Objectives:

  • Accessing ETF Industry Flows across all asset classes
  • Utilizing ETF Due Diligence tools
  • Evalu ating underlying ETF liquidity
  • Building comparison reports for ETFs and Mutual Funds
  • Factor Decomposition of ETFs & Portfolios
  • Fixed Income ETF performance & attribution analysis
  • Calculate Fixed Income ETF risk statistics
  • ETF Institutional Ownership
  • Accessing Industry news across all asset classes
  • Retrieving ETF research on the Bloomberg Intelligence Platform
  • Building index reports across all asset classes
  • Evaluating holdings data for all ETFs
  • Building risk-return due diligence reports on ETFs
  • Accessing the total trading costs, securities lending, management fees and other fees associated with ETFs
  • Evaluating all Creation-Redemption Costs for an individual ETF

Who Should Attend:

  • Anyone who uses a Bloomberg terminal and is interested in the ETF industry
5/8/2018 9:00 AM5/8/2018 1:00 PM

​Technical analysis is becoming more widely used by analysts, portfolio managers and even central banks.   While it was not considered mainstream as recently as a decade ago, technical analysis is now taught on many college campuses and has even made its way into the curriculum of the CFA examinations.  But technical analysis approaches the market from a very different perspective than fundamental analysts – assuming that wisdom is inherent in the markets, not in financial statements.  Technical analysis is used in equity, debt, commodity and currency markets around the world and can be used alone or as a complement to fundamental analysis.

To introduce our members to the field and provide them with a half-day course designed to provide them with a working knowledge of technical analysis, the CFA Society of Washington DC is conducting a half day seminar entitled Technical Analysis – The Basics.  Our instructor, Barry M. Sine, CFA, CMT, is the co-author of the CFA exam readings on technical analysis and was the founder and first director of the CMT Institute for the Market Technicians Association.  He is a publishing technical analyst and a CNBC on air contributor.
The course will cover the basic theory and assumptions behind technical analysis, then move on to practical tools including chart construction, chart patterns, volume, moving averages and momentum oscillators.  It will include current technical examples of major financial markets including a discussion of Dow Theory and what it is signaling to investors presently.  Participants are invited to send suggestions of financial instruments they are interested analyzing in advance, and the instructor will try to work as many of these into the curriculum.
**Breakfast and lunch will be served.
5/16/2018 9:00 AM5/17/2018 5:00 PM

Course Goals & Overview:

This 2-day course provides an in-depth introduction to credit risk. Techniques for modeling credit transition matrices are covered in great detail, while several statistical techniques for modeling default probabilities and correlations are explored in depth. Methodologies for modeling credit portfolio risk are covered, including the asset value approach and the structural approach. Prepayment models are developed for Mortgage-Backed Securities (MBS). All models are developed in Excel/VBA.

Learning Objectives:

  • Excel - learn several of Excel's specialized functions. Understand how to use Excel's add-in tools to implement advanced statistical techniques, such as regression analysis. Learn how to use Solver, Excel's optimization package.​
  • Visual Basic for Applications (VBA) - learn the fundamental programming structures of the VBA language, and how it can be used to extend Excel's capabilities.
  • Statistical foundations - learn to implement Monte Carlo simulation using Excel/VBA. Learn techniques for improving the speed of convergence, including importance sampling and low-discrepancy sequences. Understand the binomial and Poisson distributions. Learn the fundamental principles of linear regression analysis, as well as Poisson regression. Understand the maximum likelihood and method of moments approaches to statistical estimation.
  • Merton's model – understand Merton's model of credit risk; learn how it is related to the Black-Scholes model and how it can be used to compute default probabilities.
  • Credit ratings transition matrices - understand the structure of a transition matrix. Learn how to estimate a transition matrix with the cohort approach and the hazard rate approach.
  • Estimating default probabilities and correlations - understand how to use linear regression analysis to estimate default probabilities. Learn how to apply Poisson regression to estimate default probabilities. Understand how the asset value approach can be used to estimate default correlations using the method of moments approach and maximum likelihood approach.
  • Credit portfolio risk models - understand different approaches to modeling credit portfolio risk. Learn how to use Monte Carlo and Quasi-Monte Carlo simulation to implement the asset value approach. Learn how the structural approach is used to explain the sources of credit risk, and how it can be implemented as an extension of the Black-Scholes option pricing model.​
  • Prepayment modeling – understand the structure of Mortgage-Backed Securities (MBS) and MBS derivatives, such as Interest-Only (IO) strips and Principal-Only (PO) strips. Understand different measures of prepayment speed, such as Single Monthly Mortality (SMM), Conditional Prepayment Rate (CPR) and Absolute Prepayment Speed (ABS). Learn how to implement these measures in Excel.

Course Sections:

  • Implement statistical foundations, including Monte Carlo simulation using built-in native Excel functions and tools
  • Understand the structure of a credit ratings transition matrix and estimate using the cohort approach and the hazard rate approach
  • Estimate default probabilities and correlations, using Merton's model of credit risk, linear & Poisson regression analysis, the asset value approach (method of moments and maximum likelihood approaches)
  • Simulate and model prepayment rates, incorporating the structure of MBS & related derivatives, including IO and PO strips
  • Model different measures of prepayment speed, such as Single Monthly Mortality (SMM), Conditional Prepayment Rate (CPR) and Absolute Prepayment Speed (ABS)
  • Utilize Excel's specialized functions, including advanced statistical techniques, and Excel's built-in optimization tools
  • Code in Excel VBA: learn the fundamental programming structures and how it can be used to extend Excel's capabilities in Credit Risk Modeling

**Please Note: Participants will need to bring a laptop with them to the course.
5/22/2018 9:00 AM5/23/2018 5:00 PM

​​Course Goals and Overview:

Databases are ubiquitous and used in almost every organization. Understanding the basics of databases and how to access and manipulate data efficiently allows business professionals to significantly boost their productivity and decision making skills.

This course provides a hands-on introduction to databases and SQL. At the end of the course, participants will be confident in working with database systems to effectively analyze relevant data and draw meaningful business conclusions.

Learning Objectives:

  • Overview of different database technologies
  • Experience working with relational databases
  • Introduction to SQL and how to access and manage complex data

Who Should Attend:

Junior and senior professionals who work with databases and want to understand how to access and manipulate data efficiently

Course Sections:


  • What are relational databases, why are databases so important, overview of different database technologies, Excel vs. databases, query & syntax tips

From Data to Tables

  • Introduction to fundamental database concepts, how to structure data in tables, relationship between tables, keys and indices, normalization

Most Important SQL Commands

  • Overview of the most important  SQL statements including SELECT, BETWEEN, IN, WHERE, GROUP BY, HAVING, etc., including writing complex queries with these commands
  • How to gather basic descriptive statistics on large datasets
  • Aggregating data
Advanced SQL Functionality

  • Subqueries

In-Class Project

  • Hands-on experience importing, preparing, analyzing, and drawing meaningful business insights from a real-world database

Final Exam

Wrap-up & Summary

  • Where to go from here, recommendation of additional resources

What is needed from you:

Participants will need to bring a laptop with them to the course.
6/12/2018 9:00 AM6/12/2018 5:00 PM

​Course Goals & Overview:

Balance sheet based companies, such as banks, play by different rules and methodologies based on the unique nature of their business. Focus is placed on our Commercial Banks financial statements primer which dives deep into a bank's unique financial statement terminology and drivers. Understand how to analyze a bank and why the standard financial analysis and valuation methodologies that apply to most companies do not apply to industries that "use money to make money". Start with a brief overview of the main banking functions (commercial, investment, asset management) and quickly turn to the quality of book of loans and analysis of net vs. gross charge-offs vs. provisions, etc. Understand the critical credit ratios and capital adequacy analysis as well as Tier 1 and II definitions and Basel II impact. Crystallize the impact of Interest Rates, importance of term structure and credit spreads and implications on a bank's profitability. Examine best practices in calculating net interest income via average asset and liability balances on the income statement. Dive into an analysis of Balance Sheet assets & liabilities and articulate the drivers of EPS growth. Wrap up by analyzing valuation parameters: key banking valuation multiples (PE, PEG, Book Value, ROE).

Build a basic, streamlined bank financial model that builds upon the bank terminology in our Bank Industry Primer course. Before diving deep into the complex nuances of our Advanced Bank Financial Model, really solidify your understanding of developing the logic in loan losses and provisions and its impact on the rest of the larger bank financial statements. Perform quick back-of-the-envelope calculations for key Balance Sheet items such as Interest Earning Assets and Interest Bearing Liabilities, which yield Net Interest Income. Estimate and calculate capital adequacy ratios to wrap up your summary simplified basic bank model.

Learning Objectives:

•  Understand banking industry specific terminology, jargon and financial analysis

• Compare and contrast the approach to bank financial  modeling vs. typical non-financial institution

• Comprehend the key drivers of growth of a commercial lending institution and translate them to a model

• Construct a simplified bank model to crystalize the fundamental concepts and why one must start with the Balance Sheet instead of the Income Statement

Course Sections:

Banking Industry Overview

• Overview of main banking functions (commercial, investment, asset management)

• Quality of book of loans and analysis of net charge-offs

• Critical credit ratios and capital adequacy analysis; Tier 1 and 2 definitions and Basel impact

• Impact of Interest Rates, importance of term structure and credit spreads

Banking Financial Statement Terminology & Drivers

•  Net Interest Income Margin (Interest Expense net against Revenue not COGS)

•  Analysis of Balance Sheet Assets & Liabilities

•  Drivers of EPS growth

•  Valuation Parameters: key banking valuation multiples (PE, PEG, Book Value, ROE)

Bank Financial Modeling

• Perform quick back-of-the-envelope calculations for key Balance Sheet items

•  Guestimate Interest Earning Assets and Interest Bearing Liabilities

•  Calculate and estimate Net Interest Income

•  Estimate and calculate capital adequacy ratios to wrap up your summary simplified bank model


To maximize the educational value of these programs, we strongly recommend that you have an intermediate understanding of Excel. Lack of basic Excel skills will impede your ability to effectively acquire and implement the techniques and shortcuts that are presented in this program.  Our courses are extremely interactive, hands-on with intensive focus on Excel shortcuts and efficiency.

Bring a PC laptop with Microsoft Excel installed, and a working USB port (in case our email containing in-class materials gets lost in your junk/spam folder, we can distribute them via flash drive). If you can only bring a Mac, please avoid Office 2008 and ideally set up a Windows environment via Boot Camp, Parallels, or VMware.

6/26/2018 12:00 AM6/28/2018 11:59 PM

​Course Goals and Overview:

This hands-on data science course is a sequel to the Introduction to Data Science for Finance workshop. Advanced Data Science for Finance will provide an overview of modern machine learning algorithms that analysts, portfolio managers, traders and chief investment officers should understand and in a context that goes beyond a broader level introductory class in data science. Classification methods are touched upon in the introduction course, but the Advanced Data Science for Finance course focuses exclusively on this highly demanded and rapidly adopted segment of data science and machine learning.

This course will explore advanced classification methods including neural networks and decision trees which are among the most effective data science techniques. This workshop also provides an introduction to deep learning, a technique which has significantly increased the performance of machine learning algorithms over the last years and is heavily used in the financial services industry. Deep learning utilizes algorithms and methods that perform in a similar manner to the human brain. According to Gartner, 80% of data scientists will be competent in deep learning and deep learning will be utilized in a much larger role in different forms of predictive analytics across all functional areas of business including finance and markets.

At the end of the workshop, participants will be comfortable applying the Python programming language to build common classification algorithms and evaluate & interpret their accuracies in the context of finance.

Learning Objectives:

  • An overview and specific focus on core classification methods and how to use them to solve real-world problems in the finance industry.
  • Aims to provide attendees with a high level understanding and working knowledge of highly coveted artificial intelligence areas including deep learning and neural networks and their direct application to the field of financial analysis and capital markets.
  • Provide attendees that work in the finance industry with the ability to evaluate and select from a variety of classification methods and tools as these techniques continue to be adapted and implemented at an ever increasing rate.
  • Further and more advanced hands-on Python programming experience beyond the introduction course.

Who Should Attend:

  • Individuals working with or needing to understand machine-learning algorithms, specifically classification methods.
  • Graduates of the Introduction to Data Science for Finance course. The introduction course or equivalent is a prerequisite.

Course Sections:

Review of Core Data Science Methods

  • Supervised vs. Unsupervised learning, Classification, Regression, Clustering, Dimensionality Reduction, Ensemble, etc.

Selecting Informative Attributes

  • Information gain and entropy, overfitting/generalization

Decision Trees &  Random Forests

  • What is it?
  • How to do this in Python
  • Coding Challenge

K-Nearest Neighbors

  • What is it
  • How to do this in Python 
  • K-Nearest Neighbors Coding Challenge

Support Vector Machines

  • What are they?
  • How to do this in Python - example
  • SVM Coding Challenge

Neural Networks

  • What is it
  • How to use this in Python with an example
  • Neural Nets Coding Challenge 

Deep Learning

  • Why the hype?
  • How to get started with deep learning

Evaluation of Classification Methods

  • Accuracy, confusion matrix. ROC, AUC, Precision, Recall, etc.

Final Project

  • Given a dataset and a classification mandate, attendees have to run these different classification models and figure out which one is "best"

What is Needed from You:

  • Participants will need to bring a laptop with them to the course.
  • It is recommended that participants have previously taken Introduction to Data Science for Finance as a prerequisite. If you have not been able to take this course, please contact the instructors at

  Contact Us
CFA Society Washington, DC
1200 Eighteenth St. NW, Suite 700
Washington, DC 20036
Phone: +1 (202) 872-4310
Fax: +1 (202) 315-3332