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3/27/2019 6:00 PM3/27/2019 10:00 PM

2019 CFAW Annual Dinner

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The 2019 Annual Dinner will be held at The Ritz-Carlton, Washington, D.C. Each year, the Society hosts an Annual Dinner, which is our most prestigious event of the year and routinely sells out. It has become the “who’s who” event for the Washington, DC financial investment industry. Over 400 investment professionals attend, representing leading asset managers and advisors, corporations, institutional investors, regulatory agencies, and international financial institutions in the Washington, DC metro area. 
 

We are excited to announce that this year’s Annual Dinner Keynote Speaker is Barbara G. Novick, Vice Chairman of BlackRock.

 
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Barbara G. Novick
Vice Chairman of BlackRock
 
Barbara G. Novick, Vice Chairman, is a member of BlackRock's Global Executive Committee, Enterprise Risk Committee and Global Operating Committee. From the inception of the firm in 1988 to 2008, Ms. Novick headed the Global Client Group and oversaw global business development, marketing and client service across equity, fixed income, liquidity, alternative investment and real estate products for institutional and individual investors and their intermediaries worldwide. In her current role, Ms. Novick oversees the firm's efforts globally for public policy and for investment stewardship. Ms. Novick has authored numerous articles on asset management and public policy issues. 
 
Click here for full speaker bio. 
 
Thank you Main Event Sponsor
 
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4/2/2019 5:00 PM4/2/2019 7:00 PM

Member Organized Event

Sustainable Investing goes by many names - impact investing, ESG, SRI, double-bottom line. It's where you align your investments with your values.

All are welcome!

Whether you're a Sustainable Investing professional who wants to discuss your experience and share best practices, someone who'd like to learn more about working in the space, an investor who wants to learn more about aligning your investments with your values, or even a skeptic itching for a healthy debate – this is the gathering for you!

  
4/4/2019 8:00 AM4/4/2019 4:00 PM

M & A Deal Structuring and Merger Modeling

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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
**IMPORTANT - PLEASE NOTE**

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.​
  
4/11/2019 7:45 AM4/11/2019 5:30 PM
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ESG ASSET OWNERS SUMMIT
The Rise & Status of Sustainable Investing

Thursday, April 11, 2019 | 7:45AM – 5:30PM
​Renaissance Washington DC Downtown Hotel 
 
CFA Society Washington, DC invites you to join us at this informative and thought-provoking event to hear from top influencers, experienced investors, academics and researchers who are leading the charge on environmental, social and governance (ESG) investing trends. We have dedicated a full day to the rise and status of sustainable investing among asset owners. The agenda includes multiple panel sessions that will be engaging and enlightening with a mix of discussions and Q&A. You’ll hear from asset managers, asset owners, the World Bank Group as well as panels on  Global Perspectives on ESG Demand & Implementation, Measuring Sustainability and Big Alpha Data. Over the next four decades, it is estimated that over $41+ trillion will transfer from baby boomers to Millennials. As we move to the next generation of investors, we are going to find companies aligning their beliefs, operation, and communication strategies with that of the Millennials.
 
The CFA Institute’s 2017 survey of CFA Institute members showed that 73% take ESG into account in their investment analysis and decisions. However, the survey also showed that many investors did not understand just what was meant by ESG integration or ESG investing and were seeking guidance on “how” to best integrate ESG factors in the investment process.  
 
This 1-day summit will answer your questions in this ever-growing area, provide insight and tools to help you integrate sustainable investing practices and provide valuable connections with speakers and attendees. 
 
Who should attend: asset owners, asset managers, financial advisors, investment professionals, portfolio managers, wealth managers, analysts, researchers, members of mission-driven organizations, and others who share a common interest in sustainable, responsible, and impact investing.
 

Speakers

We are delighted to announce our speakers. 
Additional speakers will be announced as they are confirmed.

 
Keynote Speaker
Robert G. Eccles, Visiting Professor of Management Practice, Said Business School, University of Oxford
 
David Barrosse, Founder & CEO, Capstone
 
Kenneth Bertsch, Executive Director, Council of Institutional Investors 

Preeti Bhattacharji, Vice President of Integrated Capitals, The Heron Foundation
 
Atiyah Curmally, FI Global Sector Lead/Principal Environment and Social Specialist, International Finance Corporation (IFC)
 
Rui de Figueiredo, Co-Head and CIO of the Solutions & Multi-Asset Group, Morgan Stanley Investment Management
 
Ariane de Vienne,  Head of ESG Strategy - Americas, Institutional Shareholder Services (ISS)
 
Matthew DiGuiseppe, VP and Head of Americas on the Asset Stewardship Team, State Street Global Advisors
 
Timothy P. Dunn, CFA, CIO, Managing Member and Founder, Terra Alpha Investment, LLC
 
Nalini Feuilloley, Head of Canada, UN-supported Principles for Responsible Investment (PRI)
 
Sonja Gibbs, CFA, Managing Director, Global Policy Initiatives, Institute of International Finance (IIF) 
 
Jade Huang, Vice President and ESG Portfolio Manager, Calvert Research and Management
 
Evan Harvey, Director of Corporate Responsibility, Nasdaq
 
Sean Kidney, Co-Founder and CEO, Climate Bonds Initiative
 
Dinah A. Koehler, Equity Strategist and Director, UBS Asset Management
 
Olha Krushelnytska, Green Finance Specialist, The World Bank Group/The Global Environment Facility
 
Carole Laible, Chief Executive Officer,  Domini Impact Investments
 
Stephen Malinak, Chief Data and Analytics Officer, TruValue Labs 
 
Gianna McCarthy, Director of Corporate Governance, New York State Office of the Comptroller
 
Hiro Mizuno, Executive Managing Director and CIO, Japan's Government Pension Investment Fund (GPIF)
 
Matthew Orsagh, CFA, CIPM, Director of Capital Markets Policy, CFA Institute 
 
Anna Pot, Manager Responsible Investments, APG Asset Management
 
Heike Reichelt, Head of Investor Relations & New Product Development, Capital Markets Treasury, The World Bank Group 
 
Frederic Samama, Deputy Global Head of Institutional Clients, Amundi Asset Management Paris
 
Kate Starr, CFA, Chief Investment Officer, Flat World Partners​
 
Fiona Stewart, Lead Financial Sector Specialist, Finance, Competitiveness & Innovation, The World Bank Group
 
Gillian Tett, US Managing Editor, Financial Times
 
Gabriel Thoumi, CFA, Director of Capital Markets, Climate Advisors​
 
Diederik Timmer, Executive Vice President, Client Relations, Sustainalytics
 
Tim Youmans, Engagement Director, Hermes EOS
 
Michael Young, Manager of Education Programs, US SIF

*This event is in-house and is not being broadcasted‎ nor recorded. No media will be present and speaker or attendee attribution, in any form, is a violation of CFA Society Washington, DC’s code of conduct. Seats are limited to investment professionals only.
  
4/18/2019 9:00 AM4/18/2019 5:00 PM

​Global Macroeconomics (and Implications on Rates)​

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Course Goals & Overview:

Economics – if not dismal, the “science” can certainly be frustrating. Ask yourself, do weak employment figures portend a decline in corporate profits and falling equity prices, or does it signal potential intervention from the central bank and rising equity prices? Exasperating, right?

The application of economic data to real world investment decisions often requires a secondary and even tertiary analysis of its meaning. Said differently, using economic data in the real world is more a “sentiment game” than a mathematical formula. What is a sentiment game? Keynes would describe it as a newspaper beauty contest, but more technically it’s a strategic interaction between multiple players seeking to ascertain not necessarily their interpretation of a given set of information, but the interpretation and reaction of the other players in the game.

This Global Macroeconomics course examines the practice of interpreting economic information in a way that is helpful to decision makers. We address key theoretical concepts including basic macroeconomics, the business and debt cycles, monetary and fiscal policy, and international trade; but also leave the ivory tower to examine actual economic releases and discuss not what “should” happen but what does or can happen.

The course is broadly divided into two sections: Core Concepts and Key Economic Indicators & Data Series. The Core Concepts section of the course covers introductory economic theories and models that are required background information for economic analysis. This is done through an explanation of content followed by a real world example taken from a leading financial news source. The second portion of the course looks at key economic data series including among others, employment figures, price levels, monetary policy measures, and business/consumer activity measures. We use recent economic data to make it more applicable to current investment decisions and avoid the obfuscation that often accompanies older data sets.

Students should walk away with a better understanding of basic economic theory, how it translates into real world application, and knowledge about the distribution of and meaning behind important economic indicators. This is perfect for investment decision makers looking to integrate economic analysis into their decision making process or more experienced “economists” looking for a review of key concepts.

Core Concepts:

  • Basic Macro: fundamental understanding of the global economy; aggregate supply/demand, gaps, stagflation, etc
  • Business & Debt Cycles: determinants of economic growth, Neoclassical vs. Keynesian economics and implications
  • Monetary and Fiscal Policy: monetary vs. fiscal policy impacts and trading implications for rates trading desks
  • International Trade: comparative advantage and impact of trade treaties on trading strategy
  • Balance of Payments and FX: impact of balance of payments and foreign exchange trade strategies
  • Key Economic Indicators & Data Series:

Understand what each indicator is, importance of and strengths and limitations of each of the following:

  • Business Activity: business outlook, durable goods & factory orders report, production, capacity utilization and others
  • Employment: employment cost index, employment situation, jobless claims report and related employment figures
  • Real Estate: existing home sales, housing starts, new residential sales
  • Prices: consumer price index, headline vs. core, producer price index
  • Monetary: Federal Reserve Beige Book, Fed communications and signaling, money supply, commercial banks
  • Consumer: consumer confidence index, consumer sentiment index, consumer credit report, personal income
  • International and Output: international transactions, GDP, productivity and costs
  • Other: commodities, 10-year government bonds, currencies, other miscellaneous indicators
  
5/2/2019 4:30 PM5/2/2019 6:30 PM

Speed Reading for Investment Professionals

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Too much to read? Not enough time? There is no greater skill than having the ability to get through new information quickly and be able to remember it. This workshop is for investment professionals and teaches how to approach technical material and analytical reading more effectively (annual reports, contracts, research, market news through a variety of outlets - Bloomberg, WSJ, Financial Times). Learn techniques to read faster, remember more and boost your productivity. CFA Society Washington, DC has partnered with Iris Reading, the largest and most trusted provider of speed-reading & memory training, to bring this workshop to you. We expect this workshop to sell out. Seats are limited, register today!​

Learning Objectives:
  • Increase your reading speed using practical techniques
  • Improve comprehension with better focus & concentration
  • Approach technical material & analytical reading more effectively
  • Read faster on the computer screen and other digital devices
Who Should Attend? 

 
Everyone who wants to faster, remember more and boost productivity
  
5/21/2019 9:00 AM5/22/2019 5:00 PM

​ Introduction To Data Science & Python for Finance

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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.
  
6/4/2019 9:00 AM6/5/2019 5:00 PM

Recapitalization Modeling

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Course Goals & Overview:

 
This course walks through the concepts behind company recapitalizations (recaps) and quantifies the impact of the various approaches available. The first section opens with a high-level discussion of recaps and debt/equity swaps. Next is a brief primer on key credit ratios and how they can be implemented with best practices in Excel. The meat of the course then begins, reviewing multiple recapitalization strategies one by one, thoroughly explained qualitatively and executed quantitatively in a working Excel model. Afterward, these results are analyzed before wrapping up with a complicated scenario and sensitivity layer that can determine viable scenarios based on different assumptions of credit/leverage statistics. What’s left is a comprehensive model capable of simulating strategies for any recap situation.

 
Course Sections:

 
Intro to Recapitalizations
  • Understand the definition of a recapitalization, including the parties and subjects involved
  • Identify a recapitalization’s general motivations and consequences
  • List several real-world use cases of recaps with an emphasis on standard debt-for-equity swaps
Plain Debt Sweep & Credit Ratios:
  • Review basic mechanics of financial statements to understand a debt sweep’s structure
  • Create a simple debt/interest schedule in Excel using real historical financial statements
  • Discuss and calculate various key credit ratios including leverage, net leverage, FCCR, and interest coverage
  • Quantify the impact that changes to revenues/expenses have on EBITDA and cash flow
Recapitalization Model - Status Quo and Swap:

 
  • Start with a “status quo” recap option spanning across three cases (base, downside, worst)
  • Add an initial recap option for taking on a debt/equity swap, ensuring the model also accounts for interest rates, etc.
Recapitalization Model Enhancement - Swap with Paid-in-Kind (PIK) Debt:

 
  • Implement a fourth recap option incorporating the conversion of the debt/equity swap’s balance to PIK debt
  • Fully integrate all the PIK components into the model, noting the PIK’s impact on cash flow
  • Add “cash interest” alternatives for potentially misleading ratios such as EBITDA/Interest and EBIT/interest
Recapitalization Model Enhancement – Scenario Pairing:

 
  • Lay out different scenarios depending on assumed credit/leverage statistics
  • Modify the model to easily switch between viable scenarios
  • Create ratings statistics tables for the scenarios to quickly gauge the available options
Recapitalization Model Enhancement – The Works:

 
  • Implement a fifth recap option incorporating the debt/equity swap, a term loan, and PIK debt
  • Add a “Cash DSCR” ratio and utilize Excel’s conditional formatting to draw attention to unfavorable scenarios
  • Carefully re-examine DSCR under the fourth and fifth recap options to clarify an otherwise simple analysis
Conclusion:
  • Select the best courses of action (per case) to recapitalize, and develop a solid understanding of managing cash flow
  
6/11/2019 9:00 AM6/13/2019 5:00 PM

Financial Valuation and Modeling

Course Goals and Overview:

This boot camp is designed for current and future finance professionals who have never participated in a multi-day formal classroom program on financial and valuation modeling. This intensive instructor-led seminar bridges the gap between an academic understanding on finance and the hands-on skill-set that is needed on the job. Attendees will sharpen their financial and valuation modeling skills in Excel using an intuitive, step-by-step approach. The program is a synthesis of Excel modeling, navigating through various financial reports, and the application of accounting, corporate finance, and valuation courses.

Who Should Attend:
  • Investment banking analysts and associates
  • Private equity, asset management and hedge fund associates
  • Corporate finance and business development professionals
  • MBA students and business undergraduates
  • Anyone seeking to improve their financial modeling skill set
  • Course Details:
Pre-Seminar Excel Training  
This boot camp assumes proficiency in Excel. Enrollment includes access to our popular Excel Crash Course for those who need an Excel refresher.
 
Day 1 - Financial Statement Modeling
You will develop a 3-statement model completely from scratch, inputting historical data and assumptions to project out financial statements by selecting, locating, and developing appropriate projection drivers.
 
Day 2 - DCF Modeling
Participants learn how to value the company step-by-step, including how to estimate the weighted average cost of capital (WACC) in the real world, how to implement commonly used approaches to calculating terminal value, and how to use data tables to analyze a broad range of scenarios given different assumptions. 

Day 3 - M&A Modeling
Participants will build a merger model in Excel to reflect the pro forma impact of various acquisition scenarios. Key topics covered include a quick test of accretion / dilution in all-stock deals, pricing structures (exchange ratios, collars, "walk-away" rights), purchase accounting, and the step-by-step allocation of purchase price.
 
Post-Seminar Online Access
Enrollment includes lifetime access to the following online course(s): Financial Statement Modeling, DCF Modeling, Comps Modeling, M&A Modeling, and LBO Modeling.

What is needed from you:
Participants will need to bring with them to the course a laptop with Excel, version 2003 or higher
  
6/26/2019 9:00 AM6/26/2019 5:00 PM
Advanced Data Science & Machine Learning 

with Python for Finance

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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 info@cognitir.com
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Disclaimer​​​

The views expressed by the speakers do not necessarily reflect the views of policies of CFA Society Washington, DC, its Board of Directors or its members. CFA Society Washington,​ DC does not guarantee the source, originality, accuracy, completeness or reliability of any statement, information, data, finding, interpretation, advice, opinion, or view presented, nor does it make any representation concerning the same


​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
info@cfawashington.org