CFA Society Washington, DC

A Member of the CFA Institute Global Network of Societies

 Events

Annual Dinner 2016

          Policy & Market Implications of the 2016 Election

 

     

                      Annual Member Reception


  

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Title
  
Location
  
Start Time
End Time
Event Description
  
4/6/2017 9:00 AM4/6/2017 5:00 PM

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 Acquiror’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 acquiror:

  • Project simple stand-alone Income Statement for both target and acquiror
  • 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 acquiror’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 paydowns
  • Calculate accretion / dilution and credit ratios

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

  
4/6/2017 1:00 PM4/6/2017 3:00 PM

​Join us CFA Society of Washington, D.C. and CAIA Washington, D.C. for a presentation on accessing the investment characteristics of the real estate asset class through investments in exchange-traded equity REITs and iliquid real estate exposure (private equity real estate investment funds, direct property ownership, etc.), with special attention to opportunities created by the current valuation discrepancy between the public and private sides of the real estate market.

Learning Objectives:

  • Learn about empirical evidence regarding long-term (strategic) returns, volatilities, and correlations in the real estate asset class.
  • Learn about the effects of liquidity, leverage, uninvested capital, investment costs, transparency, and principal/agent issues on real estate investment performance.
  • Learn about real estate valuation metrics and what they imply for tactical asset allocation decisions.
  • Learn about new developments in risk management for real estate portfolios.

Who Should Attend?

  • Institutional investors with real estate or mixed-asset portfolios
  • Investment consultants
  • High-net-worth investors and advisors
  • Investment managers including target-date fund managers
  
5/9/2017 9:00 AM5/10/2017 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.

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  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