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Intermediate Investment Analytics & Data Visualization with R - Module 4 [Complete] Event Code: 170610W
Date 10-Jun-2017
Time 08:15 a.m. - 1:00 p.m.
Venue 14/F, BOC Group Life Assurance Tower, 136 Des Voeux Road Central, Hong Kong
Speaker(s) Mr. Mark C. Hoogendijk, CFA, CAIA
Managing Director
E8 Consulting Asia
Fee HKSFA Member(s) at HK$1,850.0/person
CFA Candidate(s) at HK$2,350.0/person
Guest(s) and Non-member(s) at HK$2,350.0/person
This seminar is qualified for 3.5 CPT hour(s), 3.5 CE hour(s), 3.5 RBV CPD hour(s)
  • Only Visa and Master Card are accepted for online payment.
  • Participants are expected to bring their laptop / notebook.

Event Details
These 5 half-day / module courses are a direct extension of the “Investment Analytics & Data Visualization with R” course, which successfully ran already thrice in the last 18 months. The course will build upon the acquired skill sets of the previous introductory course and focus on Advanced Data Mining techniques required for portfolio and risk management of large portfolios. Topics also include Derivatives Pricing and Simulation, Portfolio Back-Testing, Machine Learning and creating Portfolio / Risk Dash-Boards with automated month-end reports. The modules are great for expanding ones understanding of the R language, whilst learning more about Portfolio & Risk Analytics together with Data Visualization.
All modules are based on up-to-date and historical market prices focusing on Stocks, ETF’s, FX Rates and Macro-Economic Indicators. Packages such as dplyr, tidyr, tibble and purrr will be used for fast Data Manipulation, required for the amount of data that will be analyzed.  (The modules will work with a database of 4700 stocks including historical price data, balance-sheet data, option data, splits and dividends and calculated technical indicators).
Basic understanding of R is required. This course is a beginner to intermediate level course for R.
Similar to previous modules, all code will be shared and provided to the participants. Full overview of available online materials will be shared too. Including an overview of additional financial packages to be used for further Portfolio Analytics. Participants are expected to bring their laptops / notebooks.   

Course Overview
  • Module 1: Intermediate Data Mining. Building your portfolio database.  Running Performance & Risk Analytics over a 4700 large stock database. Adding technical indicators to the database. 
  • Module 2: Fundamental Stock Analysis in R.  Running a selection model based on Fundamentals, building screeners and capturing the outcomes in powerful visuals and tables.
  • Module 3: Option Pricing & Option Backtesting in R. Back-testing plain vanilla option hedging strategies on portfolios over multi-period timeframes including re-balancing.
  • Module 4:  The Efficient Frontier, back-testing the Efficient Frontier, and implementing the Black-Litterman model. Efficient Frontier with own moment functions.
  • Module 5: Introduction to Machine Learning for Finance.

Module 4: Efficient Frontier & Black-Litterman in R


Summary: This module provides great insights into calculating the Efficient Frontier, based on different Risk measures, such as Std.Dev, VaR, CVaR and Drawdowns, understanding the impact of using varying historical time-windows as input, and analyzing the impact of different constraints. The module also focuses on built in functionality for back-testing the application of the Efficient Frontier over time including rebalancing of the Frontier. Incorporating one’s own moment function for the Efficient Frontier is also demonstrated. The module ends with the implementation of the Black-Litternman model in R.

      Session 1: Return & Risk Metrics

  • Calculating VaR, CVaR, Drawdowns and several risk ratios
  • Calculating & Visualizing the Co-Variance & Correlation matrices
  • Calculating & Plotting the Efficient Frontier based on varying constraints and different Risk Metrics, such as Variance, CVaR and Drawdowns. Calculations are done for

o   Dow Jones Industrial Average constituents

o   Selected portfolio of US based ETF’s

      Session 2: Backtesting the Efficient Frontier assumptions and constraints

  • Setting the parameters and assumptions for Back-Testing
  • Initial basic backtest
  • Analyzing the impact of varying input assumptions, such as

o   Historical time horizons used for estimating the Return & Risk metrics.

o   Rebalancing frequency

o   Smoothing parameters wrt Rebalancing

     Session 3: Implementation of Black-Litterman model

*Rating: Intermediate

(Material presented will have technical elements requiring a working knowledge of the subject to make full use of the presentation.)  

The training will be complemented with a list of research, reference materials and R Scripts to provide the participants with a solid  understanding of Portfolio Analytics, DataManipulation and Data Visualization with the open source software R.
About the Instructor
Mr. Mark C. Hoogendijk, CFA, CAIA
Managing Director
E8 Consulting Asia
Mr. Mark C. Hoogendijk is an established derivatives and investment professional with more than 17 years’ experience. His expertise lies within Risk Analysis of Investment Portfolios, Asset & Liability Management, and Capital Management.
Over the 13 years here in Asia, he has worked closely with insurance companies, Pension Funds and Asset Managers in Hong Kong, Taiwan, Singapore, Malaysia and Australia understanding their needs, conducting risk analysis and finding viable and suitable hedging strategies for their existing investment portfolios.
The first 12 years of his career, Mark worked within the Financial Markets division of large financial institutions such as ABN AMRO, BNP Paribas and Credit Suisse. Since 2012, Mark has been working as an Investment & Risk Consultant with E8 Consulting Asia, focusing on Investment, Risk & Derivative analytics with open source software, such as R.
Mark has a Masters in Chemical Engineering from the University of Technology, Delft, Netherlands. He is a CFA & CAIA charterholder and a member of the Hong Kong Society of Financial Analysts.