Standard V (B) - Communication with Clients and Prospective Clients

This standard states that the members disclose their investment processes to clients and prospective clients. This includes information such as formats, investment principles, and changes in the processes, etc.

The members should reasonably identify the important factors affecting their investment processes and communicate them to their clients.

The members while presenting investment analysis to clients should distinguish between facts and opinions.

Examples of Violation

  • Example 1: An investment firm sends out a paid investment newsletter to high net worth individuals. In the newsletter it only includes information about the top buy and sell recommendations, but does not specify the process of investment valuation and basis of the recommendations. This is a violation of law, as the newsletter should at the minimum provide the basic process and logic behind the recommendations.
  • Example 2: An energy analyst provides a buy recommendation for an energy company. Based on indirect information, he made his own estimate of the energy generation capacity of the firm but in his report he stated it as a fact. This is a violation of the standard as he has stated his opinions as facts.
  • Example 3: An investment fund has been consistently doing well and invests only in dividend stocks. The investment manager decided to change its policy and starts including growth stocks in the fund. If the investment manager does not communicate this change in investment philosophy to his clients, he will be violating the standard. Another example of a similar violation would be if a fund whose processes are inclined towards active fund management decides to change the processes to focus on passive management strategies.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.