Research Objectivity Standards (ROS)

  • The Research Objectivity Standards are a set of guiding principles developed by CFAI.
  • The ROS are not laws, but a voluntary code of conduct.
  • Note: The legal requirements of some jurisdictions may overlap with the intent of an ROS standard.
  • The ROS are aligned with the CFAI’s professional standards of conduct.  Unlike the Soft Dollar Standards, a firm would not publicly claim compliance to ROS.  However, at the member/candidate level, by violating an ROS standard, the individual may also be violating mandatory professional standard of conduct as well.
  • Requirements.  Each standard is followed by one requirement, not necessarily the only one.  CFAI makes further recommendations for compliance.
  • Research objectivity policy – must: be written and sent to all employees, contain supervisory procedures, and owned by a senior firm officer.
  • Public appearances – firm speaker must disclose any personal or firm conflicts of interest to the audience.
  • Reasonable and adequate basis – a supervisory analyst or committee must exist in the firm to review and approve all investment recommendations.
  • Firewall – firms must ensure that activities of the investment bank unit do not influence research or recommendations in the investment management unit.
  • Analyst compensation – must be aligned with research accuracy over a period of time; cannot be tied to any investment banking activity.
  • Subject companies – investment firm cannot promise a company with a specific investment recommendation or stock price target.
  • Personal investments and trading – firms must prevent employees from trading in ahead of clients.
  • Timeliness of recommendations – must be issued regularly; quarterly is the guidance, with updates as needed in response to significant events.
  • Compliance and enforcement – firms must keep records of their internal audit activities.
  • Disclosure – firms must disclose all conflicts of interest that affect the firm and covered employees.
  • Rating system – firms must establish a rating system that investors find useful in making decisions.

Data Science in Finance: 9-Book Bundle

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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.