Standard II (B) - Market Manipulation

As per this standard, the Members and Candidates must not engage in practices that distort prices or artificially inflate trading volume with the intent to mislead market participants.

The market manipulation could be information-based or transaction-based.

Information-based manipulation refers to the situation where an analyst may spread false/misleading information that could propel trading. For example, an analyst may provide an exaggerated positive recommendation for a stock, which may lead to a hike in its price. The intention behind this could be to sell the stocks when the prices have jumped high.

Transaction-based manipulation could occur when a CFA member/candidate’s activities could artificially impact the price/volume of an asset to draw public’s attention. For example, an institutional investor with multiple accounts could trade in a stock to increase the trading volume significantly. Another transaction-based manipulation could be where the manipulator takes a dominant, controlling position in an asset so that he could exploit a derivative product.

Both information-based and transaction-based manipulations are a violation of Standard II (B).

Examples:

The following statements are a violation of Standard II (B), Market Manipulation

  1. Overstating the earnings projection to help increase stock price.
  2. Securing a controlling interest in an equity security in order to influence the price of a related derivative instrument.
  3. Spreading false rumour about a firm to encourage trading by others
  4. Disseminating misleading information about the development of new products and technologies.
  5. Buying and selling a large number of shares from a friend on exchange floor at a price 10% above last trade.

The following statements are not a violation of Standard II (B).

  1. Implementing a trading strategy to exploit differences in market power and information.
  2. Selling a security and immediately purchasing a similar security in order to minimize income tax liability.
  3. Engaging in order splitting to limit the effect on the price of a thinly traded security.
  4. Exploiting differences in market inefficiencies.

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Data Science in Finance: 9-Book Bundle

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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.