What is Private Placement of Shares?

Sometimes the companies issue their securities to a small select group of investors via a private placement. So, instead of opening the issue to the entire public through an IPO, the issue is sold directly to qualified investors using the services of an investment bank. For this reason, such an issue of securities is also called non-public offering.

These qualified investors are considered to have the money and the knowledge required to participate in the private placement. They also have a better understanding of the risk they are taking. These investors include institutional, venture capital and other sophisticated investors. A private placement memorandum (PPM) is prepared that provides details about the private placement and is used by the investors to make a sound investment decision.

The company has the advantage that it can get the money without going to the public. It also does not have to make as much public disclosure as would be required for a public offering. The costs of private placement in terms of preparing and implementing the placement are also lower. These investors will generally require a higher rate of return because these securities cannot be sold in the secondary market.

Private placements can be made for all kinds of financial securities issued by corporations such as common stock, preferred stock, warrants, promissory notes, and bonds.

In the US, even though a private placement is subject to the Securities Act of 1933, the securities do not have to be registered with SEC if they conform to certain rules. Private placements are governed by Securities and Exchange Commission (SEC) Regulation D, which provides rules for issuing securities without having to register those securities with the SEC.

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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.