What is NASDAQ and how it operates?

NASDAQ stands for National Association of Securities Dealers Automated Quotations. It was founded in 1971 to give dealers the ability to post their quotes electronically.

This is the entity that founded NASDAQ as a way to increase the trading in over-the-counter stocks which were not able to meet the requirements to get their stocks listed on larger exchanges such as the New York Stock Exchange (NYSE). These OTC stocks were previously traded over the phone and because information on the stock had to be obtained directly from a dealer who specialized in the stock it was difficult for the public to trade these stocks. The NASDAQ exchange was thus founded in 1971 giving dealers the ability to post their quotes electronically and therefore streamlining the process and opening up the stocks to a much larger audience. This was an instant success. In 1975 NASDAQ came up with its own listing requirements, which helped them separate the large companies from the smaller ones. This helped these companies to be able to compete with the other large companies that were listed on exchanges like the NYSE. Although NYSE is the largest exchange by market capitalization, it’s electronic quote mechanism made NASDAQ the largest exchange by trading volume.

The NASDAQ has no physical location, unlike the NYSE. It is a completely electronic market.

Apart from this, NASDAQ is different from NYSE in terms of how the market is quoted. While NYSE is an auction market, NASDAQ is the dealers market. In NYSE, the buyers and sellers trade directly with one another and a specialist facilitates the trade. On the other hand, on NASDAQ, the public buys and sells stocks with the help of someone known as the market maker. A market maker is the registered broker/dealer that provides both buy and sell quotes and is ready to take a position in those stocks. The primary difference between an NYSE specialist and a NASDAQ market maker is that the specialist’s job is to match the buyers and sellers in an orderly manner. The market maker on the other hand maintains his own inventory and trades on stocks in his own capacity, thereby creating the market. A market maker earns from the bid-offer spread that he quotes for the stocks that he trades in.

In a dealers market, the transaction costs are low because there are multiple market makers competing for your quote. On an average there are 14 market makers competing against one another to provide you the best pricing possible. This helps NASDAQ create the most efficient market place for traders.

A trading system called Small Order Execution System (SOES) existed along side NASDAQ to facilitate clearing trades of low volume. It has been phased out and is no longer necessary.

NASDAQ uses SuperMontage for all securities transactions. It is a fully integrated order entry and execution system.

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 includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $29 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

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.