Trade Execution Systems

In the securities markets there are different systems used to match the orders of different buyers and sellers. These are called the execution mechanisms in the market, and the markets are classified based on the execution system that they use.

There are three main types of markets: quote-driven markets, order-driven markets, and brokered markets. Some markets use a combination of these execution systems and are called hybrid markets.

Quote-driven Markets

Quotes-driven markets run through a network of dealers. The dealers quote prices and the traders have to buy and sell at those rates. These are also called over-the-counter (OTC) markets or dealers market. An example of a quote-driven market is the London Stock Exchange.

Order-driven Markets

An order-driven market does not require the intermediation of a dealer. Instead these markets work as auction markets, where the exchange runs an order matching system and trading rules are used to match the orders submitted by buyers and sellers. Most stock exchanges, futures exchanges and ECNs are organized as order-driven markets.

Brokered Markets

In a brokered market, the trades between the customers are arranged by brokers, such as in case of real estate transactions, and large block trades. The customers approach brokers to execute their orders. The brokers also actively initiate searches and suggest their clients’ different trades. The liquidity in the brokers market is primarily provided by the brokers. Such markets are however considered illiquid because of the time and cost involved in executing trades. Brokered markets are unorganized and are prevalent everywhere in the economy.

Hybrid Markets

Hybrid markets combine the features of two or more of the above market systems. An example is the New York Stock Exchange (NYSE). NYSE is largely an order-driven market, but requires dealers, like in a quotes-driven market, to provide liquidity when there is no one else. Another example is NASDAQ.

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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.