Lecture 21 - Exchanges, Brokers, Dealers, Clearinghouses

As the starting point for this lecture, Professor Shiller contrasts the view of economics as the theory of the allocation of scarce resources with the view of economics as the study of exchange. After a discussion of the difference between brokers and dealers, he outlines the history of securities exchanges from ancient Rome, to the Amsterdam Stock Exchange and Jonathan's Coffee House in London, until the formation of the New York Stock Exchange.

He complements this historic account with an overview of securities exchanges all over the world, covering India, China, Brazil, and Mexico. An example of a limit order book allows him to elaborate on the mechanics of trading at the National Association of Securities Dealers Automatic Quotation System (NASDAQ). Subsequently, he turns his attention to the growing importance of program trading and high frequency trading, but also discusses their impact on the stock market crash from October 19, 1987, as well as on the Flash Crash from May 6, 2010.

When talking about fairness in financial markets, particularly with regard to the relation between private investors and brokers, he discusses the National Market System (NMS), the Intermarket Trading System (ITS), and consolidated quotation systems. He concludes this lecture with some reflections on the operations of dealers, addressing the role of inside information and the Gambler's Ruin problem.

1. Exchange as the Key Component of Economic Activity
2. Brokers vs. Dealers
3. History of Stock Exchanges around the World
4. Market Orders, Limit Orders, and Stop Orders
5. The Growing Importance of Electronic Trading
6. Instabilities Related to High Frequency Trading
7. The Frustrations as Trading as a Dealer

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.