Lecture 12 - Misbehavior, Crises, Regulation and Self Regulation

After talking about human failures and foibles in the last lecture, this lecture is concerned with regulation to minimize the impact of human errors. Professor Shiller outlines five different levels of regulation: Regulation on the firm level, on the level of trade groups, on the regional, the national, and the international level. Concerning the first level, he emphasizes the role of the board of directors as the regulators of a company, its duties of care and loyalty, and its responsibilities in the face of tunneling.

On the level of trade groups, Professor Shiller presents the history of the New York Stock Exchange from the signing of the Buttonwood Agreement until today. The subsequent description of regional regulation centers on Blue Sky laws during the progressive era of the U.S. in the late 19th and early 20th century. On the national level of regulation, he covers the founding days of the Securities and Exchange Commission, its regulation of hedge funds, as well as its efforts against the trading of insider information and stock price manipulation.

He complements his coverage of national regulation with the regulatory efforts in the aftermath of the financial crisis from 2007-2008, i.e. the creation of the Financial Stability Oversight Council and of the Consumer Financial Protection Bureau by the Dodd-Frank Act from 2010, paired with the European efforts in the course of the European Supervisory Framework, also from 2010. With respect to the fifth and final level of regulation - international regulation - Professor Shiller talks about the Basel Committee on Banking Supervisionand the G-20.

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.