The Investment Advisor Body of Knowledge + Test Bank. IMCA

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СКАЧАТЬ Dealers (NASD) when its rulemaking and enforcement activities were consolidated with those of the enforcement division of the New York Stock Exchange in 2007.

      Thus, FINRA also monitors securities exchanges for suspicious activities and provides educational materials freely available to the public.

      State Agencies

      Each state has its own state security administrator responsible for regulating the securities industry in that state. The state security administrator also can register securities offered or sold in their state and oversee the firms and individuals selling securities or providing investment advice to their citizens.

      Although similar in scope to the SEC, investment advisors managing less than $100 million must register and file Form ADV with the state securities agency in their principal location.

      The National Securities Markets Improvement Act of 1996 amended the Securities Act of 1933 to exempt securities traded nationwide and registered with the SEC from registering with a state individually. Each state's securities law or “blue sky laws,” a name taken from a fraudulent rainmaking scheme against drought-stricken farmers, still provide the state with the authority to prosecute over securities fraud.

      The North American Securities Administrators Association (NASAA) is the membership organization for each state's securities administrator as well as for U.S. territories, Canada, and Mexico.

      CHAPTER 3

      Statistics and Methods

      Statistical concepts and calculations form an important foundation for understanding applied financial methods and formulae. Investment advisors and consultants should have a firm grasp on quantitative concepts in order to analyze historical data, calculate and analyze investment risk and returns, draw accurate conclusions, and make appropriate recommendations to clients. This chapter provides explanations of key elementary statistical concepts.

      Understanding and applying familiar concepts like compounding, discount factors, averages, measures of dispersion, and confidence intervals should come easily to investment advisors and consultants. More advanced statistical concepts and calculations however may take additional study to master. In practice, simply understanding the intuition of more advanced concepts and applications like multicollinearity and multivariate regression models is often sufficient. The readings in the chapter explain each concept from a mathematical framework and apply them to common financial and investment challenges to help one better understand and apply each tool in a more meaningful way.

      Part I Mathematics and Statistics for Financial Risk Management: Some Basic Math

      Learning Objectives

      ■ Describe the concept of compounding and compute compound returns.

      ■ Discuss the concept of limited liability.

      ■ Graph log returns and discuss how logarithms are useful for charting times series that grow exponentially.

      ■ Explain continuously compounded returns and convert the nominal rate into an annualized rate.

      ■ Discuss the significance of discount factors.

      ■ Describe the concept of an infinite series and determine the present value of a perpetuity that pays a stated coupon payment annually.

      ■ Describe the concept of a finite series and determine the present value of a newly issued bond that matures at a time certain and pays a stated coupon payment annually.

      Part II Mathematics and Statistics for Financial Risk Management: Probabilities

      Learning Objectives

      ■ Define and explain discrete random variables.

      ■ Define and explain continuous random variables.

      ■ Describe the concept of mutually exclusive events and calculate the probability that an investment will fall within a given range of returns.

      ■ Describe the concept of independent events and determine the probability that an investment will earn a return above a stated figure.

      ■ Explain probability matrices.

      Part III Mathematics and Statistics for Financial Risk Management: Basic Statistics

      Learning Objectives

      ■ Describe a population and sample data, and calculate the mean, median, and mode of a data set.

      ■ Describe discrete random variables and calculate the mean, median, and mode of a year-end portfolio value based on facts given, including default proba- bilities.

      ■ Describe continuous random variables.

      ■ Determine the price of a newly issued bond based on facts given including default probabilities.

      ■ Explain and calculate variance and standard deviation.

      ■ Describe and calculate variance and correlation.

      ■ Explain the concept of moments in statistical analysis.

      ■ Describe and illustrate skewness and kurtosis graphically.

      Part IV Mathematics and Statistics for Financial Risk Management: Distributions

      Learning Objectives

      ■ Describe a normal distribution and its implications for measuring returns and risk.

      ■ Explain the concept and application of Monte Carlo simulations for investment advisors and consultants.

      Part V Mathematics and Statistics for Financial Risk Management: Hypothesis Testing and Confidence Intervals

      Learning Objectives

      ■ Review and calculate sample mean.

      ■ Review and calculate sample variance.

      ■ Explain the concept of confidence intervals and discuss the terms: t-distribution,t-statistic, and t-stat.

      ■ Discuss hypothesis testing and confirm or reject a hypothesis based on various confidence levels.

      ■ Discuss and calculate value-at-risk (VaR) and illustrate VaR graphically.

      ■ Describe back-testing.

      ■ Explain expected shortfall.

      Part VI Mathematics and Statistics for Financial Risk Management: Linear Regression Analysis

      Learning Objectives

      ■ Describe linear regression and explain the formula (y = a + bx).

      ■ Define coefficient of determination (R-squared) and explain how it is used.

      ■ Explain the difference between univariate and multivariate regression models.

      ■ Describe multicollinearity and discuss how increasingly integrated global СКАЧАТЬ