Richard A. DeFusco CFA, McLeavey D.W., Runkle D.E. — Quantitative Methods For Investment Analysis
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Íàçâàíèå: Quantitative Methods For Investment Analysis
Àâòîðû: Richard A. DeFusco CFA, McLeavey D.W., Runkle D.E.
Àííîòàöèÿ:
As part of the CFA Institute Investment Series, the Second Edition of Quantitative Investment Analysis has been designed for a wide range of individuals, from graduate-level students focused on finance to practicing investment professionals. This globally relevant guide will help you understand quantitative methods and apply them to today's investment process.
In this latest edition, the distinguished team of Richard DeFusco, Dennis McLeavey, Jerald Pinto, and David Runkle update information associated with this discipline; improve the presentation and coverage of several major areas, including regression, time series, and multifactor models; and introduce an even greater variety of investment-oriented examples—which reflect the changes currently taking place in the investment community. Throughout the text, special attention is paid to ensuring the even treatment of subject matter, consistency of mathematical notation, and continuity of topic coverage that is so critical to the learning process.
Valuable for self-study and general reference, this book provides clear, example-driven coverage of a wide range of quantitative methods. Topics discussed include:
* The time value of money
* Discounted cash flow applications
* Common probability distributions
* Sampling and estimation
* Hypothesis testing
* Correlation and regression
* Multiple regression and issues in regression analysis
* Time-series analysis
* Portfolio concepts
And to further enhance your understanding of the tools and techniques presented here,don't forget to pick up the Quantitative Investment Analysis Workbook, Second Edition—an essential guide containing learning outcomes and summary overview sections along with challenging problems and solutions.
With each author bringing his own unique experiences and perspectives to the table, the Second Edition of Quantitative Investment Analysis distills the knowledge, skills, and abilities you need to succeed in today's fast-paced financial environment. Filled with in-depth insights and practical advice, Quantitative Investment Analysis, Second Edition offers a comprehensive treatment of quantitative methods that combines best practices with solid theory.
Sharpe ratio, negative142 Sharpe ratio, portfolio choice and620621622 Sharpe ratio, positive142 Sharpe ratio, safety-first ratio versus258 Sharpe ratio, Spearman rank correlation and358—359 Shortfall risk257259 Shorting stock184 Shrinkage estimators625 Simulation trial267 Skewed/skewness in return distribution144146—149 Spearman rank correlation357—360 Standard deviation, definitions129195 Standard deviation, equity market returns135 Standard deviation, interpreting139195 Standard deviation, using135 Standard error of estimate (SEE)401—403407 Standard error of sample mean292 Standardized beta definition643 standardizing255 Stationarity tests489—490 Statistical concepts and market returns87—177 Statistical concepts and market returns, fundamental concepts, some88—91 Statistical concepts and market returns, geometric and arithmetic means, using153—155 Statistical concepts and market returns, graphic presentation of data99—103 Statistical concepts and market returns, introduction88 Statistical concepts and market returns, kurtosis in return distribution149—153 Statistical concepts and market returns, measurement scales89—91 Statistical concepts and market returns, measures of central tendency103—120 Statistical concepts and market returns, measures of dispersion126—144 Statistical concepts and market returns, populations and samples89 Statistical concepts and market returns, quantiles as measure of locations120—126 Statistical concepts and market returns, summarizing data using frequency distributions91—99 Statistical concepts and market returns, summary155—157 Statistical concepts and market returns, symmetry and skewness in return distributions144—149 Statistical factor models634652 Statistical inference8889295325see Statistics, definitions8889286 Statistics, descriptive88—89 Statistics, inferential88 Statistics, nature of88—89 Statistics, sampling distribution of287292 Statistics, test327 Stratified random sampling288—289 Stress testing/scenario analysis260 Student's t-distribution table688 Summarizing data using frequency distributions91—99 Surprise definition634 Survey of Professional Forecasters (SPP)384 Survivorship bias in sample selection308—309310—311333 Symmetry and skewness in return distributions144—149 Systematic factors633 T-billsee "U.S. Treasury bill" t-test335—336339342347357406 Target semideviation136137 Target semivariance136137 Time value of money (TVM)1—55 Time value of money (TVM), examples6—89—10111416—171819—2020—2223242527—2829—3030—33 Time value of money (TVM), future value of series of cash flows13—15 Time value of money (TVM), future value of single cash flow4—13 Time value of money (TVM), interest rates interpretation2—3 Time value of money (TVM), introduction1—2 Time value of money (TVM), present value of series of cash flows18—26 Time value of money (TVM), present value of single cash flow15—18 Time value of money (TVM), solving for rates, number of periods, or size of annuity payments26—35 Time value of money (TVM), stationary529 Time value of money (TVM), summary36 Time-period bias in sample selection310311333 Time-series analysis515—585 Time-series analysis, autocorrelations529—530 Time-series analysis, autoregressive conditional heteroskedasticity models559—562568 Time-series analysis, autoregressive modelssee "Autoregressive (AR) time-series models" Time-series analysis, autoregressive moving-average models558—559 Time-series analysis, chain rule of forecasting533 Time-series analysis, challenges of working with517—518 Time-series analysis, cointegrated563564
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