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.
Kurtosis, calculating sample excess151—153 Kurtosis, definition149 Kurtosis, excess150 Kurtosis, normal distribution and252 Kurtosis, sample excess formula150—151 Leptokurtic149150151 Likelihoods212 Linear association377379381 Linear interpolation121 Linear regression395—418 Linear regression with one independent variable395—398 Linear regression, assumptions of395398—401 Linear regression, coefficient of determination403—405 Linear regression, confidence intervals and405406 Linear regression, cross-sectional data395—396418 Linear regression, data types395 Linear regression, diagram of how it works397 Linear regression, estimated or fitted parameters396 Linear regression, examples400—401402—403404—405407—412414—415417—418 Linear regression, hypothesis testing405—412 Linear regression, limitations48 Linear regression, model assumptions398—401 Linear regression, parameter instability418 Linear regression, prediction intervals416—418 Linear regression, problems and solutions476 Linear regression, standard error of estimate401—403 Linear regression, time seriessee "Time-series analysis" Linear regression, variance analysis with one independent variable413—415 Linear trend models518—521529 Liquidity premium3 Log-linear trend models521—526527529 Logit models490 Lognormal distribution260—266 Lognormal distribution as continuous model245 Lognormal distribution, asymmetric or skewed240 Lognormal distribution, Black — Scholes — Merton option pricing model and260 Lognormal distribution, expressions for mean and variance of261 Lognormal distribution, observations about260 Lognormal distribution, parameters261 Longitudinal data291 Look-ahead bias in sample selection309311 Macroeconomic factor models in current practice644—649 Macroeconomic factor models, definition633 Macroeconomic factor models, estimating636 Macroeconomic factor models, examples644—649649—651 Macroeconomic factor models, expected return example in644—645 Macroeconomic factor models, structure of634—636 Macroeconomic factor models, surprise in634—635 Market model, computing stock correlations using627 Market model, estimates625—629 Market model, regression209 Market model, returns and633 Market price premium618 Markowitz decision rule619—620 Maturity premium3 Mean absolute deviation (MAD)127—129 Mean absolute deviation (MAD), drawback127 Mean absolute deviation (MAD), evaluating risk example128—129 Mean absolute deviation (MAD), formula127 Mean excess return141 Mean reversion and time series532—533 Mean squared error (MSE)469 Mean versus average usage104 Mean-variance analysis141589—623 Mean-variance analysis, asset allocation determination and622—623 Mean-variance analysis, assumptions of589—590 Mean-variance analysis, capital asset pricing modelsee "Capital asset pricing model (CAPM)" Mean-variance analysis, definition589 Mean-variance analysis, determining minimum-variance frontier602—605 Mean-variance analysis, diversification and portfolio size605—609 Mean-variance analysis, estimating inputs for optimization of624—629 Mean-variance analysis, examples595—597603—605608—609610—614616—617621—622627630—632 Mean-variance analysis, extension to three-asset case599—602 Mean-variance analysis, historical estimates for624—625 Mean-variance analysis, instability in minimum variance frontier629—632 Mean-variance analysis, market model estimates: adjusted beta628629 Mean-variance analysis, market model estimates: historical beta625—628 Mean-variance analysis, Markowitz decision rule619—620 Mean-variance analysis, minimum-variance frontier590—599 Mean-variance analysis, normal distribution and257 Mean-variance analysis, portfolio choice rules619—623 Mean-variance analysis, portfolio choice with risk-free asset609—617 Mean-variance analysis, practical issues in623—632 Mean-variance analysis, summary668—670 Measurement scales89—91 Measurement scales, identifying90—91 Measurement scales, interval90 Measurement scales, nominal89 Measurement scales, ordinal90 Measurement scales, ratio90 Measures of location103 Median and central tendency108—110 Median, advantage and disadvantage of108 Median, definition108 Median, example for finding108—109 Median, example of usage of109—110 Mesokurtic149150 Mixed factor models634 Modal interval(s)111 Mode of central tendency110—112 Mode, calculating111—112 Mode, definition110 Mode, number of110 Model specification, regression476—490 Model specification, regression, examples478—479480—483483—485486—487488—489 Model specification, regression, misspecified functional form478—486 Model specification, regression, pooling data and485—486 Model specification, regression, principles477 Model specification, regression, regressors correlated with errors486—489 Model specification, regression, time-series misspecification486—490 Modern Portfolio Theory (MPT)257 Money market yields72—77 Money-weighted rate of return66—67 Monte Carlo simulation232266—272 Monte Carlo simulation, application examples269—271 Monte Carlo simulation, asset allocation and623 Monte Carlo simulation, Black — Scholes — Merton option pricing model versus272 Monte Carlo simulation, central limit theorem and293—294 Monte Carlo simulation, characteristic feature of266 Monte Carlo simulation, historical simulation versus271—272 Monte Carlo simulation, probability distributions for risk factors and339 Monte Carlo simulation, steps in267—268 Monte Carlo simulation, uses266—267 Moving-average time-series models548—553567 Moving-average time-series models, autoregressive558—559 Moving-average time-series models, autoregressive time series versus551—553 Moving-average time-series models, forecasting551—553 Moving-average time-series models, n-period, smoothing past values with549—551 Moving-average time-series models, simple551 Moving-average time-series models, weakness of551 Multicollinearity454473—476 Multicollinearity, consequences of473 Multicollinearity, correcting for476 Multicollinearity, detecting473—476 Multicollinearity, example474—475 Multicollinearity, occurrence of473 Multicollinearity, summary476 Multifactor models in portfolio management633—667 Multifactor models in portfolio management in current practice644—652 Multifactor models in portfolio management, active management633 Multifactor models in portfolio management, analyzing sources of returns652—655 Multifactor models in portfolio management, analyzing sources of risk655—663 Multifactor models in portfolio management, application examples653—655656658—659660—663663—664665—666 Multifactor models in portfolio management, applications652—666 Multifactor models in portfolio management, arbitrage pricing theorysee "Arbitrage pricing theory" Multifactor models in portfolio management, concluding remarks about666—667 Multifactor models in portfolio management, creating tracking portfolio664—666 Multifactor models in portfolio management, fundamental factor modelssee "Fundamental factor models" Multifactor models in portfolio management, importance of633 Multifactor models in portfolio management, macroeconomic factor modelssee "Macroeconomic factor models"
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