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Quantitative Equity Portfolio Management
Quantitative equity portfolio management combines theories and advanced techniques from several disciplines, including financial economics, accounting, mathematics, and operational research. While many texts are devoted to these disciplines, few deal with quantitative equity investing in a systematic and mathematical framework that is suitable for quantitative investment students. Providing a solid foundation in the subject, Quantitative Equity Portfolio Management: Modern Techniques and Applications presents a self-contained overview and a detailed mathematical treatment of various topics.
From the theoretical basis of behavior finance to recently developed techniques, the authors review quantitative investment strategies and factors that are commonly used in practice, including value, momentum, and quality, accompanied by their academic origins. They present advanced techniques and applications in return forecasting models, risk management, portfolio construction, and portfolio implementation that include examples such as optimal multi-factor models, contextual and nonlinear models, factor timing techniques, portfolio turnover control, Monte Carlo valuation of firm values, and optimal trading. In many cases, the text frames related problems in mathematical terms and illustrates the mathematical concepts and solutions with numerical and empirical examples.
Ideal for students in computational and quantitative finance programs, Quantitative Equity Portfolio Management serves as a guide to combat many common modeling issues and provides a rich understanding of portfolio management using mathematical analysis.
From the theoretical basis of behavior finance to recently developed techniques, the authors review quantitative investment strategies and factors that are commonly used in practice, including value, momentum, and quality, accompanied by their academic origins. They present advanced techniques and applications in return forecasting models, risk management, portfolio construction, and portfolio implementation that include examples such as optimal multi-factor models, contextual and nonlinear models, factor timing techniques, portfolio turnover control, Monte Carlo valuation of firm values, and optimal trading. In many cases, the text frames related problems in mathematical terms and illustrates the mathematical concepts and solutions with numerical and empirical examples.
Ideal for students in computational and quantitative finance programs, Quantitative Equity Portfolio Management serves as a guide to combat many common modeling issues and provides a rich understanding of portfolio management using mathematical analysis.
457 pages, Hardcover
First published May 11, 2007
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February 12, 2020
overall pretty solid overview of longer-term, factor-based portfolio management. even though firms can differ in what they consider alpha and risk, useful background info. interesting chapters:
ch2: CAPM + assumptions, mean-variance optimization under CAPM and for beta-neutral
ch3: what risk in CAPM represents, like contribution to risk through taylor expansion and loss contribution. they're approximately equal under certain conditions.
ch4: information ratio, information coefficient, estimating IC from raw returns vs. estimating IC from risk-adjusted returns, alpha for a factor is a function of IC/return dispersion/target risk. return dispersion is approximately 1 when returns risk-adjusted and normal. multiperiod IR can be estimated through expected alpha/expected std of alpha, which turns into IC/std(IC). IR is only ICsqrt(N) when the std of IC is 1/sqrt(N) aka purely sampling error and no correlations. in reality IC has strategy error as well, resulting in larger realized risks and worse IR.
ch6: can try parameterizing DCFs with profitability/scalability (d(operating income after tax)/d(sales) and d(sales)/d(net operating assets)) that give you return on incremental capital, compared vs. WACC. cool simulation potential with these parameters but dunno if reasonable.
ch7: composite IC (linear combo of included ICs with weights prop to factor dispersion/composite factor dispersion), alpha for composite factors (prop to composite IC), both of which can use standardized factors for simplicity. also a formula for composite IR. optimal model weights are prop to inverse IC correlations and IC values for factors. discussion of factor correlation vs. IC correlation theoretically (broadly, factor correlation should bound IC correlation but all of it goes out the window empirically lol). also if you orthogonalize + standardize your factors, things become easier.
ch8: turnover is prop to leverage and risk, and also depends on forecast autocorrelation. note that you can decrease forecast autocorrelations with a moving average. this trades alpha for less turnover. also you can decrease turnover by ignoring rebalances that are less than a certain size, but loses alpha.
ch11: unconstrained l/s is best as intuition suggests. restrictions can occur for a variety of structural/regulatory reasons and they all reduce alpha.
ch12: lots of transaction cost stuff but not clear how applicable the cost functions are.
ch2: CAPM + assumptions, mean-variance optimization under CAPM and for beta-neutral
ch3: what risk in CAPM represents, like contribution to risk through taylor expansion and loss contribution. they're approximately equal under certain conditions.
ch4: information ratio, information coefficient, estimating IC from raw returns vs. estimating IC from risk-adjusted returns, alpha for a factor is a function of IC/return dispersion/target risk. return dispersion is approximately 1 when returns risk-adjusted and normal. multiperiod IR can be estimated through expected alpha/expected std of alpha, which turns into IC/std(IC). IR is only ICsqrt(N) when the std of IC is 1/sqrt(N) aka purely sampling error and no correlations. in reality IC has strategy error as well, resulting in larger realized risks and worse IR.
ch6: can try parameterizing DCFs with profitability/scalability (d(operating income after tax)/d(sales) and d(sales)/d(net operating assets)) that give you return on incremental capital, compared vs. WACC. cool simulation potential with these parameters but dunno if reasonable.
ch7: composite IC (linear combo of included ICs with weights prop to factor dispersion/composite factor dispersion), alpha for composite factors (prop to composite IC), both of which can use standardized factors for simplicity. also a formula for composite IR. optimal model weights are prop to inverse IC correlations and IC values for factors. discussion of factor correlation vs. IC correlation theoretically (broadly, factor correlation should bound IC correlation but all of it goes out the window empirically lol). also if you orthogonalize + standardize your factors, things become easier.
ch8: turnover is prop to leverage and risk, and also depends on forecast autocorrelation. note that you can decrease forecast autocorrelations with a moving average. this trades alpha for less turnover. also you can decrease turnover by ignoring rebalances that are less than a certain size, but loses alpha.
ch11: unconstrained l/s is best as intuition suggests. restrictions can occur for a variety of structural/regulatory reasons and they all reduce alpha.
ch12: lots of transaction cost stuff but not clear how applicable the cost functions are.
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