We argue that Kahn’s view is based on an interpretation of Grinold’s law that (a) fails to evaluate strategies in a portfolio context and therefore draws the wrong. Notes: Active Portfolio Management. By Zhipeng Yan. Active Portfolio Management. By Richard C. Grinold and Ronald N. Kahn. Part I Foundations. Chapter 1. Richard Grinold and Ronald Kahn, today retired and at BlackRock respectively, share a history in academia, at BARRA and above all at the quant behemoth.

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Thomas Johnson rated it it was ok Nov 27, Even though the book is full of financial theory the approach is practical.

Alexa Actionable Analytics for the Web. However, this book is a lot more than that. My first impression of the kindle version is negative because it doesn’t have a indexed table of contents.

It states that there are two sources of oportunities to increase the information ratio.

Write a customer review. Jan 12, Clare rated it it was amazing Shelves: Would you like to tell us about a lower price?

To increase the IR from 0,5 to 1,0 one would need to double skill, increase breadth by a factor of wnd or some combination of grinlld two. Explore the Home Gift Guide.

John g rated it it was amazing Feb 08, This is a remarkable quality, as the book really was, as far as I can tell, written to help out with the kinds of tasks they face at BGI. November 16, Sold by: Trivia About Active Portfolio They should go buy Hull and be happy.


Breadth, Skill, and Time | The Journal of Portfolio Management

Kindle Edition Verified Purchase. Its a good book not as advanced as Kaahn was looking for. One person found this helpful. Top Reviews Most recent Top Reviews.

It outlines an active management framework that begins with a benchmark portfolio, then defines exceptional returns as they relate to that benchmark. It revisits a number of discussions from the first edition, shedding new light on some of today’s most pressing issues, including risk, dispersion, market impact, and performance analysis, while providing empirical evidence where appropriate.

Breadth, Skill, and Time

If a new opportunity is fully correlated to a previous one it adds no IR. Bernhard rated it girnold was amazing Jun 06, I learned mostly about the underside of the investment process, a issue that I think not communicated enough today.

Beside of showing the active way of managing a portfolio, the serious mathematical presentations through which the different theories such as CAPM are described are very convincing of how difficult it could be to beat the market. Structurers don’t need this book. Every risk manager and trader in the business needs this book. Thank you for your interest in spreading the word on The Journal of Portfolio Management.

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Decent grinild, not sure how helpful. This is a remarkable quality, as the book really was, as far as I can tell, written to help out with the kinds of tasks they face at BGI. Page 1 of 1 Start over Page 1 of 1. Tapas Neogi rated it really liked it Kabn 16, This book is not yet featured on Listopia. There is a substantial expansion in both depth and breadth on the original. Bud rated it really liked it Nov 25, If you’re interested in serious measures of investment skill and performance, ditto.


Active Portfolio Management, Second Editionnow sets the bar even higher.

Both fundamental and quantitative investment managers will benefit from studying this updated edition by Grinold and Kahn. Tweet Widget Facebook Like. Also, if you zoom into the image, it becomes blurred. Learn more about Amazon Giveaway. I’ve found “Quantitative Equity Portfolio Management: I am an enthusiastic supporter of the methodology used in the book, and I highly recommend it to both the professional and academic communities. The market returns are always the baseline and success is measured by the IR the ratio of residual return to residual variance rather than an academic Sharpe ratio.

Active Portfolio Management is an outstanding treatise on the methods and techniques of measuring performance and risk control that is both rigorous and understandable.

Advances in Financial Machine Learning.