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From the leading authorities in their field, Richard C. Grinold and Ronald N. Kahn reveal the newest, most effective tools for avoiding common pitfalls while maximizing profits through active portfolio management.

The quantitative approach to active management has developed over the past 65 years. In , Markowitz framed investing as a trade-off between expected return and risk—which he defined as the variance of return opening portfolio management to quantitative analysis. Subsequent developments like the capital asset pricing model and the efficient market hypothesis quickly focused academics on indexing: passive investing.

But, starting in the s, a number of separate efforts created a quantitative framework for active investing. In , and with a second edition in , our book, Active Portfolio Management, brought all these threads together into a coherent theory of active management. We described how to develop forecasts of risk, return, and cost and how to combine those elements into active portfolios.

We presented the information ratio—the ratio of active return to active risk—as the key statistic for active management. All investors seek active managers with high information ratios. The book was very popular when first published, and remains popular to this day, because it is the recognized authority on quantitative active management.

We have not stood still since The theory and framework of Active Portfolio Management were state-of-the-art when written, but we have advanced the state of the art since then, in particular in viewing active management as a dynamic problem rather than a one-period challenge, and also in advancing portfolio analysis to provide new perspectives. The world of active management has also not stood still since Issues, trends, and challenges have appeared since then, and we have published many articles on these emerging trends in refereed journals.

Those articles applied the Active Portfolio Management framework to analyze new problems. Articles and essays we have written—together and separately, mainly since the publication of Active Portfolio Management —compose the chapters in this book. We have chosen articles mainly along three dimensions: a recap of Active Portfolio Management , advancing the Active Portfolio Management framework, and applying the Active Portfolio Management framework to newer problems.

For each article or group of related articles, we have added an introduction reviewing the material with the benefit of time and providing context and background useful for reading the articles. One emerging theme that cuts across these articles is optimal portfolio design. This includes topics ranging from the trade-off between information turnover and trading costs, identifying where the active opportunities are greatest, the desirability of separating smart beta from pure alpha, how we must coordinate risk level and gearing for optimal implementation, how we can overdiversify multistrategy funds with surprisingly negative consequences in bad market states, and insights into structuring fees.

Another theme that cuts across the dimensions of this book and connects it to our prior book is the track record of quantitative investing. This was a fairly small endeavor in Today, quantitative investing is a substantial activity, especially if we include indexing—non-active quantitative investing.

On quantitative active management, our books and articles have helped democratize the framework and approach and helped inform many new entrants into the field, sometimes with negative consequences as in when too much money chased very similar ideas. Beyond measuring quantitative equity assets, another track record metric is the general level of interest in the field as measured by organizations, conferences, and journals now devoted to quantitative investing.

By this very different measure, interest in the field appears healthy and robust. The principles of quantitative active management are centered on the analysis of return, risk, and cost. These principles were developed and described in Active Portfolio Management. In Advances in Active Portfolio Management , we have restated those principles in a more contemporary idiom and presented significant advances in the area of product design, portfolio analytics, and dynamic portfolio management.

The framework continues to evolve. We now have access to vast new sources of data. The age of big data has arrived.

This profusion of data is accompanied by—indeed made possible by—dramatic increases in computing power. Machine learning and other advanced techniques are available. The quantitative active manager who can use these powerful tools wisely will prevail in the difficult challenge of active portfolio management.

Richard C. They are long-time active managers at Barclays Global Investors, BlackRock, and Vinva, well-known experts on quantitative investing, and the winners of the James R. Vertin Award, presented periodically by the CFA Institute to recognize individuals who have produced a body of research notable for its relevance and enduring value to investment professionals.

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Advances in Active Portfolio Management




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