Dynamic Portfolio Strategies: quantitative methods and empirical rules for incomplete information

Dynamic Portfolio Strategies: quantitative methods and empirical rules for incomplete information
Author :
Publisher : Springer Science & Business Media
Total Pages : 213
Release :
ISBN-10 : 9781461509219
ISBN-13 : 1461509211
Rating : 4/5 (211 Downloads)

Book Synopsis Dynamic Portfolio Strategies: quantitative methods and empirical rules for incomplete information by : Nikolai Dokuchaev

Download or read book Dynamic Portfolio Strategies: quantitative methods and empirical rules for incomplete information written by Nikolai Dokuchaev and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic Portfolio Strategies: Quantitative Methods and Empirical Rules for Incomplete Information investigates optimal investment problems for stochastic financial market models. It is addressed to academics and students who are interested in the mathematics of finance, stochastic processes, and optimal control, and also to practitioners in risk management and quantitative analysis who are interested in new strategies and methods of stochastic analysis. While there are many works devoted to the solution of optimal investment problems for various models, the focus of this book is on analytical strategies based on "technical analysis" which are model-free. The technical analysis of these strategies has a number of characteristics. Two of the more important characteristics are: (1) they require only historical data, and (2) typically they are more widely used by traders than analysis based on stochastic models. Hence it is the objective of this book to reduce the gap between model-free strategies and strategies that are "optimal" for stochastic models. We hope that researchers, students and practitioners will be interested in some of the new empirically based methods of "technical analysis" strategies suggested in this book and evaluated via stochastic market models.


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