 Название: Quantitative Portfolio Management: with Applications in Python
Автор: Pierre Brugiere
Издательство: Springer
Год: 2020
Страниц: 212
Язык: английский
Формат: pdf (true), epub
Размер: 15.5 MB
This self-contained book presents the main techniques of quantitative portfolio management and associated statistical methods in a very didactic and structured way, in a minimum number of pages. The concepts of investment portfolios, self-financing portfolios and absence of arbitrage opportunities are extensively used and enable the translation of all the mathematical concepts in an easily interpretable way. All the results, tested with Python programs, are demonstrated rigorously, often using geometric approaches for optimization problems and intrinsic approaches for statistical methods, leading to unusually short and elegant proofs. The statistical methods concern both parametric and non-parametric estimators and, to estimate the factors of a model, principal component analysis is explained. The presented Python code and web scraping techniques also make it possible to test the presented concepts on market data.
The Python code included enables to extract financial data from the net and to put into practice the methods described. At the time of printing of this book everything was working on Python 3.6 with the standard libraries installed. Simply copy pasting the code into a Jupyter notebook should enable the reader to run all the examples provided. The programs can also be run in the Cloud with Google Colab and the Python 3.6 version it provides. Google Colab is free and can be accessed by creating a gmail address and opening the Google drive associated. The advantage of using Google Colab is that there is no requirement to install Python on one’s computer as all calculations are made in the cloud, with the calculation capacity provided by Google. This being said, Python and its libraries, as well as the way data are structured and offered by providers, are evolving quickly. So, inevitably some adjustments will have to be made in the future for the code. If some data extraction issues occur in the future, our advice is to first check if the data provider has changed the ticker of some particular stocks. Finally, the choice of the DAX index, for most examples, is motivated by the fact that it is one of the few major national indices which is calculated on a total return basis, but the reader can adjust the tickers in the program to analyse other indices.
This book will be useful for teaching Masters students and for professionals in asset management, and will be of interest to academics who want to explore a field in which they are not specialists. The ideal pre-requisites consist of undergraduate probability and statistics and a familiarity with linear algebra and matrix manipulation. Professionals will need to have a quantitative background, being either portfolio managers or risk managers, or potentially quants wanting to double check their understanding of the subject.
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Quantitative Portfolio Management: with Applications in Python 
