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Online Portfolio Selection

Name: Online Portfolio Selection
Brand: Apple Academic Press Inc.
SKU: 9781482249637
Price: 914.89 PLN
Availability: InStock

Autor: Bin Li, Steven Chu Hong Hoi

Język:

Oprawa: Twarda

Wydawca: Apple Academic Press Inc.

Dostępność: Dostępna u dostawcy

Wysyłamy za 14-21 dni

914.89 zł

With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio...

Informacje o książce

Autor

Bin Li, Steven Chu Hong Hoi

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2015

strony

212

EAN

9781482249637

ISBN

9781482249637

Enbook ID

02808830

Wydawca

Apple Academic Press Inc.

Waga

504

Wymiary

246 x 164 x 18

Pełny opis

With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment.

The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that:

Introduce OLPS and formulate OLPS as a sequential decision task
Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning
Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques
Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art
Investigate possible future directions

Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB^® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment.

Readers are encouraged to visit the authors website for updates: http://olps.stevenhoi.org.

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