Name: Online Portfolio Selection: Principles and Algorithms (en Inglés)
Price: 64710.00 CLP
Availability: InStock
Author: Li, Bin ; Hoi, Steven Chu Hong
ISBN: 9781138894105

portada Online Portfolio Selection: Principles and Algorithms (en Inglés)

Formato

Libro Físico

Autor

Bin Li

Steven Chu Hong Hoi

Editorial

CRC Press

Año

2023

Idioma

Inglés

N° páginas

230

Encuadernación

Tapa Blanda

ISBN13

9781138894105

N° edición

Categorías

Finanzas
Probabilidad y estadística
Teoría matemática de la computación
Machine learning: aprendizaje automático

Online Portfolio Selection: Principles and Algorithms (en Inglés)

Bin Li (Autor) · Steven Chu Hong Hoi (Autor) · CRC Press · Tapa Blanda

Libro Físico

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Reseña del libro "Online Portfolio Selection: Principles and Algorithms (en Inglés)"

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(R) 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.