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Online Portfolio Selection - Principles and Algorithms (Hardcover)
Loot Price: R4,581
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Online Portfolio Selection - Principles and Algorithms (Hardcover)
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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.
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