Online Portfolio Selection: Principles and Algorithms

Bin Li, Steven Chu Hong Hoi

Online Portfolio Selection: Principles and Algorithms
  • 出版商: CRC
  • 出版日期: 2024-01-31
  • 售價: $2,490
  • 貴賓價: 9.5 折 $2,366
  • 語言: 英文
  • 頁數: 232
  • 裝訂: Paperback
  • ISBN: 1138894109
  • ISBN-13: 9781138894105
  • 相關分類: Algorithms-data-structures

    • 海外代購書籍(需單獨結帳)

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商品描述

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:

 

 

  1. Introduce OLPS and formulate OLPS as a sequential decision task
  2. Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning
  3. Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques
  4. Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art
  5. 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.

商品描述(中文翻譯)

以逐步確定一組資產的最佳配置為目標，線上投資組合選擇（OLPS）已顯著改變了金融投資領域。《線上投資組合選擇：原則與演算法》提供了對現有OLPS原則的全面調查，並提出了一系列利用機器學習技術進行金融投資的創新策略。

該書介紹了四種基於機器學習技術的新演算法，這些演算法是由作者設計的，並提供了一個新的回測系統，用於評估交易策略的有效性。該書使用真實市場數據的模擬來展示交易策略的實際應用，並為讀者提供自行部署這些策略的信心。該書分為五個部分：

1. 介紹OLPS並將其定義為一個序列決策任務
2. 提出關鍵的OLPS原則，包括基準、跟隨贏家、跟隨輸家、模式匹配和元學習
3. 詳細介紹四種基於尖端機器學習技術的創新OLPS演算法
4. 提供評估OLPS演算法的工具箱，並進行與最新技術的實證研究比較
5. 探討可能的未來發展方向

該書配有一個使用歷史數據評估交易策略表現的回測系統，以及用於回測系統的MATLAB®代碼，是金融、計算機科學和統計學研究生的理想資源。同時也適合對計算投資感興趣的研究人員和工程師。

鼓勵讀者訪問作者的網站以獲取更新信息：http://olps.stevenhoi.org。

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