Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage
Isichenko, Michael
- 出版商: Wiley
- 出版日期: 2021-08-31
- 售價: $1,980
- 貴賓價: 9.5 折 $1,881
- 語言: 英文
- 頁數: 304
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1119821320
- ISBN-13: 9781119821328
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相關主題
商品描述
Discover foundational and advanced techniques in quantitative equity trading from a veteran insider
In Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage, distinguished physicist-turned-quant Dr. Michael Isichenko delivers a systematic review of the quantitative trading of equities, or statistical arbitrage. The book teaches you how to source financial data, learn patterns of asset returns from historical data, generate and combine multiple forecasts, manage risk, build a stock portfolio optimized for risk and trading costs, and execute trades.
In this important book, you'll discover:
- Machine learning methods of forecasting stock returns in efficient financial markets
- How to combine multiple forecasts into a single model by using secondary machine learning, dimensionality reduction, and other methods
- Ways of avoiding the pitfalls of overfitting and the curse of dimensionality, including topics of active research such as "benign overfitting" in machine learning
- The theoretical and practical aspects of portfolio construction, including multi-factor risk models, multi-period trading costs, and optimal leverage
Perfect for investment professionals, like quantitative traders and portfolio managers, Quantitative Portfolio Management will also earn a place in the libraries of data scientists and students in a variety of statistical and quantitative disciplines. It is an indispensable guide for anyone who hopes to improve their understanding of how to apply data science, machine learning, and optimization to the stock market.
商品描述(中文翻譯)
發現來自資深內部人士的量化股票交易基礎與進階技術
在《Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage》中,傑出的物理學家轉型為量化專家的Dr. Michael Isichenko提供了對股票量化交易或統計套利的系統性回顧。本書教你如何獲取金融數據,從歷史數據中學習資產回報的模式,生成並結合多個預測,管理風險,建立針對風險和交易成本優化的股票投資組合,並執行交易。
在這本重要的書中,你將發現:
- 在高效金融市場中預測股票回報的機器學習方法
- 如何通過使用次級機器學習、降維和其他方法將多個預測結合成單一模型
- 避免過度擬合和維度詛咒的陷阱的方法,包括機器學習中如「良性過度擬合」等主題的活躍研究
- 投資組合建構的理論與實踐方面,包括多因子風險模型、多期間交易成本和最佳槓桿
《Quantitative Portfolio Management》非常適合投資專業人士,如量化交易員和投資組合經理,並將在各種統計和量化學科的數據科學家和學生的圖書館中佔有一席之地。這是一本對任何希望提高對如何將數據科學、機器學習和優化應用於股市的理解至關重要的指南。
作者簡介
MICHAEL ISICHENKO, PhD, is a theoretical physicist and a quantitative portfolio manager who worked at Kurchatov Institute, University of Texas, University of California, SAC Capital Advisors, Société Générale, and Jefferies. He received his doctorate in physics and mathematics from the Moscow Institute of Physics and Technology and is an expert in plasma physics, nonlinear dynamics, and statistical and chaos theory.
作者簡介(中文翻譯)
MICHAEL ISICHENKO, PhD,是一位理論物理學家及量化投資組合經理,曾在庫爾恰托夫研究所、德克薩斯大學、加州大學、SAC Capital Advisors、法國興業銀行及傑富瑞公司工作。他在莫斯科物理技術學院獲得物理與數學博士學位,並且是等離子體物理學、非線性動力學以及統計與混沌理論的專家。