Navigating the Factor Zoo: The Science of Quantitative Investing
暫譯: 穿越因子動物園：量化投資的科學
Zhang, Michael, Lu, Tao, Shi, Chuan
商品描述
Bridging the gap between theoretical asset pricing and industry practices in factors and factor investing, Zhang et al. provides a comprehensive treatment of factors, along with industry insights on practical factor development.
Chapters cover a wide array of topics, including the foundations of quantamentals, the intricacies of market beta, the significance of statistical moments, the principles of technical analysis, and the impact of market microstructure and liquidity on trading. Furthermore, it delves into the complexities of tail risk and behavioral finance, revealing how psychological factors affect market dynamics. The discussion extends to the sophisticated use of option trading data for predictive insights and the critical differentiation between outcome uncertainty and distribution uncertainty in financial decision-making. A standout feature of the book is its examination of machine learning's role in factor investing, detailing how it transforms data preprocessing, factor discovery, and model construction. Overall, this book provides a holistic view of contemporary financial markets, highlighting the challenges and opportunities in harnessing alternative data and machine learning to develop robust investment strategies.
This book would appeal to investment management professionals and trainees. It will also be of use to graduate and upper undergraduate students in quantitative finance, factor investing, asset management and/or trading.
商品描述(中文翻譯)
橋接理論資產定價與行業實踐之間的差距，Zhang 等人提供了對因子及因子投資的全面探討，並附上行業對實際因子開發的見解。
各章節涵蓋廣泛的主題，包括量化基本面（quantamentals）的基礎、市場貝塔（market beta）的複雜性、統計時刻（statistical moments）的重要性、技術分析的原則，以及市場微結構（market microstructure）和流動性（liquidity）對交易的影響。此外，書中深入探討尾部風險（tail risk）和行為金融（behavioral finance）的複雜性，揭示心理因素如何影響市場動態。討論還延伸至選擇權交易數據的複雜應用，以獲取預測見解，以及在金融決策中結果不確定性（outcome uncertainty）與分佈不確定性（distribution uncertainty）之間的關鍵區別。本書的一個突出特點是對機器學習（machine learning）在因子投資中的角色進行的檢視，詳細說明其如何改變數據預處理、因子發現和模型構建。總體而言，本書提供了當代金融市場的整體觀點，突顯了利用替代數據和機器學習開發穩健投資策略所面臨的挑戰和機遇。
本書將吸引投資管理專業人士和實習生，對於研究生和高年級本科生在量化金融、因子投資、資產管理和/或交易方面也將有所幫助。
作者簡介
Michael Zhang is the founder of Super Quantum Fund. He has over 20 years of experience in quantitative investing. He publishes in the most prestigious academic journals and has been highly cited. He holds a PhD from MIT, an MSc, and two bachelor's degrees from Tsinghua University.
Tao Lu is the CEO of Super Quantum Fund. He has extensive practical experience in portfolio management through quantitative methods and leading quantitative research teams. He holds a PhD from the Chinese University of Hong Kong and two bachelor's degrees from Tsinghua University.
Chuan Shi is the chief data scientist at Beijing Liangxin Investment Management, specializing in factor investing, portfolio allocation, and risk management. He holds a PhD from MIT and bachelor's and master's degrees from Tsinghua University. He is the lead author of "Factor Investing: Methodology and Practice."
作者簡介(中文翻譯)
Michael Zhang 是超量子基金的創辦人。他在量化投資方面擁有超過 20 年的經驗。他的研究發表在最具聲望的學術期刊上，並且被廣泛引用。他擁有麻省理工學院的博士學位、碩士學位以及清華大學的兩個學士學位。
Tao Lu 是超量子基金的執行長。他在透過量化方法進行投資組合管理及領導量化研究團隊方面擁有豐富的實務經驗。他擁有香港中文大學的博士學位以及清華大學的兩個學士學位。
Chuan Shi 是北京量心投資管理的首席數據科學家，專注於因子投資、投資組合配置和風險管理。他擁有麻省理工學院的博士學位以及清華大學的學士和碩士學位。他是《因子投資：方法論與實踐》的主要作者。
