Finding Alphas: A Quantitative Approach to Building Trading Strategies
- 出版商: Wiley
- 出版日期: 2019-10-28
- 售價: $1,550
- 貴賓價: 9.5 折 $1,473
- 語言: 英文
- 頁數: 320
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1119571219
- ISBN-13: 9781119571216
Discover the ins and outs of designing predictive trading models
Drawing on the expertise of WorldQuant's global network, this new edition of Finding Alphas: A Quantitative Approach to Building Trading Strategies contains significant changes and updates to the original material, with new and updated data and examples.
Nine chapters have been added about alphas - models used to make predictions regarding the prices of financial instruments. The new chapters cover topics including alpha correlation, controlling biases, exchange-traded funds, event-driven investing, index alphas, intraday data in alpha research, intraday trading, machine learning, and the triple axis plan for identifying alphas.
- Provides more references to the academic literature
- Includes new, high-quality material
- Organizes content in a practical and easy-to-follow manner
- Adds new alpha examples with formulas and explanations
If you're looking for the latest information on building trading strategies from a quantitative approach, this book has you covered.
IGOR TULCHINSKY is the Founder, Chairman, and CEO of WorldQuant, a global quantitative asset management firm, based in Old Greenwich, Connecticut, that he established in 2007 following 12 years as a statistical arbitrage portfolio manager at Millennium Management. Before joining Millennium, Tulchinsky was a venture capitalist, scientist at AT&T Bell Laboratories, video game programmer, and author. He holds a master's degree in Computer Science from the University of Texas, Austin, completed in a then-record nine months, and an MBA in Finance and Entrepreneurship from the Wharton School at the University of Pennsylvania. A strong believer in education, Tulchinsky is the founder of WorldQuant University, which offers an entirely free online MSc degree in financial engineering and an applied data science module.