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.
Table of Contents:
Preface xi
Preface (to the Original Edition) xiii
Acknowledgments xv
About the WebSim Website xvii
Part I Introduction 1
1 Introduction to Alpha Design 3
By Igor Tulchinsky
2 Perspectives on Alpha Research 7
By Geoffrey Lauprete
3 Cutting Losses 17
By Igor Tulchinsky
Part II Design and Evaluation 23
4 Alpha Design 25
By Scott Bender and Yongfeng He
5 How to Develop an Alpha: A Case Study 31
By Pankaj Bakliwal and Hongzhi Chen
6 Data and Alpha Design 43
By Weijia Li
7 Turnover 49
By Pratik Patel
8 Alpha Correlation 61
By Chinh Dang and Crispin Bui
9 Backtest – Signal or Overfitting? 69
By Zhuangxi Fang and Peng Yan
10 Controlling Biases 77
By Anand Iyer and Aditya Prakash
11 The Triple-Axis Plan 83
By Nitish Maini
12 Techniques for Improving the Robustness of Alphas 89
By Michael Kozlov
13 Alpha and Risk Factors 95
By Peng Wan
14 Risk and Drawdowns 101
By Hammad Khan and Rebecca Lehman
15 Alphas from Automated Search 111
By Yu Huang and Varat Intaraprasonk
16 Machine Learning in Alpha Research 121
By Michael Kozlov
17 Thinking in Algorithms 127
By Sunny Mahajan
Part III Extended Topics 133
18 Equity Price and Volume 135
By Cong Li and Huaiyu Zhou
19 Financial Statement Analysis 141
By Paul A. Griffin and Sunny Mahajan
20 Fundamental Analysis and Alpha Research 149
By Xinye Tang and Kailin Qi
21 Introduction to Momentum Alphas 155
By Zhiyu Ma, Arpit Agarwal, and Laszlo Borda
22 The Impact of News and Social Media on Stock Returns 159
By Wancheng Zhang
23 Stock Returns Information from the Stock Options Market 169
By Swastik Tiwari and Hardik Agarwal
24 Institutional Research 101: Analyst Reports 179
By Benjamin Ee, Hardik Agarwal, Shubham Goyal, Abhishek Panigrahy, and Anant Pushkar
25 Event-Driven Investing 195
By Prateek Srivastava
26 Intraday Data in Alpha Research 207
By Dusan Timotity
27 Intraday Trading 217
By Rohit Kumar Jha
28 Finding an Index Alpha 223
By Glenn DeSouza
29 ETFs and Alpha Research 231
By Mark YikChun Chan
30 Finding Alphas on Futures and Forwards 241
By Rohit Agarwal, Rebecca Lehman, and Richard Williams
Part IV New Horizon – Websim 251
31 Introduction to WebSim 253
By Jeffrey Scott
Part V A Final Word 263
32 The Seven Habits of Highly Successful Quants 265
By Richard Hu and Chalee Asavathiratham
References 273
Index 291
About the Author :
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.
