{"product_id":"finding-alphas-isbn-9781119571216","title":"Finding Alphas","description":"\u003cp\u003e\u003cb\u003eDiscover the ins and outs of designing predictive trading models\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDrawing on the expertise of WorldQuant’s global network, this new edition of \u003ci\u003eFinding Alphas: A Quantitative Approach to Building Trading Strategies\u003c\/i\u003e contains significant changes and updates to the original material, with new and updated data and examples.\u003c\/p\u003e \u003cp\u003eNine 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.\u003c\/p\u003e \u003cp\u003e•    Provides more references to the academic literature\u003c\/p\u003e \u003cp\u003e•    Includes new, high-quality material\u003c\/p\u003e \u003cp\u003e•    Organizes content in a practical and easy-to-follow manner\u003c\/p\u003e \u003cp\u003e•    Adds new alpha examples with formulas and explanations\u003c\/p\u003e \u003cp\u003eIf you’re looking for the latest information on building trading strategies from a quantitative approach, this book has you covered. \u003c\/p\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003ePreface (to the Original Edition) xiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xv\u003c\/p\u003e \u003cp\u003eAbout the WebSim Website xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 Introduction to Alpha Design 3\u003cbr\u003e\u003ci\u003eBy Igor Tulchinsky\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 Perspectives on Alpha Research 7\u003cbr\u003e\u003ci\u003eBy Geoffrey Lauprete\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 Cutting Losses 17\u003cbr\u003e\u003ci\u003eBy Igor Tulchinsky\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Design and Evaluation 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4 Alpha Design 25\u003cbr\u003e\u003ci\u003eBy Scott Bender and Yongfeng He\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 How to Develop an Alpha: A Case Study 31\u003cbr\u003e\u003ci\u003eBy Pankaj Bakliwal and Hongzhi Chen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 Data and Alpha Design 43\u003cbr\u003e\u003ci\u003eBy Weijia Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 Turnover 49\u003cbr\u003e\u003ci\u003eBy Pratik Patel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Alpha Correlation 61\u003cbr\u003e\u003ci\u003eBy Chinh Dang and Crispin Bui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 Backtest – Signal or Overfitting? 69\u003cbr\u003e\u003ci\u003eBy Zhuangxi Fang and Peng Yan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10 Controlling Biases 77\u003cbr\u003e\u003ci\u003eBy Anand Iyer and Aditya Prakash\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 The Triple-Axis Plan 83\u003cbr\u003e\u003ci\u003eBy Nitish Maini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12 Techniques for Improving the Robustness of Alphas 89\u003cbr\u003e\u003ci\u003eBy Michael Kozlov\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13 Alpha and Risk Factors 95\u003cbr\u003e\u003ci\u003eBy Peng Wan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 Risk and Drawdowns 101\u003cbr\u003e\u003ci\u003eBy Hammad Khan and Rebecca Lehman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15 Alphas from Automated Search 111\u003cbr\u003e\u003ci\u003eBy Yu Huang and Varat Intaraprasonk\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16 Machine Learning in Alpha Research 121\u003cbr\u003e\u003ci\u003eBy Michael Kozlov\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17 Thinking in Algorithms 127\u003cbr\u003e\u003ci\u003eBy Sunny Mahajan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Extended Topics 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18 Equity Price and Volume 135\u003cbr\u003e\u003ci\u003eBy Cong Li and Huaiyu Zhou\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19 Financial Statement Analysis 141\u003cbr\u003e\u003ci\u003eBy Paul A. Griffin and Sunny Mahajan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20 Fundamental Analysis and Alpha Research 149\u003cbr\u003e\u003ci\u003eBy Xinye Tang and Kailin Qi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21 Introduction to Momentum Alphas 155\u003cbr\u003e\u003ci\u003eBy Zhiyu Ma, Arpit Agarwal, and Laszlo Borda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22 The Impact of News and Social Media on Stock Returns 159\u003cbr\u003e\u003ci\u003eBy Wancheng Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23 Stock Returns Information from the Stock Options Market 169\u003cbr\u003e\u003ci\u003eBy Swastik Tiwari and Hardik Agarwal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24 Institutional Research 101: Analyst Reports 179\u003cbr\u003e\u003ci\u003eBy Benjamin Ee, Hardik Agarwal, Shubham Goyal, Abhishek Panigrahy, and Anant Pushkar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25 Event-Driven Investing 195\u003cbr\u003e\u003ci\u003eBy Prateek Srivastava\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26 Intraday Data in Alpha Research 207\u003cbr\u003e\u003ci\u003eBy Dusan Timotity\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27 Intraday Trading 217\u003cbr\u003e\u003ci\u003eBy Rohit Kumar Jha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e28 Finding an Index Alpha 223\u003cbr\u003e\u003ci\u003eBy Glenn DeSouza\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e29 ETFs and Alpha Research 231\u003cbr\u003e\u003ci\u003eBy Mark YikChun Chan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e30 Finding Alphas on Futures and Forwards 241\u003cbr\u003e\u003ci\u003eBy Rohit Agarwal, Rebecca Lehman, and Richard Williams\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV New Horizon – Websim 251\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e31 Introduction to WebSim 253\u003cbr\u003e\u003ci\u003eBy Jeffrey Scott\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V A Final Word 263\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e32 The Seven Habits of Highly Successful Quants 265\u003cbr\u003e\u003ci\u003eBy Richard Hu and Chalee Asavathiratham\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eReferences 273\u003c\/p\u003e \u003cp\u003eIndex 291\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eIGOR TULCHINSKY\u003c\/b\u003e 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\u0026amp;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.   \u003c\/p\u003e\u003cp\u003eFor even the most experienced traders, designing the predictive mathematical models (alphas) at the core of quantitative trading is a complex, labor-intensive process that requires significant research and testing. Based on ever-changing data, quantitative strategies may have limited life spans; new models must be generated on a constant basis. Igor Tulchinsky, Founder, Chairman, and CEO of WorldQuant, and the team of researchers, portfolio managers, and technologists he has assembled at his global quantitative asset management firm have significant firsthand experience in this area.\u003ci\u003e Finding Alphas: A Quantitative Approach to Building Trading Strategies \u003c\/i\u003edraws on WorldQuant’s expertise in developing quantitative mathematical models to help you identify potentially profitable opportunities and build your own quantitative trading strategies. Covering everything from basic theory to advanced design and analysis techniques, this one-stop resource: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eDescribes how to design, evaluate, and deploy quantitative trading strategies. \u003c\/li\u003e \u003cli\u003eCovers the development and backtesting of alphas, momentum alphas, the use of futures and forwards, institutional research in alpha development, and more.\u003c\/li\u003e \u003cli\u003eExplains how to use WebSim, WorldQuant’s proprietary, internet-enabled simulation platform.\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989213724901,"sku":"NP9781119571216","price":47.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119571216.jpg?v=1761783231","url":"https:\/\/k12savings.com\/products\/finding-alphas-isbn-9781119571216","provider":"K12savings","version":"1.0","type":"link"}