Finding Alphas: A Quantitative Approach to Building Trading Strategies, Second Edition
- 4h 19m
- Igor Tulchinsky
- John Wiley & Sons (UK)
- 2020
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.
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.
In this Book
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Introduction to Alpha Design
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Perspectives on Alpha Research
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Cutting Losses
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Alpha Design
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How to Develop an Alpha—A Case Study
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Data and Alpha Design
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Turnover
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Alpha Correlation
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Backtest – Signal or Overfitting?
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Controlling Biases
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The Triple-Axis Plan
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Techniques for Improving the Robustness of Alphas
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Alpha and Risk Factors
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Risk and Drawdowns
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Alphas from Automated Search
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Machine Learning in Alpha Research
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Thinking in Algorithms
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Equity Price and Volume
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Financial Statement Analysis
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Fundamental Analysis and Alpha Research
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Introduction to Momentum Alphas
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The Impact of News and Social Media on Stock Returns
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Stock Returns Information from the Stock Options Market
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Institutional Research 101—Analyst Reports
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Event-Driven Investing
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Intraday Data in Alpha Research
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Intraday Trading
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Finding an Index Alpha
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ETFs and Alpha Research
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Finding Alphas on Futures and Forwards
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Introduction to WebSim
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The Seven Habits of Highly Successful Quants
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References
