Cover image for Finding Alphas, 2nd Edition

Finding Alphas, 2nd Edition

Author Igor Tulchinsky
Release Date: 2019/10/01
ISBN: 9781119571216
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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

  1. Cover
  2. Preface
  3. Preface (to the Original Edition)
  4. Acknowledgments
    1. DISCLAIMER
  5. About the WebSim Website
    1. WEBSIM RESEARCH CONSULTANTS
  6. PART I: Introduction
    1. 1 Introduction to Alpha Design
    2. DESIGNING ALPHAS BASED ON DATA
    3. DEFINING QUALITY IN ALPHAS
    4. ALPHA CONSTRUCTION, STEP BY STEP
    5. CONCLUSION
    6. 2 Perspectives on Alpha Research
    7. PHDS ON THE STREET
    8. A NEW INDUSTRY
    9. STATISTICAL ARBITRAGE
    10. EXISTENCE OF ALPHAS
    11. IMPLEMENTATION
    12. EVALUATION
    13. LOOKING BACK
    14. THE OPPORTUNITY
    15. 3 Cutting Losses
    16. HOW TO APPLY THE PRINCIPLE OF THE UNRULE TO CUTTING LOSSES
    17. SUMMARY
  7. PART II: Design and Evaluation
    1. 4 Alpha Design
    2. DATA INPUTS TO AN ALPHA
    3. ALPHA UNIVERSE
    4. ALPHA PREDICTION FREQUENCY
    5. VALUE OF AN ALPHA
    6. PRACTICAL ALPHA EVALUATION
    7. FUTURE PERFORMANCE
    8. CONCLUSION
    9. 5 How to Develop an Alpha: A Case Study
    10. CONCLUSION
    11. NOTE
    12. 6 Data and Alpha Design
    13. HOW WE FIND DATA FOR ALPHAS
    14. DATA VALIDATION
    15. UNDERSTAND THE DATA BEFORE USING IT
    16. EMBRACE THE BIG DATA ERA
    17. CONCLUSION
    18. 7 Turnover
    19. ALPHA HORIZON
    20. THE COST OF A TRADE
    21. THE CROSSING EFFECT
    22. CONTROLLING TURNOVER
    23. EXAMPLES
    24. TUNING THE TURNOVER
    25. NOTE
    26. 8 Alpha Correlation
    27. ALPHA PnL CORRELATION
    28. ALPHA VALUE CORRELATION
    29. CORRELATION WITH ALPHA POOL
    30. CONCLUSION
    31. 9 Backtest – Signal or Overfitting?
    32. INTRODUCTION
    33. STATISTICAL ARBITRAGE
    34. BACKTESTING
    35. OVERFITTING
    36. HOW TO AVOID OVERFITTING
    37. CONCLUSION
    38. 10 Controlling Biases
    39. INTRODUCTION
    40. CATEGORIES OF BIAS
    41. SYSTEMATIC BIASES
    42. BEHAVIORAL BIASES
    43. CONCLUSION
    44. 11 The Triple-Axis Plan
    45. THE TAP
    46. IMPLEMENTING TAP
    47. CONCLUSION
    48. 12 Techniques for Improving the Robustness of Alphas
    49. INTRODUCTION
    50. METHODS FOR ROBUSTNESS IMPROVEMENT
    51. CONCLUSION
    52. 13 Alpha and Risk Factors
    53. 14 Risk and Drawdowns
    54. ESTIMATING RISKS
    55. DRAWDOWNS
    56. CONTROLLING RISKS
    57. CONCLUSION
    58. 15 Alphas from Automated Search
    59. EFFICIENCY AND SCALE
    60. INPUT DATA SHOULD NOT COME FROM TOO MANY CATEGORIES
    61. INPUT DATA AND UNITLESS RATIOS
    62. UNNECESSARY SEARCH SPACE
    63. INTERMEDIATE VARIABLES
    64. SEAS OF ALPHAS, NOT SINGLE ALPHAS
    65. SIMPLE ALPHAS
    66. LONGER BACKTESTING PERIODS
    67. ALPHA BATCH, NOT SINGLE ALPHA PERFORMANCE
    68. DIVERSIFY THE ALPHA BATCH
    69. SENSITIVITY TESTS AND SIGNIFICANCE TESTS
    70. MANUAL ALPHA SEARCH
    71. CONCLUSION
    72. 16 Machine Learning in Alpha Research
    73. INTRODUCTION
    74. MACHINE LEARNING METHODS
    75. CONCLUSION
    76. 17 Thinking in Algorithms
    77. DIGITAL FILTERS
    78. OPTIMIZATION AND LOSS FUNCTION DESIGN
    79. THE BIAS–VARIANCE TRADE-OFF
    80. DIMENSIONALITY REDUCTION
    81. SHRINKAGE ESTIMATORS
    82. PARAMETER OPTIMIZATION
    83. CONCLUSION
  8. PART III: Extended Topics
    1. 18 Equity Price and Volume
    2. INTRODUCTION
    3. SEEKING PROFITS THROUGH PRICE AND VOLUME
    4. CONCLUSION
    5. 19 Financial Statement Analysis
    6. BASICS
    7. THE BALANCE SHEET
    8. THE INCOME STATEMENT
    9. THE CASH FLOW STATEMENT
    10. GROWTH
    11. CORPORATE GOVERNANCE
    12. NEGATIVE FACTORS
    13. SPECIAL CONSIDERATIONS
    14. FACTORS AS SCREENS
    15. CONVERTING FACTORS TO ALPHAS
    16. CONCLUSION
    17. 20 Fundamental Analysis and Alpha Research
    18. FINANCIAL STATEMENTS
    19. FINANCIAL STATEMENT ANALYSIS
    20. CONCLUSION
    21. 21 Introduction to Momentum Alphas
    22. CONCLUSION
    23. 22 The Impact of News and Social Media on Stock Returns
    24. INTRODUCTION
    25. NEWS IN ALPHAS
    26. ACADEMIC RESEARCH
    27. SENTIMENT
    28. NOVELTY
    29. RELEVANCE
    30. NEWS CATEGORIES
    31. EXPECTED AND UNEXPECTED NEWS
    32. HEADLINES AND FULL TEXT
    33. NO NEWS IS GOOD NEWS?
    34. NEWS MOMENTUM
    35. SOCIAL MEDIA
    36. CONCLUSION
    37. 23 Stock Returns Information from the Stock Options Market
    38. INTRODUCTION
    39. VOLATILITY SKEW
    40. VOLATILITY SPREAD
    41. OPTIONS TRADING VOLUME
    42. OPTION OPEN INTEREST
    43. CONCLUSION
    44. NOTE
    45. 24 Institutional Research 101: Analyst Reports
    46. ACCESSING ANALYST RESEARCH (FOR FREE, OF COURSE)
    47. SO FAR, SO GOOD. BUT WHY SHOULD YOU CARE?
    48. THINGS TO WATCH OUT FOR IN READING ANALYST REPORTS
    49. WHY DO STOCK ANALYSTS TALK TO THE FINANCIAL MEDIA?
    50. CONCLUSION
    51. NOTES
    52. 25 Event-Driven Investing
    53. INTRODUCTION
    54. MERGER ARBITRAGE
    55. SPIN-OFFS, SPLIT-OFFS, AND CARVE-OUTS
    56. DISTRESSED-ASSET INVESTING
    57. INDEX-REBALANCING ARBITRAGE
    58. CAPITAL STRUCTURE ARBITRAGE
    59. CONCLUSION
    60. 26 Intraday Data in Alpha Research
    61. DATA IN MARKET MICROSTRUCTURE
    62. THE ILLIQUIDITY PREMIUM IN ASSET PRICES
    63. MARKET MICROSTRUCTURE AND EXPECTED RETURNS
    64. CONCLUSION
    65. 27 Intraday Trading
    66. DAILY TRADING VERSUS INTRADAY TRADING
    67. DIFFERENT TYPES OF INTRADAY ALPHAS
    68. MAKING AN INTRADAY ALPHA
    69. CONCLUSION
    70. 28 Finding an Index Alpha
    71. INDEX ARBITRAGE IN PRACTICE
    72. MARKET IMPACT FROM INDEX CHANGES
    73. OTHER INDEX ANOMALIES
    74. CONCLUSION
    75. 29 ETFs and Alpha Research
    76. MERITS OF INVESTING IN ETFS
    77. RISKS BEHIND THESE INSTRUMENTS
    78. ALPHA OPPORTUNITIES
    79. CHALLENGES IN ETF ALPHA RESEARCH
    80. CONCLUSION
    81. 30 Finding Alphas on Futures and Forwards
    82. KEY MARKET FEATURES
    83. UNDERLYING FACTOR EXPOSURE
    84. CONSEQUENCES OF INSTRUMENT GROUPING
    85. BASIC CHECKLIST FOR ALPHA TESTING
    86. CONCLUSION
  9. PART IV: New Horizon – WebSim
    1. 31 Introduction to WebSim
    2. INTRODUCTION
    3. WHY WEBSIM WAS DEVELOPED
    4. HOW WEBSIM IS USED GLOBALLY
    5. WHO USES WEBSIM
    6. WHERE ALPHA IDEAS COME FROM
    7. SAMPLE DATA TYPES
    8. CREATING AN ALPHA
    9. MANAGING SIMULATION SETTINGS
    10. ANALYZING RESULTS
    11. AN ALPHA EXAMPLE
    12. CONCLUSION
    13. NOTE
  10. PART V: A Final Word
    1. 32 The Seven Habits of Highly Successful Quants
    2. 1. WORK HARD WITHOUT EVEN REALIZING IT
    3. 2. SET AMBITIOUS LONG-TERM TARGETS BUT ATTAINABLE WEEKLY GOALS
    4. 3. PRIORITIZE BASED ON RISK AND REWARD
    5. 4. STAY CURIOUS
    6. 5. PERFORM VALUE-ADDED WORK – AND AUTOMATE, AUTOMATE, AUTOMATE
    7. 6. MAKE SENSIBLE CHANGES, AND BEWARE OF OVERFITTING
    8. 7. FORM SYNERGISTIC TEAMS
  11. References
    1. JOURNAL ARTICLES (PRINTED)
    2. JOURNAL ARTICLES (ELECTRONIC/ONLINE)
    3. BOOKS
    4. UNPUBLISHED MANUSCRIPTS/WORKING PAPER SERIES
    5. WEBSITES
    6. PUBLIC REPORTS
  12. Index
  13. End User License Agreement
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