Quantitative Investment Analysis, 4/e (Hardocver)

本書序言

We are pleased to bring you Quantitative Investment Analysis, Fourth Edition, which focuses on key tools that are needed for today's professional investor. In addition to classic areas such as the time value of money and probability and statistics, the text covers advanced concepts in regression, time series, machine learning, and big data projects. The text teaches critical skills that challenge many professionals, and shows how these techniques can be applied to areas such as factor modeling, risk management, and backtesting and simulation.
      The content was developed in partnership by a team of distinguished academics and practitioners, chosen for their acknowledged expertise in the field, and guided by CFA Institute. It is written specifically with the investment practitioner in mind and is replete with examples and practice problems that reinforce the learning outcomes and demonstrate real-world applicability.
      The CFA Program Curriculum, from which the content of this book was drawn, is subjected to a rigorous review process to assure that it is:
• Faithful to the findings of our ongoing industry practice analysis
• Valuable to members, employers, and investors
• Globally relevant
• Generalist (as opposed to specialist) in nature
• Replete with sufficient examples and practice opportunities
• Pedagogically sound
      The accompanying workbook is a useful reference that provides Learning Outcome Statements that describe exactly what readers will learn and be able to demonstrate after mastering the accompanying material. Additionally, the workbook has summary overviews and practice problems for each chapter.
      We are confident that you will find this and other books in the CFA Institute Investment Series helpful in your efforts to grow your investment knowledge, whether you are a relatively new entrant or an experienced veteran striving to keep up to date in the ever-changing market environment. CFA Institute, as a long-term committed participant in the investment profession and a not-for-profit global membership association, is pleased to provide you with this opportunity.

本書特色

Whether you are a novice investor or an experienced practitioner, Quantitative Investment Analysis, Fourth Edition has something for you. Part of the CFA Institute Investment Series, this authoritative guide is relevant the world over and will  facilitate your mastery of quantitative methods and their application in today's investment process.

This updated edition provides all the statistical tools and latest information you need to be a confident and knowledgeable investor. This edition expands coverage to Machine Learning algorithms and the role of Big Data in an investment context along with capstone chapters in applying these techniques to factor modeling, risk management and backtesting and simulation in investment strategies. The authors go to great lengths to ensure an even treatment of subject matter, consistency of mathematical notation, and continuity of topic coverage that is critical to the learning process. Well suited for motivated individuals who learn on their own, as well as general reference, this complete resource delivers clear, example -driven coverage of a wide range of quantitative methods. Inside you'll find:

• Learning outcome statements (LOS) specifying the objective of each chapter

• A diverse variety of investment-oriented examples both aligned with the LOS and reflecting the realities of today's investment world

• A wealth of practice problems, charts, tables, and graphs to clarify and reinforce the concepts and tools of quantitative investment management
Sharpen your skills by furthering your hands-on experience with the Quantitative Investment Analysis Workbook, Fourth Edition-an essential guide, containing learning outcomes and summary over view sections, along with challenging problems and solutions.

Chapter 1 The Time Value of Money
Chapter 2 Organizing, Visualizing, and Describing Data
Chapter 3 Probability Concepts
Chapter 4 Common Probability Distributions
Chapter 5 Sampling and Estimation
Chapter 6 Hypothesis Testing
Chapter 7 Introduction to Linear Regression
Chapter 8 Multiple Regression
Chapter 9 Time-Series Analysis
Chapter 10 Machine Learning
Chapter 11 Big Data Projects
Chapter 12 Using Multifactor Models
Chapter 13 Measuring and Managing Market Risk
Chapter 14 Backtesting and Simulation