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Über dieses Buch

This book provides readers with a systematic approach to quantitative investments and bridges the gap between theory and practice, equipping students to more seamlessly enter the world of industry. A successful quantitative investment strategy requires an individual to possess a deep understanding of the financial markets, investment theories and econometric modelings, as well as the ability to program and analyze real-world data sets.

In order to connect finance theories and practical industry experience, each chapter begins with a real-world finance case study. The rest of the chapter introduces fundamental insights and theories, and teaches readers to use statistical models and R programming to analyze real-world data, therefore grounding the learning process in application. Additionally, each chapter profiles significant figures in investment and quantitative studies, so that readers can more fully understand the history of the discipline.

This volume will be particularly useful to advanced students and practitioners in finance and investments.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract
In this chapter, we introduce major concepts and layout of the book. The industrial practice of quantitative investing emerged in the 1960s developed rapidly in the 1990s and became on par with traditional fundamental investing in the 2000s. Naturally, mathematical and statistical modeling plays a critical role in quantitative investing. Major factors contributing to the development of quantitative strategies are the rise of modern investment theory, large-scale use of computers, greater availability of data sets, and development of programming languages. While building a successful quantitative strategy is not easy, it is achievable and requires a fundamental understanding of the market, strong knowledge of investment theory, mastery of quantitative methodologies, and proficiency in data analysis and programming. These constitute the four pillars of successful quantitative investment described in this book. In this chapter, we introduce quantitative investing and present a hall of fame for modern investment theory and quantitative methods. We then share industry insights with readers and briefly introduce R programming.
Lingjie Ma

Chapter 2. Is the Current US Stock Market Overvalued? Univariate Analysis

Abstract
In this chapter, we employ univariate analysis to evaluate the US stock market. Using the S&P 500 daily pricing data from 1950 to 2018, we illustrate the concepts of four moments, density and cumulative distribution functions, and hypothesis testing. We present two important figures: Benjamin Graham on investment and Student (William Sealy Gosset) on univariate analysis. We show nonnormality of asset returns and present an industry approach to outliers. In the last section, we introduce R and demonstrate simple calculations and plots.
Lingjie Ma

Chapter 3. What Is the Relationship Between the Chinese and US Stock Markets? Bivariate Analysis

Abstract
In this chapter, we explore the relationship between two random variables using the example of the Chinese and US stock markets. How does the performance of the S&P 500 impact the Chinese stock market and vice versa? Are the impacts the same when the US market is bullish and bearish? How long do the impacts last? Is there a scientific way to measure the impacts and formulate an investment strategy? We answer all of these questions in this chapter. First, we introduce the Chinese stock market with a focus on its origins, development, and special features. We then introduce bivariate analysis, the concepts of correlation and rank correlation, and how to apply correlation to measure the relationship between the S&P 500 and CSI 300. We present industry approaches, such as spillover effects and information decay, and demonstrate how to explore the asymmetry of the relationship between the two stock markets. On the programming side, we show how to write a function in R and introduce various methods for loops.
Lingjie Ma

Chapter 4. How to Construct a Stock Selection Strategy: Multi-Factor Analysis

Abstract
In this chapter, we introduce stock selection strategies and demonstrate how to employ a multi-factor model to build alphas for such a strategy. How can we forecast stock returns? To answer this critical question, we first discuss market inefficiency and identify sources of return anomalies. We then show how to transform these fundamental sources into a multi-factor alpha model. Regarding related finance theory, we introduce the capital asset pricing model (CAPM). On the quantitative side, we present the ordinary least squares (OLS) method. We explore estimation, inference, and properties and conditions of OLS estimates. Regarding industry insights, we show, using the Russell 1000 security level data, how to construct a multi-factor alpha model for a large-cap core stock selection portfolio. For R programming, we introduce commonly used utility functions in quantitative investing.
Lingjie Ma

Chapter 5. More on Stock Selection Strategy: Alpha Hunting, Risk Adjustment, and Nonparametric Diagnostics

Abstract
In the previous chapter, we introduced a general procedure and multi-factor framework for alpha construction of stock selection strategies. In this chapter, we continue to explore stock selection strategy with more advanced topics. In particular, we focus on alpha (new factor) hunting, risk adjustment, and nonparametric diagnostics. Regarding new alpha discovery, we present the guidance of IPARE. From a methodological perspective, we introduce the weighted least squares (WLS) method, which provides a tool to integrate risk into a multi-factor alpha model. We then introduce nonparametric approaches as a complement to parametric analysis. In the industry insights section, we provide a nonparametric diagnostics package used in the industry to investigate a new factor. The last section on R programming shows how to refine plots with parameters.
Lingjie Ma

Chapter 6. How to Forecast Commodity Price Movements: Time Series Models

Abstract
In this chapter, we focus on commodity pricing and investment with time series models. For stock selection strategies, the ability to forecast individual stock returns is critical and usually relies on company-level factors, such as profitability, management quality, etc. In commodity investing, a deep understanding of the geopolitical dynamics and identification of macro-level factors are very important for a successful strategy. A stock selection strategy requires cross-sectional analysis, while commodity investing requires time series analysis. In this chapter, we get into details about the special features of time series models, introduce the concepts of unit root, spurious relationship, and cointegration, and show how they can be employed for quantitative investing in crude oil and pair trading.
Lingjie Ma

Chapter 7. Portfolio Construction: From Alpha/Risk to Portfolio Weights

Abstract
In this chapter, we focus on portfolio construction. In particular, we present details of the classical mean–variance approach, including principles, algorithms, and examples of a long only and a market neutral long-short portfolio. We also discuss backtesting and portfolio performance attribution. We introduce Harry Markowitz who made important contributions to modern portfolio theory. Regarding industry insights, we show how industry practitioners build MV portfolios with practical constraints. For R programming, we discuss the structure of R codes and functions.
Lingjie Ma

Chapter 8. Quantitative Investing with Tail Behavior—A Distributional Approach

Abstract
In previous chapters, we introduced classical mean–variance methodologies. Classical methodologies have been used widely in risk management, such as the use of standard deviation for risk; alpha models, such as the use of OLS for weighting schemes; and modern portfolio theory, such as the mean–variance optimization. However, these are all based on the assumption that the first two moments will capture most information about asset returns, which is usually not true of real-world finance data, where fat and long tails are often the case. In this chapter, we present a distributional approach to capture tail behaviors for quantitative investing. Quantile regression (QR), a frontier methodology that extends beyond the median into tail percentiles, provides a useful tool for incorporating tail information into portfolios. We explore how QR can be employed for risk management, alpha modeling, and portfolio construction. The last section introduces R codes and packages for QR.
Lingjie Ma

Chapter 9. Quantamental Investment

Abstract
In this chapter, we introduce quantamental investment—a frontier investment approach that emerged following the financial crisis of 2008. Quantamental investment combines fundamental and quantitative analysis to try to achieve both depth and breadth. We provide a heuristic definition of quantamental investment, explore how to conduct quantamental investment, and discuss important factors for successful quantamental investments such as team building and corporate culture. In the section on industry insights, we illustrate in detail how to employ a quantamental approach to build a Japanese stock selection strategy. In the R section, we discuss surveying with R.
Lingjie Ma

Backmatter

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