We use the cross-section regression approach of Fama and MacBeth (FM 1973) to construct cross-section factors corresponding to the time-series factors of Fama and French (FF 2015). Time-series models that use only cross-section factors provide better descriptions of average returns than time-series models that use time-series factors. This is true when we impose constant factor loadings and when we use time-varying loadings that are natural for time-series factors and time-varying loadings that are natural for cross-section factors.

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.

This textbook is intended to fill a gap in undergraduate finance curriculums by providing an asset pricing text that is accessible for undergraduate finance students. It offers an overview of original works on foundational asset pricing studies that follows their historical publication chronologically throughout the text. Each chapter stays close to the original works of these major authors, including quotations, examples, graphical exhibits, and empirical results. Additionally, it includes statistical concepts and methods as applied to finance. These statistical materials are crucial to learning asset pricing, which often applies statistical tests to evaluate different asset pricing models. It offers practical examples, questions, and problems to help students check their learning and better understand the fundamentals of asset pricing., alongside including PowerPoint slides and an instructor’s manual for professors.

This book proposes a new capital asset pricing model dubbed the ZCAPM that outperforms other popular models in empirical tests using US stock returns. The ZCAPM is derived from Fischer Black’s well-known zero-beta CAPM, itself a more general form of the famous capital asset pricing model (CAPM) by 1990 Nobel Laureate William Sharpe and others. It is widely accepted that the CAPM has failed in its theoretical relation between market beta risk and average stock returns, as numerous studies have shown that it does not work in the real world with empirical stock return data. The upshot of the CAPM’s failure is that many new factors have been proposed by researchers. However, the number of factors proposed by authors has steadily increased into the hundreds over the past three decades. This new ZCAPM is a path-breaking asset pricing model that is shown to outperform popular models currently in practice in finance across different test assets and time periods. Since asset pricing is central to the field of finance, it can be broadly employed across many areas, including investment analysis, cost of equity analyses, valuation, corporate decision making, pension portfolio management, etc. The ZCAPM represents a revolution in finance that proves the CAPM as conceived by Sharpe and others is alive and well in a new form, and will certainly be of interest to academics, researchers, students, and professionals of finance, investing, and economics.

Stocks and bonds? Real estate? Hedge funds? Private equity? If you think those are the things to focus on in building an investment portfolio, Andrew Ang has accumulated a body of research that will prove otherwise. In this book, Ang upends the conventional wisdom about asset allocation by showing that what matters aren't asset class labels but the bundles of overlapping risks they represent.

An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.