
Contents
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1.1 Ad hoc Sparsity in Empirical Asset Pricing 1.1 Ad hoc Sparsity in Empirical Asset Pricing
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1.2 Ad hoc Sparsity in Theoretical Asset Pricing 1.2 Ad hoc Sparsity in Theoretical Asset Pricing
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1.3 Machine Learning 1.3 Machine Learning
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1.4 Terminology 1.4 Terminology
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1.5 Supervised and Unsupervised Learning 1.5 Supervised and Unsupervised Learning
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1.6 Limitations of This Book 1.6 Limitations of This Book
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1.7 Organization of This Book 1.7 Organization of This Book
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Cite
Abstract
This chapter discusses prediction problems that are central to asset pricing, emphasizing that investors must forecast firms' future cash flows when it comes to price stocks. Researchers testing asset pricing models look for predictor variables that can forecast return differences between assets or that capture forecastable variation in returns across time. It describes models of credit risk that require predictors of default, including hedging and risk management models that require forecasts of asset return comovement. The chapter reviews technological advances that have led to an explosive growth in the amount of information that is available to investors and analysts. Even if looking at the narrow slice of data that can be extracted from corporate financial reports, the growth in data availability has been staggering.
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