We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...

We introduce a new text-mining methodology that extracts sentiment information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised...

We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text content of 800,000 Wall Street Journal articles for 1984{2017, we estimate a topic model that summarizes business news as easily interpretable topical themes and quantifies the...

We develop the necessary methodology to conduct principal component analysis at high frequency. We construct estimators of realized eigenvalues, eigenvectors, and principal components and provide the asymptotic distribution of these estimators. Empirically, we study the high frequency covariance...

We propose a three-pass method to estimate the risk premia of observable factors in a linear asset pricing model, which is valid even when the observed factors are just a subset of the true factors that drive asset prices or they are measured with error. We show that the risk premium of a factor...

We propose a model-selection method to systematically evaluate the contribution to asset pricing of any new factor, above and beyond what a high-dimensional set of existing factors explains. Our methodology explicitly accounts for potential model-selection mistakes, unlike the standard...

Investors in option markets price in a substantial collective government bailout guarantee in the financial sector, which puts a floor on the equity value of the financial sector as a whole, but not on the value of the individual firms. The guarantee makes put options on the financial sector...

We empirically analyze the pricing of political uncertainty, guided by a theoretical model of government policy choice. To isolate political uncertainty, we exploit its variation around national elections and global summits. We find that political uncertainty is priced in the equity option...

Firm volatilities co-move strongly over time, and their common factor is the dispersion of the economy-wide firm size distribution. In the cross section, smaller firms and firms with a more concentrated customer base display higher volatility. Network effects are essential to explaining the...

We use textual analysis of high-dimensional data from patent documents to create new indicators of technological innovation. We identify significant patents based on textual similarity of a given patent to previous and subsequent work: these patents are distinct from previous work but are...