Text data is ultra-high dimensional, which makes machine learning techniques indispensable for textual analysis. Text is often selected--journalists, speechwriters, and others craft messages to target their audiences' limited attention. We develop an economically motivated high dimensional...

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

We find that shocks to the equity capital ratio of financial intermediaries--Primary Dealer counterparties of the New York Federal Reserve--possess significant explanatory power for crosssectional variation in expected returns. This is true not only for commonly studied equity and government...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

The core statistical technology in artificial intelligence is the large-scale transformer network. We propose a new asset pricing model that implants a transformer in the stochastic discount factor. This structure leverages conditional pricing information via cross-asset information sharing and...

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...

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 propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

We propose and implement a procedure to dynamically hedge climate change risk. To create our hedge target, we extract innovations from climate news series that we construct through textual analysis of high-dimensional data on newspaper coverage of climate change. We then use a mimicking...