Using text from 200 million pages of 13,000 US local newspapers and machine learning methods, we construct a 170-year-long measure of economic sentiment at the country and state levels, that expands existing measures in both the time series (by more than a century) and the cross-section. Our...

We formulate a quantitative dynamic equilibrium theory of trade in the fed funds market, calibrate it to fit a comprehensive set of marketwide and micro-level cross-sectional observations, and use it to make two contributions to the operational side of monetary policy implementation. First, we...

Using a two-period model of a commodity market with a large number of atomistic consumers and two strategic sellers, we show that a speculator with access to storage can lower the market price while buying and raise the price while selling by clever use of limit, stop-loss, and market orders....

We measure investors' short- and long-term stock-return expectations using both options and survey data. These expectations at different horizons reveal what investors think their own short-term expectations will be in the future, or forward return expectations. While contemporaneous short-term...

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 abnormal return associated with a stock being added to the S&P 500 has fallen from an average of 3.4% in the 1980s and 7.6% in the 1990s to 0.8% over the past decade. This has occurred despite a significant increase in the percentage of stock market assets linked to the index. A similar...

Is shareholder interest in corporate social responsibility driven by pecuniary motives (abnormal rates of return) or non-pecuniary ones (willingness to sacrifice returns to address various firm externalities)? To answer this question, we categorize the literature into seven tests: (1) costs of...

Using transaction data from a large non-fungible token (NFT) trading platform, this paper examines how the behavioral bias of selection-neglect interacts with extrapolative beliefs, accelerating the boom and delaying the crash in the recent NFT bubble. We show that the price-volume relationship...

We address the joint hypothesis problem in cross-sectional asset pricing by using measured analyst expectations of earnings growth. We construct a firm-level measure of Expectations Based Returns (EBRs) that uses analyst forecast errors and revisions and shuts down any cross-sectional...

We introduce artificial intelligence pricing theory (AIPT). In contrast with the APT's foundational assumption of a low dimensional factor structure in returns, the AIPT conjectures that returns are driven by a large number of factors. We first verify this conjecture empirically and show that...