We construct a slope factor from changes in federal funds futures of different horizons. Slope predicts stock returns at the weekly frequency: faster monetary policy easing positively predicts excess returns. Investors can achieve increases in weekly Sharpe ratios of 20% conditioning on the...

This paper sheds new light on the role of communication for cartel formation. Using machine learning to evaluate free-form chat communication among firms in a laboratory experiment, we identify typical communication patterns for both explicit cartel formation and indirect attempts to collude...

This paper addresses the steep learning curve in Machine Learning faced by non-computer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage ”once you understand OLS, you can work...

Using a unique dataset of 22.5 million news articles from the Dow Jones Newswires Archive, we perform an in depth real-time out-of-sample forecasting comparison study with one of the most widely used data sets in the newer forecasting literature, namely the FRED-MD dataset. Focusing on U.S. GDP,...

This paper proposes a theoretical model that incorporates corporate governance into the basic CAPM, where corporate governance affects the disutility of managerial effort and the possibility of managers to divert company resources. It shows that corporate governance affects firms’ stock...

Monetary policy shocks have a large impact on aggregate stock market returns in narrow event windows around press releases by the Federal Open Market Committee. We use spatial autoregressions to decompose the overall effect of monetary policy shocks into a direct (demand) effect and an indirect...

We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG...

The adaptive learning approach has been fruitfully employed to model the formation of aggregate expectations at the macroeconomic level, as an alternative to rational expectations. This paper uses adaptive learning to understand, instead, the formation of expectations at the micro-level, by...

Using a long-panel dataset of Japanese firms that contains firm-level sales forecasts, we provide evidence on firm-level uncertainty and imperfect information over their life cycle. We find that firms make non-negligible and positively correlated forecast errors. However, they make more precise...

The literature on learning in unknown environments emphasises reinforcing on actions which produce positive results. But, in some cases, success requires shifting from a currently successful actions to others. We examine, experimentally and theoretically in a very simple framework, how...