This paper examines the possibility of using derivative-implied risk premia to explain stock returns. The rapid development of derivative markets has led to the possibility of trading various kinds of risks, such as credit and interest rate risk, separately from each other. This paper uses...

Leveraged and inverse ETFs (LETFs) were introduced in 2006 and their popularity surged starting in 2008. As of the first quarter of 2012 there were over 200 such ETFs with over $30 billion in assets under management (AUM). By late 2008 there was concern about their late-day impact on stock...

This paper examines whether tone (positive and negative) and volume of firm-specific news media content provide valuable information about future stock returns, using UK news media data from 1981–2010. The results indicate that both tone and volume of news media content significantly predict...

This paper examines whether tone (positive and negative) and volume of firm-specific news media content provide valuable information about future stock returns, using UK news media data from 1981-2010. The results indicate that both tone and volume of news media content significantly predict...

This paper finds positive evidence of return predictability and investment gains for individual corporate bonds for an extended period from 1973 to 2017. Our sample consists of both public and private company bond observations. We have implemented multiple machine learning methods and designed a...

There is a generalized conviction that variation in dividend yields is exclusively related to expected returns and not to expected dividend growth - e.g. Cochrane's presidential address (Cochrane (2011)). We show that this pattern, although valid for the aggregate stock market, is not true for...

We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based upon observable characteristics, using machine learning principles with linear models. The best performing methods are flexible two-stage...

In this paper we introduce a new class of approaches to empirical asset pricing research, namely LASSO methods augmented by further penalties related to differences in adjacent coefficient estimates (at t and t+1) for a given characteristic. The economic motivation for this is that the...

With approximately 900 million observations we conduct, to our knowledge, the largest study ever of intraday stock return predictability using machine learning techniques finding consistent out-of-sample predictability across market, sector, and individual stock returns at various time horizons....