We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions,...

Using proprietary data on millions of trades by retail investors, we provide the first large-scale evidence that retail short selling predicts negative stock returns. A portfolio that mimics weekly retail shorting earns an annualized risk-adjusted return of 9%. The predictive ability of retail...

You're probably familiar, at least in passing, with the 'convexity' of long-term bonds - i.e. that yields dropping 1% produce a bigger price move than yields rising 1%. A significant amount of brainpower has gone into understanding all the ramifications of this convexity in the fixed income...

We study the out-of-sample and post-publication return-predictability of 97 variables that academic studies show to predict cross-sectional stock returns. Portfolio returns are 26% lower out-of-sample and 58% lower post-publication. The out-of-sample decline is an upper bound estimate of data...

Portfolio optimization focuses on risk and return prediction, yet implementation costs critically matter. Predicting trading costs is challenging because costs depend on trade size and trader identity, thus impeding a generic solution. We focus on a component of trading costs that applies...

This paper investigates the risk and return properties of a trading strategy for the cryptocurrency market. The main predictive power for portfolio formation comes from a simple prospect theory model that only uses price information readily available. The dataset consists of a large body of...

What is the market impact of predictable order flow? Leveraged exchange-traded products are useful for answering this question because they generate daily rebalancing flows whose size, sign and timing are predictable. This paper presents new evidence from the market for leveraged volatility...

In an empirical study of Standard & Poor's 500 index options, this paper analyses the predictability of future market excess returns by means of decomposed higher-moment risk premiums. The study proposes a new measure of kurtosis risk premium and suggests a decomposition of higher-moment risk...

Which market has leading informational advantage: stocks or options? Using large set of stock and option characteristics, and machine learning, we provide a comprehensive analysis of which characteristics are the first order importance predictors of options and stock returns. First, we find that...

A growing body of literature confirms the significance of the commodity futures basis factor: It has a significantly positive premium and it explains the cross-section of commodity-futures excess returns. We extend the literature by documenting predictive relation between this factor and the...