In the estimation of risk measures such as Value at Risk and Expected shortfall relatively short estimation windows are typically used rendering the estimation error a possibly non-negligible component. In this paper we build upon previous results for the Value at Risk and discuss how the...

Using a novel equity lending dataset, this paper is the first to show that expected returns strongly and negatively predict future equity lending fees. In comparing two expected return measures, I find that a rational expected return has stronger predictive power of future short selling activity...

In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting, this brief study analyzes the predictability of Bitcoin volume and returns using Google search values. We employed a rich set of established empirical approaches, including a...

In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting, this brief study analyzes the predictability of Bitcoin volume and returns using Google search values. We employed a rich set of established empirical approaches, including a...

Experts’ opinions are widely considered for investment decisions. We collect textual information from cryptocurrency experts, study the dynamics in their discussion topics and their sentiment in relation to market movements. Based on the analysis we test various hypothesis which span if the...

We use the expected lifetime range (ELR) ratio based on the extreme values of asset prices to detect the presence of mean reversion in stock returns. We find that the actual cross-sectional average of the ELR ratio is significantly less than its bootstrap means, thereby indicating a considerable...

We investigate whether there are predictable patterns in the dynamics of higher order risk-neutral moments extracted from the market prices of S&P 500 index options. To this end, we conduct a horse race among alternative forecasting models within an out-of-sample context over various forecasting...

Can the degree of predictability found in the data be explained by existing asset pricing models? We provide two theoretical upper bounds on the R-squares of predictive regressions. Using data on the market and component portfolios, we find that the empirical R-squares are significantly greater...

The accuracy of U.S. stock return forecasts based on the cyclically-adjusted P/E (CAPE) ratio has deteriorated since 1985. The issue is not the CAPE ratio, but CAPE regressions that assume it reverts mechanically to its long-run average. Our approach conditions mean reversion in the CAPE ratio...

Predicting long-term equity market returns is of great importance for investors to strategically allocate their assets. We apply machine learning methods to forecast 10-year-ahead U.S. stock returns and compare the results to traditional Shiller regression-based forecasts more commonly used in...