Bitcoin has been described as a decentralized, partially anonymous, virtual currency, not backed by any government or other legal entity. Bitcoins are highly liquid, have low transaction costs, and are very volatile. This paper will look at the behavior of the value of Bitcoins, forecast the...

This paper documents a significant time-series momentum effect that is consistent and robust across all examined conventional asset classes from 1969 to 2015. We find that the duration and magnitude of time-series momentum is different in developed and emerging markets, but this is no longer the...

Long histories of returns are needed but often lacking when estimating the equity premium. This paper studies stock return predictability from the perspective of a Bayesian investor who has access to international data. Learning across countries arises whenever this investor believes that...

This paper introduces a new out-of-sample forecasting methodology for monthly market returns using the variance risk premium (VRP) that is both statistically and economically significant. This methodology is motivated by the `beta representation,' which implies that the market risk premium is...

This paper considers how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of different vector autoregressive models, the investor is able, each period, to revise past predictive mistakes and learn about...

This paper provides a comprehensive analysis of stock return predictability in the Indian stock market by employing both the portfolio and cross-sectional regressions methods using the data from January 1994 and ending in December 2018. We find strong predictive power of size, cash-flow-to-price...

We show that aggregate insider trading (AIT) in the S&P 500 is a reliable predictor of the U.S. equity premium, while AIT outside the S&P 500 seems to be uninformative. Aggregate trading of S&P 500 insiders outperforms a broad set of well-established predictors considering in- and out-of-sample...

Taking the perspective of international asset allocation, this paper tests if predictive regressions conditional on time-series and cross-sectional information can improve forecasts of stock index returns. We use different current price-to-fundamental ratios as predictors and condition the...

We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfitting, we use the elastic net to estimate a high-dimensional...