While the literature concerned with the predictability of stock returns is huge, surprisingly little is known when it comes to role of the choice of estimator of the predictive regression. Ideally, the choice of estimator should be rooted in the salient features of the data. In case of predictive regressions of returns there are at least three such features; (i) returns are heteroskedastic, (ii) predictors are persistent, and (iii) regression errors are correlated with predictor innovations. In this paper we examine if the accounting of these features in the estimation process has any bearing on our ability to forecast future returns. The results suggest that it does.

In this paper, we examine the relationship between oil price and firm returns for 560 US firms listed on the NYSE. First, we find that oil price affects returns of firms differently depending on their sectoral location. Second, we find strong evidence of lagged effect of oil price on firm returns. Third, we test whether oil price affects firm returns based on different regimes and find that in five out of the 14 sectors this is indeed the case. Finally, we unravel that oil price affects firm returns differently based on firm size, implying strong evidence of size effects.

In this paper, we examine convergence of stock markets. Our empirical exercise is based on 11 different panels, which together consist of 120 countries. The richness of the dataset allows us to disaggregate countries into panels, such as high income, middle income, low income, OECD, CSI, and developing country panels. In addition, we construct regional panels, such as those representing the Arab States, East Asia and the Pacific, South Asia, Latin America and the Caribbean, and Sub-Saharan Africa. Our main finding is that, based on the conditional convergence model, convergence of stock market capitalization and stocks traded is found for four panels, namely the high and low-income panels, the OECD panel, and the Sub-Saharan African panel. The speed of convergence is high, in most cases between 20% and 30%.

In this paper we study whether the commodity futures market predicts the commodity spot market. Using historical daily data on four commodities—oil, gold, platinum, and silver—we find that they do. We then show how investors can use this information on the futures market to devise trading strategies and make profits. In particular, dynamic trading strategies based on a mean–variance investor framework produce somewhat different results compared with those based on technical trading rules. Dynamic trading strategies suggest that all commodities are profitable and profits are dependent on structural breaks. The most recent global financial crisis marked a period in which commodity profits were the weakest.