This paper presents a theory to explain the economic value of tranching and provides empirical evidence to support the theoretical implications. I show that riskier firms are more likely to take loans with multiple tranches. Therefore, the average credit spread on a syndicated loan with multiple tranches is higher than that on a non-tranched loan. However, after accounting for the risk characteristics of a tranched loan, I show that borrowings that are a part of tranched loans have lower credit spreads than otherwise identical non-tranched loans. I also show that the benefits of tranching accrue primarily to riskier borrowers.

This paper provides an empirical analysis of the performance of 45 international closed-end funds and compares alternate measures of performance using the sample of funds and 35 national market indices. The empirical evidence indicates that the risk-adjusted performance of the shares or the net asset values of the funds match the performance of their respective local market indices, as well as the world market index and do not exhibit superior timing ability. These findings are robust to conditioning on information.

We hypothesize that vanity amplifies realization utility in teams; admitting mistakes is particularly painful when mistakes have to be admitted to self and colleagues. Consistent with the Vanity hypothesis, U.S. stock funds run by teams hold on to losers when losers were initiated by a subset of the team (to avoid admitting a mistake to their non-initiating colleagues), when initiators of loser positions are more experienced (to avoid losing authority by admitting mistakes to junior colleagues), and when all colleagues agree that a position is a loser. Vanity is costly – losers held underperform by a risk-adjusted 1% annually.

Chinese banks have become the largest cross-border creditors for almost half of all emerging market and developing economies (EMDEs). While they look similar to other EMDE banks in terms of ownership and balance-sheet structure, their global cross-border lending resembles that of banks from advanced economies along several dimensions, especially when lending to EMDEs. We find that geographical distance poses a barrier for cross-border lending, including for Chinese banks. For them, given their network of affiliates, this barrier is lower than for other EMDE banks, more like US or European banks. We show that across all bank nationalities, bilateral economic interactions, like trade, FDI and portfolio investment, all positively correlate with cross-border lending. What stands out is that Chinese banks’ lending to EMDEs correlates more than any other nationality with trade, but there is no such correlation with FDI and, unlike all other banks, their lending correlates negatively with portfolio investment.

This paper explores the determinants of financial development by focusing on the role played by barriers to the diffusion of financial technology. These barriers are measured using human genetic distance from the technology frontier. The results based on cross-sectional data for 123 countries suggest that genetic distance to the global frontier has an economically and statistically significant effect on financial development, in that countries that are genetically far from the technology leader tend to have lower levels of financial development. Genetic distance is found to have the largest effect, even after controlling for other determinants of financial development established in the literature. These findings indicate that cultural barriers to the diffusion of financial technology across borders impact financial development by influencing the follower countries’ ability to adopt and adapt innovations from the frontier.

We propose the use of stochastic frontier approach to modelling financial constraints of firms. The main advantage of the stochastic frontier approach over the stylised approaches that use pooled OLS or fixed effects panel regression models is that we can not only decide whether or not the average firm is financially constrained, but also estimate a measure of the degree of the constraint for each firm and for each time period, and also the marginal impact of firm characteristics on this measure. We then apply the stochastic frontier approach to a panel of Indian manufacturing firms, for the 1997–2006 period. In our application, we highlight and discuss the aforementioned advantages, while also demonstrating that the stochastic frontier approach generates regression estimates that are consistent with the stylised intuition found in the literature on financial constraint and the wider literature on the Indian credit/capital market.

We examine the effect of aggregate cash flow news and discount rate news on momentum returns. We find that momentum profits are higher following aggregate positive cash flow news, even in down markets or low sentiment periods. This finding expands on the evidence in Cooper et al. (2004) that momentum is significant only when past market returns are non-negative and in Antoniou et al. (2013) that momentum is weaker when sentiment is pessimistic. We find that the higher momentum profits during aggregate positive cash flow news periods are primarily driven by the losers continuing to underperform in subsequent periods. Our findings are consistent with the Hong and Stein (1999) model in the sense that gradual diffusion of contradictory news is accentuated when change in wealth is positive and relatively more permanent.

In this paper we show that Indian stock returns, based on industry portfolios, portfolios sorted on book-to-market, and on size, are predictable. While we discover that this predictability holds both in in-sample and out-of-sample tests, predictability is not homogenous. Some predictors are important than others and some industries and portfolios of stocks are more predictable and, therefore, more profitable than others. We also discover that a mean combination forecast approach delivers significant out-of-sample performance. Our results survive a battery of robustness tests.

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.