LIQUIDITY AND ASSET PRICING UNDER THE THREE‐MOMENT CAPM PARADIGM

We examine whether the use of the three‐moment capital asset pricing model can account for liquidity risk. We also make a comparative analysis of a four‐factor model based on Fama–French and Pástor–Stambaugh factors versus a model based solely on stock characteristics. Our findings suggest that neither of the models captures the liquidity premium nor do stock characteristics serve as proxies for liquidity. We also find that sensitivities of stock return to fluctuations in market liquidity do not subsume the effect of characteristic liquidity. Furthermore, our empirical findings are robust to differences in market microstructure or trading protocols between NYSE/AMEX and NASDAQ.     

On efficiency of the ratio estimator

Summary The exact mean square error for the ratio estimator of a finite population total based on simple random sampling without replacement is shown to have an expected value less than that of the variance of the ratio estimator based on Midzuno’s scheme, under a usual super-population model.     

Bank mergers and components of risk: An evaluation

The present study empirically examines the contribution of the acquired banks in only the nonconglomerate types of mergers (i.e., banks with banks), where the bulk of the payment is in the form of equity to the acquiring bank and finds overwhelmingly statistically significant evidence that nonconglomerate types of mergers definitely reduce the total as well as the unsystematic risk while having no statistically significant effect on systematic risk. Therefore, it seems that diversification may be a possible motive for bank mergers.     

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

Global marketing of industrial products: Are interpersonal relationships always critical?

The present paper examines the issue of whether interpersonal relationships are critical for global marketing of industrial products. The fields of relationship marketing, IMP group research, sales research, and network theory have stressed the importance of interpersonal relationships in the business-to-business or industrial marketing context. In contrast to this emphasis on interpersonal relationships, we argue that industrial firms can both conceive and enhance marketing strategies based on developing high quality and consistent processes, products, services or outcomes (consistent processes and outcomes). Such strategies are especially important given the fact that developing interpersonal relationships is expensive due to their reliance on frequent and/or face-to-face communications. In this paper, we examine industry and country contexts that lead to the choice of alternative industrial product marketing strategies and highlight some future research directions and managerial implications.     

Trade Size Preference of Informed Traders in Indian Equity Markets

This study utilized high frequency transactions data to analyze the trade size preference of informed traders in Indian equity markets. It is observed that informed traders at an aggregate level adopt stealth trading strategy, wherein they prefer medium sized trades over large sized trades in order to camouflage their private information. However, the stealth trading behavior varies across stocks, wherein informed traders prefer more large sized trades on firms that are part of an index compared to non-index firms. Trading behavior also varies across other market conditions. It has been noted that informed traders prefer large sized trades during periods of high market thickness, negative returns, and low volatility. This study also provides a rationale for such varied behavior of informed traders.     

Corporate Risk Management under Information Asymmetry

This paper examines the financial and operational hedging activities of US pharmaceutical and biotech firms that are subject to a high level of information asymmetry stemming from R&D investments during 2001–2006. We find evidence in support of the information asymmetry hypothesis à la Froot, Scharfstein and Stein (1993) that hedging helps mitigate the under‐investment problem. Specifically, we find that the use of financial derivatives is associated with greater firm value and that the value enhancement is larger for firms subject to greater information asymmetry and better growth opportunities. There is a synergy between financial hedging and operational hedging where the latter is used to counter product development risk. The results are robust with respect to alternative performance measures, industry‐specific growth measures, and the endogeneity problem. Our work is differentiated from existing studies that examined commodity‐based industries without addressing information asymmetry.