Subrata Kumar Mitra

Nonlinear impact of rain on foodgrain production in India

Owing to a very high population and low land resources, the economic importance of crop production is very high in India. However, in the absence of adequate irrigation facility, crop production of the country largely depends on rainfall during monsoon months. I, therefore, analysed the impact of rainfall on crop production using the ARDL approach of Pesaran et al. (2001). As the impact of rain might have an asymmetric effect on crop production, I analysed such possibility using the nonlinear ARDL model proposed by Shin et al. (2013).

The nexus between food price inflation and monsoon rainfall in India: exploring through comparative data mining models

In the paper we analysed the impact on food inflation of monsoon rainfall using different data mining tools, namely: lda (Linear Discriminant Analysis), qda (Quadratic Discriminant Analysis), lr (logistic regression), rpart (Recursive Partitioning and Regression Trees), knn (k-Nearest Neighbors Network), and formulated the models in such a way that food inflation at the end of the financial year can be predicted from the rainfall received during the monsoon month of the year, and a few other known variables. The study is expected to be useful as it can predict the chances of high food inflation with 65% and 63% of accuracy by rpart and lr models, respectively. This information on the chances of high food inflation just after monsoon months can be very useful for policy-makers. While prediction of high food inflation will not in itself solve the problem, it would help decision-makers to take precautionary measures to minimize its adverse impacts on the population.

Identifying periods of market inefficiency for return predictability

ABSTRACT The article examines the efficiency of 31 stock index series spanning 26 countries across the world, using generalized spectral test (GST) and detects departure from the martingale difference hypothesis (MDH). A moving window of 24 months was used and p-values of GST were estimated. In order to explore whether the departure from market efficiency can be used for generating profitable trades, an exponentially weighted-moving-average-based trading rule was applied and was found that average profits per trade were significantly higher when p-value of the GST was less than 0.1. These observations are in consistent with the adapted market hypothesis.

The efficiency of microfinance institutions with problem loans: A directional distance function approach

This article examines the question of whether the inclusion of problem loans leads to any variation in the technical efficiency of microfinance institutions (MFIs). This question has become pertinent as MFIs, which are well known for their excellent asset quality, have been vulnerable to a delinquency crisis worldwide. Traditionally, the efficiency of MFIs has been measured through non-parametric data envelopment analysis (DEA) or parametric stochastic frontier analysis. As both methods are not flexible enough to cover undesirable outputs, we have instead used the method of directional distance function (DDF) that accounts for the joint production of both desirable and undesirable outputs. Using data from 64 large MFIs, this study reveals corroborative evidence that, with the inclusion of at-risk portfolios as undesirable outputs in the efficiency analysis, the scores and rankings of sample MFIs differ significantly from the results of conventional DEA after the use of DDF. MFIs whose numbers of at-risk portfolios are comparatively high have exhibited lower efficiency scores and vice versa. It is therefore critical that MFIs also include problem loans in their efficiency assessment. This would help MFIs get a more accurate picture of their performance as compared to their peers.

Asymmetric impact of rainfall on India’s food grain production: evidence from quantile autoregressive distributed lag model

This study used a quantile autoregressive distributed lag (QARDL) model to capture asymmetric impact of rainfall on food production in India. It was found that the coefficient corresponding to the rainfall in the QARDL increased till the 75th quantile and started decreasing thereafter, though it remained in the positive territory. Another interesting finding is that at the 90th quantile and above the coefficients of rainfall though remained positive was not statistically significant and therefore, the benefit of high rainfall on crop production was not conclusive. However, the impact of other determinants, such as fertilizer and pesticide consumption, is quite uniform over the whole range of the distribution of food grain production.

Herding by Foreign Institutional Investors: An Evidential Exploration for Persistence and Predictability

ABSTRACT The primary objective of the study is to explore the predictability of herding patterns of foreign institutional investors in the Indian market using high frequency data over a period from January 2003 to June 2014. Herding of an individual stock was measured estimating a simple volume based ratio and persistence of trends was detected using the runs test (Wald and Wolfowitz [1940]) on that ratio. Predictability of herding behavior has been successfully modeled by applying 7 data mining models using various measures of performance. Market regulators may consider our findings to regulate the foreign institutional investors trading to make the financial system more transparent and robust.

Relationship between Biopsychosocial Factors and Financial Risk Tolerance: An Empirical Study

Executive Summary Financial risk tolerance (FRT) refers to the retail investors’ willingness to accept the negative changes in the value of investment or an outcome that is adversely different from the expected one. Understanding and assessing FRT plays a crucial role in individual choices about wealth accumulation, portfolio allocation, and all other investment and finance-related decisions, and in achieving financial goals. An advisor has to accurately assess FRT for achieving his/her goal or investor’s goal. Failure to do so leads to the choice of an investment option/portfolio which is inconsistent with one’s FRT, resulting in investor disappointment, that is, unbearable loss to the client. Such a situation may adversely affect the client–advisor relationship. Measuring FRT is challenging as it is a multidimensional attitude. Besides, it is an elusive concept that appears to be influenced by a number of predisposing factors such as demographic, environmental, and psychosocial factors. This study aims to identify the factors that are related to risk tolerance from outside the financial services domain such as psychology, economics, and bio-sociology. It deals most specifically with the relationship between biopsychosocial factors and FRT. Those who are interested in assessing and predicting FRT can move closer to a theoretical account that blends psychological and economic insights and supplements the understanding of risk-taking attitudes and behaviour of retail investors. Such an understanding will help financial advisors, policy makers, and researchers in identifying the determinants of an individual’s FRT to suggest the suitable investment alternatives to their clients. A single cross-sectional survey was conducted among 951 retail investors with various levels of investment experience through a structured questionnaire covering a variety of demographic factors. An analysis of the data collected through the questionnaire indicates that all the three factors—self-esteem, personality type, and sensation seeking—are positively related to FRT. This study adds to the extant literature on psychological determinants of FRT.