The present issue contains four research papers. In the first paper, “A Credit
Scoring Model for Microfinance Bank Based on Fuzzy Classifier Optimized by a
Differential Evolution Algorithm”, the author, Ibtissem Baklouti, notes that the process of effective credit risk assessment plays an important role in the financial decision making in Microfinance Institutions (MFIs) by enabling faster credit approval decisions and diminishing the possible risks associated with customers’ payment defaults. Credit scoring is the most commonly used technique for evaluating the creditworthiness of loan and has gradually begun to find its way into the microfinance field. Many parametric and nonparametric techniques have been adopted by financial institutions to develop accurate credit scoring models. In this study, the author develops a credit scoring model for a Tunisian Microfinance Bank by applying fuzzy classifiers where the fuzzy knowledge bases are optimized through differential evolution. The performance of the proposed model is then compared to that of the decision tree model. The obtained results reveal that the proposed model consistently gives a better average correct classification rate than the decision tree model. As with the decision tree model, the proposed model can be easily understood by any user and is very useful in the context of credit evaluation process since it is in ‘if-then’ rule form. Unlike decision tree model, the proposed model does not stay in a black box. In the proposed model, the interpretation of independent variables may provide valuable information for bankers and consumers, especially in the explanation of why credit applications are rejected.

In the second paper, “A Comparative Performance Evaluation of Private Sector and Public Sector Equity Funds of India”, the authors, Kshama Agarwal and Prerna Patwa, compare the performance of equity funds, focusing on the growth of public sector mutual fund and private sector mutual fund. During the last few years, the bank rates have been dropping and have generally been lower than the inflation rate. Therefore, it is not an intelligent option to deposit large amounts in banks as fixed deposits or term deposits. In order to overcome the increasing requirements of day-to-day routine life and to maintain a decent standard of living, one needs to not only save money, but also invest it in such avenues where one can get maximum returns. But as the return increases, the risk also increases. It is tough for an investor to earn decent returns matching his risk appetite. Mutual fund is such an investment avenue which offers different schemes to its investors that match their risk-bearing capacity and generate smart returns. The present study evaluates the performance of equity funds by analyzing a sample of four companies each from both the sectors and five schemes of similar nature. It basically evaluates the risk-return profile of the funds by testing two hypotheses using Mann-Whitney U-test. The findings reveal that there is a significant difference between the performances of private and public sector mutual funds and that the private sector has performed better than the public sector.

In the third paper, “Order Imbalance and Returns: Evidence of Lead-Lag Relationship”, the authors, Nikhil Rastogi, V N Reddy and Kiran Kumar Kotha, explore the lead-lag relationship between the variables of order imbalance and return. Order imbalance is defined as the difference between buyer and seller initiated trades. Using tick test, the trades are classified as buyer and seller initiated. The authors find positive correlation between the variables of order imbalance in the futures market and the returns in the spot market. This relationship is further explored using a VAR framework for the daily as well as a shorter interval of 120 min. The authors find that even after controlling for lagged futures and spot returns, the futures market imbalance has a significant effect on spot market returns.

In the last study, Sajad Ahmad Bhat and Md Zulquar Nain, focuses on “Modeling the Conditional Heteroscedasticity and Leverage Effect in the BSE Sectoral Indices”. The study uses three volatility models of the GARCH family to examine the volatility behavior and in particular volatility persistence or long memory of the return series of four BSE sectoral indices. The study uses the daily data from January 1, 2002 to December 31, 2013. The results of the standard GARCH model suggest the presence of volatility persistence in the return series of all four indices. The EGARCH results suggest that the leverage effect is present and significant for BSE IT and BSE Bankex only, implying that BSE Metal and BSE PSU can be good investment in anticipation of bad times. The CGARCH estimates indicate that the short-run volatility component is weaker. However, the permanent components of the conditional variance exhibit a high degree of persistence for all return series.

-Nupur Pavan Bang
Consulting Editor