Banks play a vital role in strengthening the financial system of a country; hence, their survival is decisive for the stability of national economies. Therefore, analyzing the survival probability of the banks is an essential and continuing research activity. However, the current literature available indicates that research is currently limited on banks’ stress quantification in countries like India where there have been fewer failed banks. The literature also indicates a lack of scientific and quantitative approaches that can be used to predict bank survival and failure probabilities. Against this backdrop, the present study attempts to establish a bankruptcy prediction model using a machine learning approach and to compute and compare the financial stress that the banks face. The study uses the data of failed and surviving private and public sector banks in India for the period January 2000 through December 2017. The explanatory features of bank failure are chosen by using a two-step feature selection technique. First, a relief algorithm is used for primary screening of useful features, and in the second step, important features are fed into the support vector machine to create a forecasting model. The threshold values of the features for the decision boundary which separates failed banks from survival banks are calculated using the decision boundary of the support vector machine with a linear kernel. The results reveal, inter alia, that support vector machine with linear kernel shows 92.86% forecasting accuracy, while a support vector machine with radial basis function kernel shows 71.43% accuracy. The study helps to carry out comparative analyses of financial stress of the banks and has significant implications for their decisions of various stakeholders such as shareholders, management of the banks, analysts, and policymakers.

This study primarily investigates into as to what influenced the dividends payment of BSE constituent companies for the years 2002 through as latest as 2011. The primary model used is that of Lintner (1956) with addition of relevant factors. The study tests three models including Lintner's basic model. While dividends paid is criterion variable in all the models, basic earnings and lagged dividends are predictor variables in the first model (Lintner model, 1956), cash earnings and lagged dividends in the second model and growth opportunities (depreciation and capital expenditure) in the third model are the predictor variables. The study tests the hypotheses if the dividends paid (criterion variable) depended on basic earnings, lagged dividends, cash earnings and capital expenditure. The multiple regression analysis has been performed using SPSS 15.0 version through ENTER method for every year and for all the years on an aggregate basis across the sample companies. Significance 'F' revealed that in all the three models dividends paid depended significantly (at 5% significance level) on all predictors variables. The value of multiple 'R' indicated that the models were very strong. Coefficient of determination (R²) also revealed that the explained portion of the relationship between criterion and predictor variables has been very high and significant enough to accept the model fit. However, standardized beta co-efficients (â) and 't' statistic revealed that basic earnings, cash earnings and lagged dividends exercised highest impact on dividends paid in most of the years during the study period. On the other hand, other predictor variables, depreciation and capital expenditure, did not have any significant impact on the dividends paid. The Durbin Watson coefficient indicated that multi co-linearity among predictor variables was strong enough to accept the validity of the model almost during the entire period of the study. Thus, the results and findings of the study support the prevalence and relevance of Lintner model of dividend policy. This means that the finance manager can't afford to ignore the variables like earnings capacity and lagged dividends while framing a dividend policy.

The broad objective of the paper is to have an understanding of the movement of volatility over a fair period in respect of the market portfolio. Also, it enables an understanding on how divergent the implied volatility has been from this estimate. It uses Volatility Cone, Volatility Smile and Volatility Surface as the parameters. The study takes different rolling periods percentiles of volatility. Hoadley Options Calculator is used for calculation and analysis purpose. The study empirically proves that there is a clear reversion to the mean as indicated by the volatility cone. The study of volatility smiles in respect of NIFTY options throws up different patterns. The Garch (1.1) model reveals that historical volatility for the period from 2004 to 2004 and for the year 2009 were estimated. Interestingly, but not totally surprisingly, the average implied volatility of calls and puts on Nifty during the period January to March 2010 showed differences.

Earlier in the 1960s, though they were aware of the concept of risk, the portfolio managers did not know as to how to measure and hence their performance was measured only in terms of rate of return. Though quite a few measures were developed in 1960s, it was Friend, Blume and Crockett who developed a mechanism to group portfolios into similar risk class. This in fact helped the portfolio managers to compare the performance of various funds more meaningfully in terms of risk-return relationship. Keeping the importance of two sides of investment coin: the Risk and the Return, we, in this paper attempted to analyze the performance of equity linked and diversified funds. We also tested if the portfolio managers' stock selection ability enhanced the performance. We have used measures like Treynor's, Sharpe's, Jensen's Alpha, the Information Ratio and Net Selectivity. Using these measures, we attempted to find out if the portfolio managers could generate above-average rate of return for a given risk class. The sample comprised equity linked savings and diversified funds in Indian context. The analysis was done on quarterly, half yearly, yearly and five yearly basis for each fund. This facilitated us to identify if the time factor played a role in the performance of a given fund. The results revealed that the performance of the fund managers primarily depended on the type of measure. While the fund(s) performed better according to a given method, than that of others in a given risk class, it was vice versa according to other measures. This reveals that the selection of performance measure matters a lot while assessing the performance of a fund. Analysis of Variance (ANOVA) revealed that the performance of a fund depended on time factor also. The results of our study carry very significant implications with respect to portfolio performance analysis.

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