M Thenmozhi

This paper examines the effectiveness of stock index futures by analyzing the dynamic interactions and causal relationship between speculative type activity and spot market volatility. Evidence for bi-directional information flow between speculative activity and volatility are obtained using VAR methodology. It appears that investors speculate in the futures market, in particular when faced with volatility in the cash market. The fluctuations as a result of speculative activity are decreasing over a period of time, possibly due to the hedging activities taking place in the market. The dynamic interactions between speculative activity and spot volatility shows that index futures are having a stabilizing effect on underlying spot market.

This study investigates the performance of four training algorithms, namely, Standard Backpropagation (SBP), Scaled Conjugate Gradient (SCG), Resilient Backpropgation (RBP) and Levenberg-Marquardt (LM) Backpropagation in forecasting three financial time series, namely, Indian call rates, INR/USD exchange rates and S&P CNX Nifty Index. The models are trained from historical data using six technical indicators. The predicted results show that among the four training algorithms, LM based model outperforms other models when measured on commonly used non-penalty based metrics while SCG based model outperforms the other models when direction and sign based performance metrics are used.

Non-traditional debt instruments, particularly innovative instruments, have been the most preferred choice of firms in capital structure financing. This paper attempts to examine the micro finance factors that influence the firm's choice towards non-traditional debt financing. Various micro finance factors like financial leverage, operating leverage, volatility of earnings, value of collateral assets, non-debt tax shield, profitability, market to book ratio, firm size and firm size relative to economy, interest coverage ratio, price-earnings ratio, bankruptcy cost and cash constraint are identified as the probable discriminating factors. Multiple discriminant analysis shows that financial leverage, non-debt tax shield, profitability, firm size and cash constraint are the most significant factors that discriminate firms using traditional debt financing and those who use nontraditional debt financing. The analysis further reveals that a firm that issues non-traditional debt is characterized by high financial leverage, market to book ratio and bankruptcy cost on the one hand and by low level of volatility of earnings, profitability, Value of collateral assets, non-debt tax shield and cash constraint on the other.

In this study forecasting of the NIFTY stock index futures returns is carried out using backpropagation and recurrent neural network model and a linear ARIMA model. A comparison of different models shows that for NIFTY index futures returns backpropagation neural network model outperforms the recurrent neural network and the traditional ARIMA models. Moreover recurrent neural network models outperform the traditional ARIMA models. A 3-2-1 neural network architecture is best fit for forecasting futures returns.

The advent of Information Technology (IT) is rapidly changing the banking industry. Banks are viewed as information processors and provide an interesting focus of information problems in decision-making. Information is itself a product that customers and staff in any organization consume. Poor information quality has a significant impact on banking and the importance of information quality to current business practices has long drawn attention of practitioners and academicians. This paper aims to broaden the understanding about information quality as a critical factor through which information technology spreads its influences on the banking success. In the context of Indian banking, this study examines how banking technology has benefited through information quality. A research framework and associated hypotheses are proposed. An empirical survey was conducted and questionnaires were distributed to 300 bankers. A total of 270 valid observations was collected and analyzed using multiple regressions using OLS technique. The results suggest that information quality has positive effects on the banking success.