Forecasting currency exchange rates is an important financial problem that has received much attention especially because of its intrinsic difficulty and practical applications. The statistical distribution of foreign exchange rates and their linear unpredictability are recurrent themes in the literature of international finance. Failure of various structural econometric models and models based on linear time series techniques to deliver superior forecasts to the simplest of all models, the simple random walk model, have prompted researchers to use various non-linear techniques. A number of non-linear time series models have been proposed in the recent past for obtaining accurate prediction results, in an attempt to ameliorate the performance of simple random walk models. In this paper, we use a hybrid artificial intelligence method, based on neural network and genetic algorithm for modelling daily foreign exchange rates. A detailed comparison of the proposed method with non-linear statistical models is also performed. The results indicate superior performance of the proposed method as compared to the traditional non-linear time series techniques and also fixed-geometry neural network models.

This paper uses Data Envelopment Analysis to measure labor use efficiency of individual branches of a large public sector bank with several thousand branches across India. We find considerable variation in the average levels of efficiency across the four metropolitan regions considered in this study. In this context, we introduce the concept of area or spatial efficiency for each region relative to the nation as a whole. Our findings suggest that the policies, procedures, and incentives handed down from the corporate level cannot fully neutralize the influence of the local work culture in the different regions. Most of the potential reduction in labor cost appears to be coming from possible downsizing the clerical and subordinate staff. Our analysis identifies branches that operate at very low levels of efficiency and may be gainfully merged with other branches wherever possible.

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Investigations of offshore outsourcing of information systems have presented little evidence on developing country software and information technology (IT) industries. This study probes how Indian software and IT suppliers trade off work in India versus bodyshopping of employees. Worldwide clients view these practices as full offshoring versus on-shore temporary hiring from an Indian firm, but these practices are probed from suppliers' perspective. Suppliers' characteristics are theorized to affect their use of bodyshopping versus in-India work. A Reserve Bank of India survey of every Indian software and IT firm elicited suppliers' use of bodyshopping to serve clients abroad. Consistent with theoretical rationales, suppliers that were larger, incorporated, public, and owned foreign subsidiaries most frequently provided bodyshopping among their international services. Bodyshopping was used frequently for IT purchasing and systems maintenance and infrequently for business process applications, and was infrequent to nations where bodyshopped labor costs were high. The evidence expands knowledge of the vibrant entrepreneurial IT industry in India and how it serves client firms abroad.