Many researchers have assumed one stage trade credit financing. In this study, we considered two levels of trade credit policy using Discounted Cash Flow (DCF) approach. Demand rate is considered to be stock-dependent for the first level (credit demand) and constant for second level (cash demand). Mathematical models are derived under two different circumstances i.e., case I: The permissible delay period is less than or equal to the cycle time and case II: The permissible delay period is greater than or equal to the cycle time for settling the account. An algorithm is provided to determine the optimal order quantity and annual profit. In addition, numerical examples are presented to demonstrate the solution process. Finally, sensitivity analysis of the optimal solution is discussed with respect to different parameters.

Credit Risk is inherent to any firm, organization, bank etc. The management of such credit risk becomes a cumbersome task for the managers. The main reason for growth of the credit risk management area is the ongoing and pressing need to maximize a bank's risk-adjusted rate of return by maintaining credit exposure in acceptable parameters. NPA's can have a huge negative impact on a bank's profitability and can lead to complete erosion of its asset base. The objective of our study is based on such parameter. We have identified the factors affecting the NPAs, the reasons for increasing NPAs, the tools and techniques used for managing such NPAs and making a comparison between the NPAs of the top 5 public sector banks and private sector banks.

There are many studies found in the field of stock volatility and institutional investors. Most of the studies found an inconsistent relationship between volatility and institutional investors. It creates a curiosity in the mind of investor, whether riskier securities attract institutional investors or an increase in institutional holdings results in an increase in volatility. In this paper we tried to examine the impact of institutional ownership pattern on stock volatility. We have considered BSE-30 companies and taken 5 year data from 1st

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