The Impact of Interest Rate Futures on the Underlying Interest Rate Markets in India

If the market is perfect and complete, ideally, the introduction of derivatives should not in any way affect the equilibrium conditions in the underlying market.  However, the presence of information asymmetry in the market ensures that introduction of a derivative alters the speed with which equilibrium is attained.  This may generally affect the underlying asset’s price level and also its volatility. A study of a similar phenomenon is done in the case of the Indian bond market. The Indian Bond market which is predominantly G-sec saw the introduction of the interest rate futures recently.  The 10 year Interest rate futures contract based on a 10 year notional coupon bearing Government of India security, the 91 day T Bill futures which is based on 91 day T bills issued by the Government of India and the 2 and 5 year Interest rate Futures based on 2 and 5 year notional Gsec. The purpose of this paper is to understand their impact on the underlying market. The developments in the interest rate futures market can be attributed to the novelty of this market in India. In this paper we will try and understand whether there has been any change in the behavior of the markets for the underlying post the introduction of these derivatives. It is seen that both the short term interest rate and long term interest rate markets gets impacted on their turnover post the introduction of these derivatives. However, when it comes to volatility, it is only the short term interest rates which gets significantly impacted

The Role of Artificial Intelligence in Banking for Leveraging Customer Experience

Purpose: In light of digital advancements, banks need to create customer experiences that strengthen loyalty and trust. For establishing a strong digital banking base, it is crucial for banks to make their processes efficient and fast. The purpose of the paper is to analyze the efficacy of banking functions on implementing Artificial Intelligence for enhancing customer engagement and improving customer satisfaction. It targets banks in metropolitan cities of India having tech-savvy customers, leading a fast-paced life who desire personalization and expect faultless and seamless services. Methodology: The study focusses on front, middle and back-office banking processes. The data for middle and back-office processes is collected through 10 interviews of senior officials and head of IT team in major banks. Literature review and theoretical research is carried out for various international and Indian banks with respect to the integration of AI to improve customer interactions and internal banking processes. For understanding the front-office user experience with AI-Banking, data has been gathered through a survey regarding usage of Chatbots on online banking platforms. A quantitative analysis using the Relative Importance Index reveals major use-cases ranked by customers. Spearman correlation is applied to find the relationship between the two most popular use-cases. Findings: The research paper reveals banking features integrated with AI. Chatbot use-cases on banking platforms are ranked based on customer experience. It is proved that there is a positive correlation (0.247) between the two most popular use-cases. The paper proposes IT Architecture and best practices for the digital banking sector. Practical/Theoretical implications: Based on the complete picture of AI integration with banking operations, evolving Indian banks could focus on the most popular use-cases to attract customers. A comparison with the features developed for various banks may provide a way for growth in the digital banking sector. The correlation between Chatbot use-cases may benefit the established Indian banks to further expand business. Originality/value: Implementation of AI in banking is identified for Indian Banks. It is proved that if a person uses Chatbot for assistance in customer service, they are likely to use Chatbot for recommendation regarding offers and discounts

Optimization of Clustering Algorithm Using Metaheuristic

A vital issue in information grouping and present a few answers for it. We explore utilizing separation measures other than Euclidean sort for enhancing the execution of Clustering. We additionally build up another point symmetry-based separation measure and demonstrate its proficiency. We build up a novel successful k-Mean calculation which enhances the execution of the k-mean calculation. We build up a dynamic linkage grouping calculation utilizing kd-tree and we demonstrate its superior. The Automatic Clustering Differential Evolution (ACDE) is particular to Clustering basic information sets and finding the ideal number of groups consequently. We enhance ACDE for arranging more mind boggling information sets utilizing kd-tree. The proposed calculations don't have a most pessimistic scenario bound on running time that exists in numerous comparable calculations in the writing. Experimental results appeared in this proposition exhibit the viability of the proposed calculations. We contrast the proposed calculations and other ACO calculations. We display the proposed calculations and their execution results in point of interest alongside promising streets of future examination

Do Indian Firms Engage in Greenwashing? Evidence from Indian Firms

This study aims to look at ESG reporting in India through the lens of greenwashing, with a focus on the companies listed in the National Stock Exchange under the NIFTY 50 index using available ESG scores and assessments. Further, it aims to measure the indulgence of greenwashing by the companies. This study also analyses and attempts to highlight the factors that influence a company’s greenwashing behaviour, focusing specifically on the Indian context. Data for the empirical study and calculation of greenwashing score is collected from secondary data sources. We further use a regression method to study the nature of influence and significance of various factors on the calculated greenwashing score and assess the findings with our hypothesis. The study identifies 54% from the 48 companies, part of our sample size as green washers. Most of these companies belong to the manufacturing and energy sector of the Indian economy. The regression results suggest that a company’s cross listing status or its presence in any ESG focus fund has a significant and negative relationship with its observed greenwashing score. On firm level characteristics, the regression results indicate that a company’s board size and the presence of independent directors have a significant impact on the company’s observed greenwashing score

RP-HPLC METHOD DEVELOPMENT AND VALIDATION OF TREMADOL HYDROCHLORIDE IN BULK FORM BY ION-PAIR LIQUID CHROMATOGRAPHY

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Pattern-based Process Characterization and Gain Scheduling for Nonlinear Chemical Processes

Accurate characterization of process dynamics from on-line sensor data is the key issue in successful implementation of gain scheduling for controlling chemical processes. This work presents a development of pattern-based gain scheduling for process control. The approach employs process state maps constructed from windowed slices of multisensor plant trend data. Process identification is done using principles of similarity based pattern recognition. This technique provides a straightforward means to associate unique gain, integral time and/or derivative time controller settings with different states of the process. Simulation results show that better control performance may be achieved by use of gain scheduled controller as compared to the conventional fixed feedback systems

Under what kind of parametric fluctuations is spatiotemporal regularity the most robust?

It was observed that the spatiotemporal chaos in lattices of coupled chaotic maps was suppressed to a spatiotemporal fixed point when some fraction of the regular coupling connections were replaced by random links. Here we investigate the effects of different kinds of parametric fluctuations on the robustness of this spatiotemporal fixed point regime. In particular we study the spatiotemporal dynamics of the network with noisy interaction parameters, namely fluctuating fraction of random links and fluctuating coupling strengths. We consider three types of fluctuations: (i) noisy in time, but homogeneous in space; (ii) noisy in space, but fixed in time; (iii) noisy in both space and time. We find that the effect of different kinds of parameteric noise on the dynamics is quite distinct: quenched spatial fluctuations are the most detrimental to spatiotemporal regularity; spatiotemporal fluctuations yield phenomena similar to that observed when parameters are held constant at the mean-value; and interestingly, spatiotemporal regularity is most robust under spatially uniform temporal fluctuations, which in fact yields a larger fixed point range than that obtained under constant mean-value parameters

Anxiolytic activity of Angiotensin-Receptor-Blocker in Experimental Models of Anxiety in Mice

The present study aimed to explore the role of Angiotensin- Receptor-Blocker in the management of anxiety. Male Swiss albino mice of age 6-8 weeks and weight 25-30 g were used in the present study. Candesartan (Angiotensin receptor blocker) was administered in two doses (1 and 2 mg/kg; i.p.) to mice for 14 successive days regularly. Anxiety was induced in mice by two different methods: (i) exposing the mice to immobilization stress for a period of 6 h daily for 7 consecutive days; (ii) administration of caffeine (25 mg/kg; i.p.) daily for 7 days. Elevated Zero Maze and Open Field Apparatus were used to evaluate the level of anxiety in different groups. After behavioral evaluation, the animals were sacrificed and their brains were used for estimation TBARS, GSH and Nitrite levels in the brain. Administration of Candesartan (1 and 2 mg/kg; i.p) for 14 successive days significantly (p<0.05) reduced anxiety due to immobilization stress and caffeine induced anxiety. Candesartan (1and 2 mg/kg; i.p) treated mice showed an increase (p<0.05) in GSH levels while a decrease (p<0.05) in TBARS and nitrite levels in brain. Thus, candesartan may prove to be a useful remedy for the management of anxiety owing to its neuroprotective and antioxidant activity