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

AN ASSESSMENT OF THE EFFICACY OF SELECTIVE LASER TRABECULOPLASTY (SLT) IN OPEN-ANGLE GLAUCOMA PATIENTS: A CLINICAL STUDY.

Introduction: Objectives: The present study aims to assess the efficacy of selective laser trabeculoplasty (SLT) in reducing intraocular pressure (IOP) when used as the first-line or supplementary therapy for primary open-angle glaucoma (POAG) patients.  Methods:  In this study conducted over one year, 1820 patients visited the Regional Institute of Ophthalmology (RIO), IGIMS, Patna, Bihar, India for SLT, averaging about 35 patients per week. The sample was bifurcated into two groups: Group A (944 patients, 1888 eyes) received SLT in addition to their existing anti-glaucomatous medications (AGM), while Group B (876 patients, 1752 eyes) comprised newly diagnosed POAG patients, for whom SLT served as the primary treatment.  Results:  In this study, the overall cohort displayed an average baseline intraocular pressure of 21.3 ± 4.8 mm of Hg, which decreased to 15.5 ± 2.6 mm Hg post-SLT. The pressure reduction was 33.5 % in Group A and 41.5 % in Group B. In some cases, when treatment with SLT alone could not give the desired intraocular pressure, both groups turned to initiating or increasing AGM. Notably, 51.3% belonging to the first group and 64.3% belonging to the second group achieved target intraocular pressure with SLT alone, and after SLT, 71.5% of first group patients and 65.3% of second group patients no longer required glaucoma medication at the last visit.  Conclusion:  SLT proves effective as both a primary and additional therapy for open-angle glaucoma, significantly reducing medicine usage. The IOP reduction remains consistent in both primary and adjunctive treatment groups during a year of follow-up.  Recommendation:  Based on the results, it is recommended to integrate SLT into glaucoma treatment protocols, exploring its potential to reduce medication reliance, and promoting extended research for a more comprehensive assessment of SLT's long-term benefits

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

Evolution beats random chance: Performance-dependent network evolution for enhanced computational capacity

The quest to understand structure-function relationships in networks across scientific disciplines has intensified. However, the optimal network architecture remains elusive, particularly for complex information processing. Therefore, we investigate how optimal and specific network structures form to efficiently solve distinct tasks using a novel framework of performance-dependent network evolution, leveraging reservoir computing principles. Our study demonstrates that task-specific minimal network structures obtained through this framework consistently outperform networks generated by alternative growth strategies and Erd\H{o}s-R\'enyi random networks. Evolved networks exhibit unexpected sparsity and adhere to scaling laws in node-density space while showcasing a distinctive asymmetry in input and information readout nodes distribution. Consequently, we propose a heuristic for quantifying task complexity from performance-dependently evolved networks, offering valuable insights into the evolutionary dynamics of network structure-function relationships. Our findings not only advance the fundamental understanding of process-specific network evolution but also shed light on the design and optimization of complex information processing mechanisms, notably in machine learning.Comment: 22 pages, 6 figure

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