This study investigates the relationship between weather patterns and stock market returns across major Asian economies from 2000 to 2023. Drawing on monthly index data from nine prominent Asian stock exchanges, the research categorizes weather into hot, wet, and dry seasons to analyze their impact on market performance.
Prediction of the stock price is one of the major concerns in today’s need. Due to the pandemic, financial stability of the country was cryptic. It made investors in a predicament whether to invest or where to invest. Predicting the stock market helps to determine futuristic stock value of financial exchange. In this paper, authors have used Convolution Neural Network (CNN) and XGBoost, a class of Deep Neural Networks in predicting the stock prices. CNN and XGBoost identify predominant features among various other features without human intervention.
This study presents a thorough investigation of the variables affecting price changes in the Indian Stock Market. The study aims to pinpoint the key elements that significantly influence the closing price of the Indian stock market (Nifty 50) by utilizing three different datasets and two different machine learning algorithms. The results show a notable result: the efficiency of the algorithm did not significantly increase as a result of including economic variables in the analysis.
Retail industry is undergoing transformation. Many changes are happening in this industry due to change in consumer behaviour. Every retailer is trying to understand, what drives the customers to select the retailer? It is technology, sustainability or both. Retailers are trying to identify the right technology for their business, which will delight customers and at the same trying to find out how to achieve sustainability in their business.
The effective management of mutual fund portfolios is paramount for investors seeking to optimize returns while minimizing risk. However, traditional optimization techniques often struggle to accurately forecast portfolio performance, leading to suboptimal investment decisions. To address this challenge, this paper offers a novel solution to this problem by combining the Quantum Neural Network (QNN) for performance prediction with the Quantum-Inspired Evolutionary Algorithm (QEA) for portfolio optimisation.
After completion of the case study, students will be able to discuss the characteristics of sustainable enterprises driving the innovation; analyze the concept of waste to wealth, along with its associated benefits and challenges; provide an example of a sustainable start-up that operates conventionally and is attempting to increase production capacity through automation; and describe the strategies for scaling up the business.
The digital payment sector is becoming an increasingly important aspect of people’s life as a result of recent advancements in mobile and internet technology, delivering numerous fascinating and helpful services like M-banking. The m-banking system enables consumers to make purchases from anywhere using any electronic device, including mobile phones or tablets. M-banking is expected to have a better future as a result of current movements in international digital markets.
The objective of the study is to analyse the impact of e-service quality attributes on customer satisfaction and on purchase intention and purchase frequency of electronic gadgets and home appliances in Bangalore, India using the service quality (SERVQUAL) model. The e-service quality attributes chosen for the study are empathy, information, security, reliability, user interface, responsiveness, fulfillment, and personalization. This paper extends existing research by quantifying the impact of chosen attributes of e-service quality on customer satisfaction and purchase intentions, and on purchase frequency.
This case study explores Gandhadagudi Candles, a brand committed to sustainability by producing eco-friendly candles using natural, renewable, and biodegradable materials such as soy wax and beeswax. The case focuses on the brand's sustainable practices, including the use of natural scents, recyclable packaging, and energy-efficient production methods. Through the use of a circular economy model, Gandhadagudi Candles has reduced its carbon footprint, improved cost savings, and enhanced customer loyalty.
To study AI Implementation in Digital Payment, as an empirical analysis on Banking Sector in India. Questionnaires comprised of closed-ended questions were given out in order to accomplish these objectives. The elements of the proposed model were evaluated using the validity, correlation, and reliability analyses.
A unique B-School that carries the flag of the RV Educational Institutions, RV Institute of Management (RVIM) was founded in the year 1999. RVIM is spearheading the cause of education in various fields since 1940. It is an autonomous institution for excellence which is approved by the ALL India Council for Technical Education (AICTE) New Delhi, Affiliated to Bengaluru City University, and recognised by Govt. of Karnataka, located in a state-of-the-art campus in Jayanagar, a beautiful and centrally-located suburb of Bengaluru, India.
It offers a two-year full-time Autonomous Master of Business Administration (MBA) programme (affiliated to BCU), and also offers many value addition programmes in the specialised areas of Banking & Insurance, Entrepreneurship, Finance, Healthcare, Human Resources, Marketing, Business Analytics, Operations and Supply Chain Management. The institute encourages both faculty and students to involve in research and contribute to the research output.