Search results

Kaleidofin was co-founded in 2017 by Puneet Gupta and Sucharita Mukherjee; former CFO and CEO of IFMR (Institute for Financial Management and Research) Holdings Pvt Ltd. As part of their roles at IFMR, Gupta and Mukherjee focused on designing products and developing technology to push for financial inclusion. In their field interactions, the co-founders had an epiphany of the challenges faced by people while trying to save towards important life goals. They saw an opportunity in the large segment of financially under-served people in India and quit their jobs to start Kaleidofin. Kaleidofin was conceptualised as a digital platform that offers customised financial solutions to help customers meet their life goals. The start-up partnered with mutual fund companies for solutions on one hand and network partners (NGOs, microfinance organizations, cooperative banks) on the other for access to their existing customers.

Kaleidofin grew from 50 customers in January 2018 to 15,000 customers by March 2019. Aiming to grow to 1 million customers in the next 30 months Kaleidofin faces a dilemma about its future course. The start-up could continue to grow by expanding its current target segment which is the low-income households and preserve its vision at the risk of increasing costs. The second option would be to look at other potential target segments, such as, middle-income households and risk diluting their vision. The case study highlights the unique customer-centric model of Kaleidofin and the need for start-ups to understand the value proposition of their products/services.

Lean manufacturing (LM) is prevalent in the manufacturing industry; thus, focusing on fast and accurate lean tool implementation is the new paradigm in manufacturing. Value stream mapping (VSM) is one of the many LM tools. It is understood that combining LM implementation with VSM tools can generate better outcomes. This paper aims to develop an expert system for optimal sequencing of VSM tools for lean implementation.

A proposed artificial neural network (ANN) model is based on the analytic network process (ANP) devised for this study. It will facilitate the selection of VSM tools in an optimal sequence.

Considering different types of wastes and their level of occurrence, organizations need a set of specific tools that will be effective in the elimination of these wastes. The developed ANP model computes a level of interrelation between wastes and VSM tools. The ANN is designed and trained by data obtained from numerous case studies, so it can predict the accurate sequence of VSM tools for any new case data set.

The design and use of the ANN model provide an integrated result of both empirical and practical cases, which is more accurate because all viable aspects are then considered. The proposed modeling approach is validated through implementation in an automobile manufacturing company. It has resulted in benefits, namely, reduction in bias, time required, effort required and complexity of the decision process. More importantly, according to all performance criteria and subcriteria, the main goal of this research was satisfied by increasing the accuracy of selecting the appropriate VSM tools and their optimal sequence for lean implementation.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.